From 9e7a5239309e5b2fce6464a9fc922f559526ce91 Mon Sep 17 00:00:00 2001 From: Daniel Nachun Date: Wed, 21 Oct 2026 23:46:23 -0400 Subject: [PATCH 1/2] add support for LD matrix and use better genotype readers --- .github/recipe/recipe.yaml | 16 +- DESCRIPTION | 16 +- NAMESPACE | 63 +- R/AllClasses.R | 117 +- R/AllGenerics.R | 389 +- R/AnnotationMatrix.R | 2 +- R/ColocBoostResult.R | 16 +- R/ColocResult.R | 41 +- R/CtwasResult.R | 2 +- R/GenotypeDosageSeed.R | 383 ++ R/GenotypeHandle.R | 281 +- R/GenotypeMixture.R | 515 +++ R/GwasFineMappingResult.R | 16 +- R/H2Estimate.R | 11 +- R/JointGroup.R | 49 +- R/LdData.R | 723 ---- R/LdEigen.R | 147 +- R/LdMatrix.R | 652 ++++ R/LdScore.R | 256 +- R/LdStatistic.R | 425 ++- R/MethodOptions.R | 14 +- R/MethodParam.R | 27 +- R/MultiStudyQtlDataset.R | 21 +- R/QtlDataset.R | 165 +- R/QtlFineMappingResult.R | 38 +- R/RangedTupleList.R | 45 +- R/causalInferencePipeline.R | 196 +- R/colocPipeline.R | 177 +- R/colocboostPipeline.R | 441 ++- R/cpp11.R | 56 +- R/crossValidation.R | 89 +- R/ctwasPipeline.R | 631 ++-- R/ctwasWrapper.R | 31 + R/enloc.R | 8 +- R/exampleData.R | 43 +- R/fineMappingPipeline.R | 1302 +++---- R/fineMappingRow.R | 92 +- R/fineMappingWrappers.R | 417 ++- R/genotypeIo.R | 1834 +++++++--- R/gwasSumStats.R | 59 +- R/h2Annotations.R | 5 +- R/h2EstimationWrappers.R | 952 ++--- R/jointEngine.R | 470 ++- R/jointSpecification.R | 1599 +++------ R/ld.R | 3062 +++++++--------- R/manifestLoaders.R | 514 +-- R/mashPipeline.R | 94 +- R/mashWrapper.R | 36 +- R/pvalCombineWrappers.R | 90 +- R/qtlAssociationPostprocess.R | 16 +- R/qtlEnrichmentPipeline.R | 91 +- R/qtlSumStats.R | 49 +- R/regularizedRegressionWrappers.R | 1291 ++++--- R/relatednessQc.R | 4 +- R/resultReaders.R | 27 +- R/resultWriters.R | 223 +- R/sumstatsQc.R | 1980 ++++++----- R/tupleSelectors.R | 283 +- R/twasWeights.R | 171 +- R/twasWeightsPipeline.R | 1349 +++---- R/variantId.R | 19 +- README.md | 5 +- _pkgdown.yml | 32 +- data/colocboostResultExample.rda | Bin 2964 -> 2968 bytes data/ctwasEstExample.rda | Bin 249840 -> 249636 bytes data/ctwasFinemapExample.rda | Bin 250348 -> 250524 bytes data/ctwasInputsExample.rda | Bin 249768 -> 248948 bytes data/ctwasWeightsExample.rda | Bin 5924 -> 6528 bytes data/fsusieFineMappingExample.rda | Bin 22176 -> 22180 bytes data/gwasFineMappingExample.rda | Bin 650796 -> 651664 bytes data/gwasFineMappingLbfExample.rda | Bin 41348 -> 41356 bytes data/gwasSumStatsS4Example.rda | Bin 7448 -> 7344 bytes data/ldEigenExample.rda | Bin 963288 -> 973656 bytes data/ldScoreExample.rda | Bin 552068 -> 583572 bytes data/multiStudyQtlDatasetExample.rda | Bin 7456 -> 7172 bytes data/mvsusieFineMappingExample.rda | Bin 118008 -> 117592 bytes data/qtlDatasetExample.rda | Bin 5888 -> 5640 bytes data/qtlFineMappingExample.rda | Bin 212452 -> 211696 bytes data/qtlFineMappingLbfExample.rda | Bin 79192 -> 79176 bytes data/qtlFineMappingPairedExample.rda | Bin 19332 -> 19336 bytes data/qtlSumStatsExample.rda | Bin 8404 -> 8304 bytes data/qtlSumStatsMulticontextExample.rda | Bin 16488 -> 16384 bytes data/twasWeightsExample.rda | Bin 10104 -> 10132 bytes .../chr22/protocol_example.LD.chr22.afreq | 352 +- inst/scripts/build_bgen_fixtures.R | 45 + inst/scripts/build_ctwas_examples.R | 55 +- inst/scripts/build_examples.R | 76 +- inst/scripts/build_h2_examples.R | 117 +- inst/scripts/build_h2_parity_z.R | 80 + inst/scripts/build_sldsc_upstream_z.R | 69 - inst/scripts/prepare_example_data.R | 31 +- .../prepare_paired_finemapping_example.R | 2 +- man/ColocBoostResult.Rd | 4 +- man/ColocResult-class.Rd | 2 +- man/ColocResult.Rd | 4 +- man/ColocResultBase-class.Rd | 9 +- man/CredibleSetParam.Rd | 28 +- man/CrossValidationParam.Rd | 21 +- man/FineMappingResultBase-class.Rd | 11 +- man/GenotypeDosageSeed-class.Rd | 51 + man/GenotypeHandle-class.Rd | 65 +- man/GenotypeMixture-class.Rd | 47 + man/GenotypeMixture-constructor.Rd | 38 + man/GwasFineMappingParam.Rd | 4 +- man/GwasFineMappingResult.Rd | 6 +- man/GwasSumStats-class.Rd | 2 +- man/GwasSumStats.Rd | 20 +- man/LdData-class.Rd | 77 - man/LdData.Rd | 59 - man/LdEigen-class.Rd | 11 +- man/LdMatrix-class.Rd | 54 + man/LdMatrix-constructor.Rd | 65 + man/LdScore-class.Rd | 13 +- man/LdScore.Rd | 3 +- man/LdStatistic-class.Rd | 12 + man/PanelFilterParam.Rd | 5 +- man/PoolrOptions.Rd | 7 +- man/QtlDataset-class.Rd | 2 +- man/QtlFineMappingResult.Rd | 14 +- man/QtlSumStats-class.Rd | 2 +- man/QtlSumStats.Rd | 13 +- man/RaissParam.Rd | 25 +- man/SumStatsBase-class.Rd | 13 +- man/TwasWeights-class.Rd | 9 +- man/TwasWeights.Rd | 4 +- man/af.Rd | 23 +- man/assembleCtwasInputs.Rd | 7 +- man/blockMetadata.Rd | 31 - man/bplapplyBlocks.Rd | 24 - man/buildLdEigen.Rd | 57 +- man/buildLdScore.Rd | 77 +- man/causalInferencePipeline.Rd | 37 +- man/checkGenomeBuild.Rd | 6 +- man/colocPipeline.Rd | 32 +- man/colocViews.Rd | 2 +- man/colocboostPipeline.Rd | 22 +- man/combineFineMappingResults.Rd | 6 +- man/combineGwasSumStats.Rd | 17 +- man/combinePValues.Rd | 7 +- man/combineQtlSumStats.Rd | 12 +- man/combineTwasWeights.Rd | 6 +- man/computeCsCorrelation.Rd | 4 +- man/computeLd.Rd | 7 +- man/computeLdScores.Rd | 4 +- man/contexts.Rd | 10 +- man/credibleSetSummary.Rd | 2 +- man/credibleSets.Rd | 2 +- man/ctwasInputsExample.Rd | 5 +- man/ctwasPipeline.Rd | 54 +- man/cvResult.Rd | 6 +- man/dentist.Rd | 16 +- man/dentistSingleWindow.Rd | 12 +- man/dot-annotDetectFormat.Rd | 5 +- man/dot-penalizedRssWeights.Rd | 12 +- man/dot-plinkStem.Rd | 18 - man/dprWeights.Rd | 3 +- man/eigenList.Rd | 12 +- man/estCtwasGroupPriors.Rd | 6 +- man/estimateH2.Rd | 2 +- man/exportToBed.Rd | 12 +- man/exportToVcf.Rd | 9 +- man/extractCsInfo.Rd | 4 +- man/fineMappingPipeline.Rd | 128 +- man/fineMappingResult.Rd | 9 +- man/finemapCtwasRegions.Rd | 5 +- man/formatFinemappingOutput.Rd | 2 +- man/fsusieAffectedRegions.Rd | 2 +- man/fsusieCredibleBand.Rd | 2 +- man/fsusieTraitEffects.Rd | 2 +- man/genomicRegion.Rd | 2 +- man/genotypeFormat.Rd | 2 +- man/genotypeHandle.Rd | 19 +- man/genotypes.Rd | 12 +- man/gwasFineMappingExample.Rd | 4 +- man/gwasSumStatsS4Example.Rd | 2 +- man/hasGenotypes.Rd | 31 - man/jackknifeSe.Rd | 20 - man/l0learnRssWeights.Rd | 44 +- man/lassosumRss.Rd | 19 +- man/lassosumRssWeights.Rd | 43 +- man/lbf.Rd | 2 +- man/ldEigenExample.Rd | 20 +- man/ldMatrix.Rd | 23 +- man/ldMatrixList.Rd | 10 +- man/ldMismatchQc.Rd | 6 +- man/ldPanelNSamples.Rd | 19 +- man/ldPruneByCorrelation.Rd | 17 +- man/ldReference.Rd | 49 + man/ldScoreExample.Rd | 8 +- man/ldSketch.Rd | 41 - man/learnTwasWeights.Rd | 7 +- man/loadGenotypeRegion.Rd | 12 +- man/loadGwasSumStatsFromManifest.Rd | 24 +- man/loadLdMatrix.Rd | 98 - man/loadMultiStudyQtlDatasetFromManifest.Rd | 4 +- man/loadQtlSumStatsFromManifest.Rd | 10 +- man/marginalEffects.Rd | 2 +- man/mashInput.Rd | 10 +- man/mcpRssWeights.Rd | 44 +- man/mergeSusieCs.Rd | 2 +- man/methodNames.Rd | 4 +- man/mixtureWeights.Rd | 30 +- man/mrashRssWeights.Rd | 58 +- man/mrashWeights.Rd | 20 +- man/mrmashRssWeights.Rd | 20 +- man/mvsusieRssWeights.Rd | 23 +- man/nSamples.Rd | 15 +- man/pecotmr-genotype-io.Rd | 9 +- man/pecotmr-h2-gldsc.Rd | 11 +- man/pecotmr-h2-sldsc.Rd | 23 +- man/penalizedRss.Rd | 20 +- man/pip.Rd | 8 +- man/postprocessFinemappingFits.Rd | 2 +- man/prsCs.Rd | 47 +- man/prsCsWeights.Rd | 51 +- man/qtlEnrichment.Rd | 2 +- man/qtlEnrichmentPipeline.Rd | 27 +- man/qtlSumStatsExample.Rd | 2 +- man/qtlSumStatsFromBetaMatrix.Rd | 6 +- man/qtlSumStatsFromZMatrix.Rd | 6 +- man/raiss.Rd | 45 +- man/readGenotypes.Rd | 44 +- man/refPanel.Rd | 31 - man/refVariantInfo.Rd | 9 +- man/residualizedGenotypes.Rd | 4 +- man/residualizedPhenotypes.Rd | 4 +- man/resolveWeights.Rd | 11 +- man/scadRssWeights.Rd | 44 +- man/sdpr.Rd | 52 +- man/sdprWeights.Rd | 47 +- man/show-methods.Rd | 15 +- man/shrinkLd.Rd | 25 - man/slalom.Rd | 15 +- man/snpIdx.Rd | 31 - man/sumStats.Rd | 10 +- man/sumStatsDataFrame.Rd | 26 +- man/summaryStatsQc.Rd | 45 +- man/susieAshRssWeights.Rd | 11 +- man/susieFit.Rd | 3 +- man/susieInfRssWeights.Rd | 11 +- man/susieResult.Rd | 13 +- man/susieRssWeights.Rd | 11 +- man/topLoci.Rd | 2 +- man/traitNames.Rd | 8 +- man/traitPosition.Rd | 14 +- man/twasWeightsPipeline.Rd | 169 +- man/twasZ.Rd | 11 +- man/varY.Rd | 10 +- man/variantIds.Rd | 16 +- man/variantInfo.Rd | 30 - pixi.toml | 8 +- src/bgen.cpp | 220 ++ src/cpp11.cpp | 114 +- src/dentist_iterative_impute.cpp | 182 +- src/lassosum_rss.cpp | 110 +- src/ld_prune.cpp | 75 + src/penalized_rss.cpp | 169 +- src/prscs_mcmc.cpp | 103 +- src/prscs_mcmc.h | 111 +- src/qtl_enrichment.cpp | 2 +- src/rss_ld.h | 89 + src/sdpr.cpp | 135 +- src/sdpr_mcmc.cpp | 445 ++- src/sdpr_mcmc.h | 251 +- src/vcf_gt.cpp | 69 + tests/testthat/helper-bgen.R | 288 ++ tests/testthat/helper-cfgForwarding.R | 54 + tests/testthat/helper-h2Classes.R | 139 +- tests/testthat/helper-rrwFixtures.R | 27 +- tests/testthat/helper-s4Constructors.R | 4 +- tests/testthat/helper-showMethods.R | 4 +- tests/testthat/helper-vcf.R | 125 + tests/testthat/test_AllClasses.R | 39 +- tests/testthat/test_ColocResult.R | 3 +- tests/testthat/test_GenotypeDosageSeed.R | 303 ++ tests/testthat/test_GenotypeMixture.R | 301 ++ tests/testthat/test_GwasFineMappingResult.R | 20 +- tests/testthat/test_GwasSumStats.R | 48 +- tests/testthat/test_H2Estimate.R | 38 + tests/testthat/test_JointGroup.R | 42 +- tests/testthat/test_LdEigen.R | 66 +- tests/testthat/test_LdMatrix.R | 232 ++ tests/testthat/test_LdScore.R | 154 +- tests/testthat/test_LdStatistic.R | 340 +- tests/testthat/test_MethodParam.R | 203 ++ tests/testthat/test_MultiStudyQtlDataset.R | 10 +- tests/testthat/test_QtlDataset.R | 127 +- tests/testthat/test_QtlFineMappingResult.R | 18 +- tests/testthat/test_RangedTupleList.R | 32 +- tests/testthat/test_causalInferencePipeline.R | 213 +- tests/testthat/test_colocPipeline.R | 50 +- tests/testthat/test_colocboostPipeline.R | 148 +- tests/testthat/test_cpp11.R | 25 +- tests/testthat/test_crossValidation.R | 41 + tests/testthat/test_ctwasPipeline.R | 308 +- tests/testthat/test_ctwasWrapper.R | 54 +- tests/testthat/test_data/bgen/LICENSE_1_0.txt | 24 + tests/testthat/test_data/bgen/complex.bgen | Bin 0 -> 835 bytes .../testthat/test_data/bgen/complex.bgen.bgi | Bin 0 -> 3072 bytes tests/testthat/test_data/bgen/complex.sample | 6 + tests/testthat/test_data/bgen/complex.vcf | 15 + tests/testthat/test_data/bgen/haplotypes.bgen | Bin 0 -> 300 bytes .../test_data/bgen/haplotypes.bgen.bgi | Bin 0 -> 3072 bytes tests/testthat/test_data/bgen/haplotypes.haps | 4 + tests/testthat/test_data/h2ParityLd.rds | Bin 0 -> 435588 bytes .../{sldscUpstreamZ.rds => h2ParityZ.rds} | Bin tests/testthat/test_exampleData.R | 2 +- tests/testthat/test_fineMappingPipeline.R | 479 ++- tests/testthat/test_fineMappingWrappers.R | 306 +- tests/testthat/test_genotypeHandle.R | 390 +- tests/testthat/test_genotypeIo.R | 1374 ++++--- tests/testthat/test_h2EstimationWrappers.R | 586 +-- tests/testthat/test_jointEngine.R | 393 +- tests/testthat/test_jointSpecification.R | 621 +--- tests/testthat/test_ld.R | 3154 ++++------------- tests/testthat/test_ldData.R | 1048 ------ tests/testthat/test_manifestLoaders.R | 244 +- tests/testthat/test_mashPipeline.R | 78 +- tests/testthat/test_pvalCombineWrappers.R | 59 +- tests/testthat/test_qtlEnrichmentPipeline.R | 54 +- tests/testthat/test_qtlSumStats.R | 62 +- .../test_regularizedRegressionWrappers.R | 448 ++- tests/testthat/test_resultWriters.R | 216 +- tests/testthat/test_rrDispatch.R | 124 +- tests/testthat/test_rrLassosum.R | 16 +- tests/testthat/test_rrMrAshRss.R | 14 +- tests/testthat/test_rrMrash.R | 18 +- tests/testthat/test_rrPenalizedRss.R | 32 +- tests/testthat/test_rrPrsCs.R | 55 +- tests/testthat/test_rrSdpr.R | 4 +- tests/testthat/test_rrSignalRecovery.R | 13 +- tests/testthat/test_rrSusie.R | 19 - tests/testthat/test_sumstatsQc.R | 1880 ++++------ tests/testthat/test_tupleSelectors.R | 65 +- tests/testthat/test_twasWeights.R | 101 +- tests/testthat/test_twasWeightsPipeline.R | 707 +++- tests/testthat/test_variantId.R | 6 + vignettes/coloc-pipeline.Rmd | 13 +- vignettes/colocboost-pipeline.Rmd | 4 +- vignettes/constructing-qtl-datasets.Rmd | 5 +- vignettes/constructing-sumstats.Rmd | 38 +- vignettes/ctwas-pipeline.Rmd | 23 +- vignettes/fine-mapping.Rmd | 29 +- vignettes/heritability.Rmd | 78 +- vignettes/ld-infrastructure.Rmd | 173 +- vignettes/rss-qc.Rmd | 16 +- vignettes/twas-weights.Rmd | 101 +- vignettes/twas-zscore.Rmd | 18 +- vignettes/xqtl-enrichment.Rmd | 2 +- 349 files changed, 24720 insertions(+), 20109 deletions(-) create mode 100644 R/GenotypeDosageSeed.R create mode 100644 R/GenotypeMixture.R delete mode 100644 R/LdData.R create mode 100644 R/LdMatrix.R create mode 100644 inst/scripts/build_bgen_fixtures.R create mode 100644 inst/scripts/build_h2_parity_z.R delete mode 100644 inst/scripts/build_sldsc_upstream_z.R create mode 100644 man/GenotypeDosageSeed-class.Rd create mode 100644 man/GenotypeMixture-class.Rd create mode 100644 man/GenotypeMixture-constructor.Rd delete mode 100644 man/LdData-class.Rd delete mode 100644 man/LdData.Rd create mode 100644 man/LdMatrix-class.Rd create mode 100644 man/LdMatrix-constructor.Rd delete mode 100644 man/blockMetadata.Rd delete mode 100644 man/bplapplyBlocks.Rd delete mode 100644 man/dot-plinkStem.Rd delete mode 100644 man/hasGenotypes.Rd delete mode 100644 man/jackknifeSe.Rd create mode 100644 man/ldReference.Rd delete mode 100644 man/ldSketch.Rd delete mode 100644 man/loadLdMatrix.Rd delete mode 100644 man/refPanel.Rd delete mode 100644 man/shrinkLd.Rd delete mode 100644 man/snpIdx.Rd delete mode 100644 man/variantInfo.Rd create mode 100644 src/bgen.cpp create mode 100644 src/ld_prune.cpp create mode 100644 src/rss_ld.h create mode 100644 src/vcf_gt.cpp create mode 100644 tests/testthat/helper-bgen.R create mode 100644 tests/testthat/helper-vcf.R create mode 100644 tests/testthat/test_GenotypeDosageSeed.R create mode 100644 tests/testthat/test_GenotypeMixture.R create mode 100644 tests/testthat/test_LdMatrix.R create mode 100644 tests/testthat/test_data/bgen/LICENSE_1_0.txt create mode 100644 tests/testthat/test_data/bgen/complex.bgen create mode 100644 tests/testthat/test_data/bgen/complex.bgen.bgi create mode 100644 tests/testthat/test_data/bgen/complex.sample create mode 100644 tests/testthat/test_data/bgen/complex.vcf create mode 100644 tests/testthat/test_data/bgen/haplotypes.bgen create mode 100644 tests/testthat/test_data/bgen/haplotypes.bgen.bgi create mode 100644 tests/testthat/test_data/bgen/haplotypes.haps create mode 100644 tests/testthat/test_data/h2ParityLd.rds rename tests/testthat/test_data/{sldscUpstreamZ.rds => h2ParityZ.rds} (100%) delete mode 100644 tests/testthat/test_ldData.R diff --git a/.github/recipe/recipe.yaml b/.github/recipe/recipe.yaml index 99b9fa5f6..bc8891b77 100644 --- a/.github/recipe/recipe.yaml +++ b/.github/recipe/recipe.yaml @@ -24,17 +24,21 @@ requirements: - ${{ compiler('cxx') }} host: - bioconductor-biocparallel + - bioconductor-biocsingular + - bioconductor-gdsarray - bioconductor-genomicranges - bioconductor-iranges - bioconductor-qvalue - bioconductor-s4vectors - bioconductor-snprelate - bioconductor-snpstats - - bioconductor-mungesumstats - bioconductor-multiassayexperiment + - bioconductor-plinkmatrix - bioconductor-rsamtools - bioconductor-rtracklayer + - bioconductor-seqarray - bioconductor-summarizedexperiment + - bioconductor-variantannotation - r-archive - r-arrow - r-aspu @@ -49,6 +53,7 @@ requirements: - r-cpp11 - r-cpp11armadillo - r-ctwas + - r-dbi - r-dplyr - r-flashier - r-fsusier @@ -79,6 +84,7 @@ requirements: - r-rfast - r-rlang - r-robustbase + - r-rsqlite - r-softimpute - r-stringr - r-susier @@ -92,6 +98,8 @@ requirements: - r-yaml run: - bioconductor-biocparallel + - bioconductor-biocsingular + - bioconductor-gdsarray - bioconductor-genomicranges - bioconductor-iranges - bioconductor-qvalue @@ -99,10 +107,12 @@ requirements: - bioconductor-snprelate - bioconductor-snpstats - bioconductor-multiassayexperiment - - bioconductor-mungesumstats + - bioconductor-plinkmatrix - bioconductor-rsamtools - bioconductor-rtracklayer + - bioconductor-seqarray - bioconductor-summarizedexperiment + - bioconductor-variantannotation - r-archive - r-arrow - r-aspu @@ -117,6 +127,7 @@ requirements: - r-cpp11 - r-cpp11armadillo - r-ctwas + - r-dbi - r-dplyr - r-flashier - r-fsusier @@ -147,6 +158,7 @@ requirements: - r-rfast - r-rlang - r-robustbase + - r-rsqlite - r-softimpute - r-stringr - r-susier diff --git a/DESCRIPTION b/DESCRIPTION index c5494b181..fae439ffa 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -72,22 +72,26 @@ Suggests: coda, CorShrink, ctwas, + DBI, ebnm, flashier, fsusieR, GBJ, + GDSArray, gdsfmt, glmnet, harmonicmeanp, igraph, knitr, L0Learn, + logging, mashr, mr.mashr, mvsusieR, ncvreg, nnls, pgenlibr, + PlinkMatrix, plinkQC, plyranges, poolr, @@ -97,7 +101,9 @@ Suggests: Rfast, rmarkdown, robustbase, + RSQLite, rtracklayer, + SeqArray, SNPRelate, snpStats, testthat, @@ -119,6 +125,8 @@ VignetteBuilder: knitr Config/roxygen2/version: 8.1.0 Collate: 'AllGenerics.R' + 'GenotypeMixture.R' + 'LdMatrix.R' 'RangedTupleList.R' 'GenotypeHandle.R' 'AllClasses.R' @@ -128,24 +136,24 @@ Collate: 'tupleSelectors.R' 'CtwasResult.R' 'CtwasResultEntry.R' + 'GenotypeDosageSeed.R' 'GwasFineMappingResult.R' + 'MethodOptions.R' + 'MethodParam.R' 'H2Estimate.R' 'JointGroup.R' 'LdBlocks.R' - 'LdData.R' 'LdStatistic.R' 'LdEigen.R' 'LdScore.R' 'MashPrior.R' - 'MethodOptions.R' - 'MethodParam.R' + 'qtlSumStats.R' 'QtlDataset.R' 'MultiStudyQtlDataset.R' 'QtlFineMappingResult.R' 'SldscData.R' 'causalInferencePipeline.R' 'gwasSumStats.R' - 'qtlSumStats.R' 'fineMappingPipeline.R' 'ld.R' 'colocPipeline.R' diff --git a/NAMESPACE b/NAMESPACE index 57b01701e..2ac1a9307 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -67,6 +67,7 @@ export(FsusieOptions) export(GbjOmniOptions) export(GbjOptions) export(GenotypeFilterParam) +export(GenotypeMixture) export(GlmnetOptions) export(GwasFineMappingParam) export(GwasFineMappingResult) @@ -74,8 +75,8 @@ export(GwasSumStats) export(KrigingOptions) export(L0learnOptions) export(LassosumOptions) -export(LdData) export(LdEigen) +export(LdMatrix) export(LdPruningOptions) export(LdScore) export(MashComponentParam) @@ -155,7 +156,6 @@ export(bayesLWeights) export(bayesNWeights) export(bayesRWeights) export(blockId) -export(blockMetadata) export(buildLdEigen) export(buildLdScore) export(buildMrmashPriorMatrices) @@ -245,7 +245,6 @@ export(genotypeCovariates) export(genotypes) export(glmnetWeights) export(h2EstimateToSldscTrait) -export(hasGenotypes) export(heritabilityEstimate) export(heritabilityEstimateSe) export(heritabilityIntercept) @@ -263,7 +262,6 @@ export(krigingOutlierQc) export(l0learnRssWeights) export(l0learnWeights) export(lassoWeights) -export(lassosumRss) export(lassosumRssWeights) export(lbf) export(lbfToAlpha) @@ -274,11 +272,10 @@ export(ldMatrixList) export(ldMismatchQc) export(ldPanelNSamples) export(ldPruneByCorrelation) -export(ldSketch) +export(ldReference) export(learnTwasWeights) export(loadGenotypeRegion) export(loadGwasSumStatsFromManifest) -export(loadLdMatrix) export(loadMultiStudyQtlDatasetFromManifest) export(loadQtlDatasetFromManifest) export(loadQtlSumStatsFromManifest) @@ -329,12 +326,10 @@ export(overlapTopLoci) export(panelFilterParam) export(parseRegion) export(parseVariantId) -export(penalizedRss) export(perCsColumns) export(phenotypeCovariates) export(pip) export(postprocessFinemappingFits) -export(prsCs) export(prsCsWeights) export(pval) export(qcDiagnostics) @@ -354,7 +349,6 @@ export(readSldscAnnot) export(readSldscFrq) export(readSldscTrait) export(readTwasWeights) -export(refPanel) export(refVariantInfo) export(regionToDf) export(regionVcp) @@ -369,7 +363,6 @@ export(scaleResiduals) export(scoreFromCs) export(scoreStats) export(screenCtwasRegions) -export(sdpr) export(sdprWeights) export(se) export(secondaryCoverage) @@ -381,7 +374,6 @@ export(sldscPostprocessingPipeline) export(sldscResults) export(sldscSubsetMeta) export(sliceMashData) -export(snpIdx) export(standardizeSldscTrait) export(stratifiedHeritabilityEnrichment) export(studyName) @@ -412,7 +404,6 @@ export(twasZ) export(updateMashModelCov) export(varY) export(variantIds) -export(variantInfo) export(waldTestPval) export(weightStandardized) export(weightsDataType) @@ -436,12 +427,13 @@ exportClasses(FineMappingMethodsParam) exportClasses(FineMappingResultBase) exportClasses(FineMappingRow) exportClasses(GenotypeFilterParam) +exportClasses(GenotypeMixture) exportClasses(GwasFineMappingParam) exportClasses(GwasFineMappingResult) exportClasses(GwasSumStats) exportClasses(H2Estimate) -exportClasses(LdData) exportClasses(LdEigen) +exportClasses(LdMatrix) exportClasses(LdScore) exportClasses(LdStatistic) exportClasses(MashComponentParam) @@ -493,7 +485,6 @@ exportMethods(as.list) exportMethods(baselineAnnotations) exportMethods(bindROWS) exportMethods(blockId) -exportMethods(blockMetadata) exportMethods(coef) exportMethods(colnames) exportMethods(colocBoostOutcomes) @@ -526,7 +517,6 @@ exportMethods(fullFit) exportMethods(genomicRegion) exportMethods(genotypeCovariates) exportMethods(genotypes) -exportMethods(hasGenotypes) exportMethods(heritabilityEstimate) exportMethods(heritabilityEstimateSe) exportMethods(heritabilityIntercept) @@ -541,7 +531,7 @@ exportMethods(ldBlocks) exportMethods(ldMatrix) exportMethods(ldMatrixList) exportMethods(ldPanelNSamples) -exportMethods(ldSketch) +exportMethods(ldReference) exportMethods(length) exportMethods(localH2Blocks) exportMethods(longForm) @@ -568,7 +558,6 @@ exportMethods(qtlAssociationPostprocess) exportMethods(qtlDatasets) exportMethods(readAnnotations) exportMethods(readGenotypes) -exportMethods(refPanel) exportMethods(regionVcp) exportMethods(residualizedGenotypes) exportMethods(residualizedPhenotypes) @@ -580,7 +569,6 @@ exportMethods(se) exportMethods(show) exportMethods(significantQtls) exportMethods(sldscResults) -exportMethods(snpIdx) exportMethods(stratifiedHeritabilityEnrichment) exportMethods(studyName) exportMethods(subsetChr) @@ -596,7 +584,6 @@ exportMethods(twasWeights) exportMethods(twasWeightsPipeline) exportMethods(varY) exportMethods(variantIds) -exportMethods(variantInfo) exportMethods(weightStandardized) exportMethods(weights) exportMethods(weightsDataType) @@ -606,6 +593,7 @@ importClassesFrom(GenomicRanges,GRanges) importClassesFrom(MultiAssayExperiment,MultiAssayExperiment) importClassesFrom(S4Vectors,DataFrame) importClassesFrom(S4Vectors,SimpleList) +importClassesFrom(S4Vectors,Vector) importClassesFrom(S4Vectors,character_OR_NULL) importClassesFrom(SummarizedExperiment,RangedSummarizedExperiment) importFrom(BiocGenerics, @@ -613,12 +601,12 @@ importFrom(BiocGenerics, ncol, nrow, path, - weights + weights, + width ) importFrom(BiocParallel, MulticoreParam, bplapply, - bpparam, multicoreWorkers ) importFrom(Biostrings, @@ -635,10 +623,18 @@ importFrom(IRanges, CharacterList, DataFrameList, IRanges, - findOverlaps + findOverlaps, + overlapsAny ) importFrom(MASS,ginv) -importFrom(MultiAssayExperiment,longForm) +importFrom(MultiAssayExperiment, + ExperimentList, + MultiAssayExperiment, + experiments, + longForm, + subsetByAssay, + subsetByRow +) importFrom(Rsamtools, TabixFile, asBcf, @@ -656,8 +652,10 @@ importFrom(S4Vectors, SimpleList, bindROWS, endoapply, + extractROWS, mcols, metadata, + normalizeSingleBracketSubscript, queryHits, subjectHits ) @@ -696,7 +694,6 @@ importFrom(checkmate, checkList, checkMatrix, checkNames, - checkNull, checkNumeric, checkString, checkSubset, @@ -761,6 +758,7 @@ importFrom(purrr, list_rbind, map, map2, + map2_dbl, map2_lgl, map_chr, map_dbl, @@ -768,6 +766,7 @@ importFrom(purrr, map_int, map_lgl, partial, + pmap, possibly, reduce, set_names, @@ -788,19 +787,20 @@ importFrom(rlang, abort, arg_match, arg_match0, + caller_arg, cnd_signal, + eval_bare, + fn_env, + fn_fmls, inform, is_named, try_fetch, warn ) importFrom(stats, - as.dist, coef, complete.cases, cor, - cutree, - hclust, lm, lm.fit, median, @@ -861,15 +861,10 @@ importFrom(tidyr, separate ) importFrom(tidyselect,all_of) -importFrom(tools, - file_ext, - file_path_sans_ext -) +importFrom(tools,file_ext) importFrom(utils, combn, - head, - read.table, - tail + head ) importFrom(vctrs,vec_duplicate_detect) importFrom(vroom,vroom) diff --git a/R/AllClasses.R b/R/AllClasses.R index 153bd0f0f..044f16a68 100644 --- a/R/AllClasses.R +++ b/R/AllClasses.R @@ -11,71 +11,40 @@ # topologically orders the Collate field for us. # ============================================================================= -#' @include AllGenerics.R GenotypeHandle.R RangedTupleList.R +#' @include AllGenerics.R GenotypeHandle.R RangedTupleList.R LdMatrix.R +#' @include GenotypeMixture.R #' @importFrom methods setClass setMethod new is validObject +#' @importClassesFrom GenomicRanges GRanges +#' @importClassesFrom S4Vectors DataFrame NULL -# The LD reference panel a collection was harmonized against, as the same -# RangedSummarizedExperiment shape QtlDataset uses for its genotypes: variants -# in rowRanges, dosages in a DelayedArray assay that reads lazily through a -# GenotypeHandle. NULL when a collection carries no panel. -# -# A union rather than "ANY": the slot has always been documented as holding a -# panel, and an untyped slot with a typed docstring is the kind of thing that -# is only true until someone puts something else in it. -#' @importClassesFrom SummarizedExperiment RangedSummarizedExperiment -setClassUnion( - "LdSketch_OR_NULL", - c("RangedSummarizedExperiment", "NULL") -) - -# What an LdData can read genotypes from. Four shapes, all real: +# The LD reference a collection was harmonized against -- the one place every +# pipeline reads LD from. It is one of two things: # -# GenotypeHandle a lazy file-backed panel; `snpIdx` selects into its whole -# snpInfo, so the handle must not be pre-narrowed -# list one handle per panel for a mixture reference, averaged by -# `mixtureWeights` -# matrix dosages already extracted and filtered, kept so -# genotypes() answers without reopening the file (see -# .loadLdFromBlocks); `snpIdx` is NULL in this case because -# the matrix is already the subset -# NULL no genotypes -- the object carries a pre-computed R +# RangedSummarizedExperiment a genotype panel, the same shape QtlDataset +# uses for its genotypes: variants in rowRanges, +# dosages in a DelayedArray assay that reads +# lazily through a GenotypeHandle +# GenotypeMixture several such panels, their LD averaged +# LdMatrix a correlation matrix the caller built # -# A panel (RangedSummarizedExperiment) is what callers pass; the constructor -# unwraps it to its handle, so the slot itself never holds one. +# NULL when a collection carries no LD reference. # -# A union rather than "ANY", for the same reason as above: the slot's -# docstring named two of these four shapes and nothing enforced even that. +# A union rather than "ANY": the slot has always been documented as holding +# one of these, and an untyped slot with a typed docstring is the kind of +# thing that is only true until someone puts something else in it. +#' @importClassesFrom SummarizedExperiment RangedSummarizedExperiment setClassUnion( - "LdGenotypeSource", - c("GenotypeHandle", "matrix", "list", "NULL") + "LdReference_OR_NULL", + c("RangedSummarizedExperiment", "GenotypeMixture", "LdMatrix", "NULL") ) -# The rest of LdData's payload, typed for the same reason. Each union is the -# set of shapes the class is actually built with, confirmed by recording slot -# classes at validity across the LD test files (validity runs for every -# object, however it was constructed). -# -# `correlation` is a single matrix, or one matrix per block for -# block-diagonal LD, or NULL when it has to be computed from genotypes. -setClassUnion("LdCorrelation", c("matrix", "list", "NULL")) - -# `snpIdx` selects into the genotype source's snpInfo; NULL when the -# correlation is pre-computed, or when the source is already the subset. The -# constructor coerces to integer, so a caller may pass doubles. -setClassUnion("LdSnpIndex", c("integer", "NULL")) - -# Mixing proportions, one per panel, when `genotypeHandle` is a list. -#' @importClassesFrom GenomicRanges GRanges -#' @importClassesFrom S4Vectors DataFrame -setClassUnion("LdMixtureWeights", c("numeric", "NULL")) - # ============================================================================= # SumStatsBase # ----------------------------------------------------------------------------- # Shared parent of the QTL and GWAS summary statistics collections. # Concrete subclasses (QtlSumStats, GwasSumStats) inherit from -# RangedTupleList and share the ldSketch / qcInfo slots (the genome build +# RangedTupleList and share the ldReference / qcInfo slots (the genome build # lives in seqinfo, not a slot). Each element # is one tuple's per-variant GRanges: x[[i]], formerly x$entry[[i]]. # @@ -89,17 +58,20 @@ setClassUnion("LdMixtureWeights", c("numeric", "NULL")) #' @description Virtual base class for QTL and GWAS summary statistics #' collections. Concrete subclasses (\code{QtlSumStats}, \code{GwasSumStats}) #' inherit from \code{\linkS4class{RangedTupleList}} and share the -#' \code{ldSketch} / \code{qcInfo} slots, and the genome build in +#' \code{ldReference} / \code{qcInfo} slots, and the genome build in #' \code{seqinfo()}. #' #' Each element is the per-variant \code{GRanges} of one tuple, so #' \code{x[[i]]} is that tuple's summary statistics and the identity columns #' live in \code{mcols(x)}. There is no \code{entry} column: what used to be #' \code{x$entry[[i]]} is now simply \code{x[[i]]}. -#' @slot ldSketch The \code{GenotypeHandle} the QC pipeline harmonized against, -#' or \code{NULL}. Optional: LD-free workflows (e.g. mash, which operates -#' across conditions per variant) carry \code{NULL}; pipelines that need LD -#' validate its presence when they consume the collection. +#' @slot ldReference The LD reference the collection was harmonized against +#' and every pipeline reads LD from: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}, or \code{NULL}. Optional: LD-free workflows +#' (e.g. mash, which operates across conditions per variant) carry +#' \code{NULL}; pipelines that need LD validate its presence when they +#' consume the collection. #' @slot qcInfo A \code{list} recording which QC steps ran. Empty \code{list()} #' on construction; populated by \code{summaryStatsQc()} with a per-step audit #' record (filter names, drop counts, liftover target, RAISS settings, etc.). @@ -111,7 +83,7 @@ setClass( "SumStatsBase", contains = c("VIRTUAL", "RangedTupleList"), representation( - ldSketch = "LdSketch_OR_NULL", + ldReference = "LdReference_OR_NULL", qcInfo = "list" ) ) @@ -164,8 +136,10 @@ setClass( return(gr) } win <- .asGRegion(ranges) - onWindowChrom <- as.character(seqnames(gr)) %in% + onWindowChrom <- is_in( + as.character(seqnames(gr)), as.character(seqnames(win)) + ) if (!any(onWindowChrom)) { return(gr[0L]) } @@ -246,12 +220,12 @@ setMethod("qcDiagnostics", "SumStatsBase", function(x, entry = 1L) { } }) -#' @rdname ldSketch +#' @rdname ldReference #' @examples #' data(qtlSumStatsExample) -#' ldSketch(qtlSumStatsExample) +#' ldReference(qtlSumStatsExample) #' @export -setMethod("ldSketch", "SumStatsBase", function(x) x@ldSketch) +setMethod("ldReference", "SumStatsBase", function(x) x@ldReference) #' @rdname studyName #' @examples @@ -729,7 +703,7 @@ setMethod( # ----------------------------------------------------------------------------- # Shared parent of the QTL and GWAS fine-mapping result collections. # Concrete subclasses (QtlFineMappingResult, GwasFineMappingResult) carry -# a DFrame of per-fit rows plus a shared ldSketch slot. Downstream +# a DFrame of per-fit rows plus a shared ldReference slot. Downstream # pipelines dispatch on FineMappingResultBase for behaviors that apply to # either flavour, and on the concrete subclass when the tuple shape # matters. @@ -738,19 +712,20 @@ setMethod( #' @title Fine-Mapping Result Base Class #' @description Virtual base class for fine-mapping result collections. Concrete #' subclasses (\code{QtlFineMappingResult}, \code{GwasFineMappingResult}) -#' carry a \code{DFrame} of per-fit rows and a shared \code{ldSketch} slot. +#' carry a \code{DFrame} of per-fit rows and a shared \code{ldReference} slot. #' Downstream pipelines should dispatch on \code{FineMappingResultBase} for #' behaviors that apply to either flavour, and on the concrete subclass when #' the tuple shape matters. -#' @slot ldSketch The LD reference \code{GenotypeHandle} the fits were computed -#' against, or \code{NULL} when the fits were derived from individual-level -#' data (no LD reference). Used downstream for cross-pipeline LD-sketch -#' identity validation. +#' @slot ldReference The LD reference the fits were computed against (a +#' genotype panel, a \code{GenotypeMixture} or an \code{LdMatrix}), or +#' \code{NULL} when the fits were derived from individual-level data. Used +#' downstream to check that pipelines combining results share one LD +#' reference. #' @export setClass( "FineMappingResultBase", contains = c("VIRTUAL", "RangedTupleList"), - representation(ldSketch = "LdSketch_OR_NULL") + representation(ldReference = "LdReference_OR_NULL") ) #' @rdname studyName @@ -759,9 +734,9 @@ setMethod("studyName", "FineMappingResultBase", function(x) { unique(as.character(x$study)) }) -#' @rdname ldSketch +#' @rdname ldReference #' @export -setMethod("ldSketch", "FineMappingResultBase", function(x) x@ldSketch) +setMethod("ldReference", "FineMappingResultBase", function(x) x@ldReference) #' @rdname methodNames #' @examples @@ -846,7 +821,7 @@ setMethod( GenomicRanges::GRangesList(map(entries, variants)), value = .withEntryPayload(mcols(x, use.names = FALSE), entries) ) - new(class(x), grl, ldSketch = .asLdSketch(ldSketch(x))) + new(class(x), grl, ldReference = .asLdRef(ldReference(x))) } # TRUE when an entry shares at least one variant with `keepVariants`, matched diff --git a/R/AllGenerics.R b/R/AllGenerics.R index 6721f9499..deb1db412 100644 --- a/R/AllGenerics.R +++ b/R/AllGenerics.R @@ -73,7 +73,7 @@ NULL #' ) #' ss <- GwasSumStats(studyName = "trait1", entry = list(gr), #' genome = unique(unname(GenomeInfoDb::genome(ldScoreExample))), -#' ldSketch = panel) +#' ldReference = panel) #' estimateH2(ss, ldScoreExample, method = "sldsc") #' @export setGeneric( @@ -96,7 +96,9 @@ setGeneric( #' @title Compute LD Scores #' @description Compute LD scores from an LD reference, optionally stratified by -#' annotations. +#' annotations. Each r-squared is ldsc's small-sample unbiased estimate, +#' \eqn{r^2 - (1 - r^2)/(n - 2)} for a reference of \eqn{n} samples, as +#' \code{\link{buildLdScore}} stores, so the two agree. #' @param ldRef An \code{LdStatistic} object. #' @param annotations An \code{AnnotationMatrix} object, or NULL. #' @param ... Additional arguments. @@ -114,14 +116,14 @@ setGeneric("computeLdScores", function(ldRef, annotations = NULL, ...) { # ============================================================================= #' @title Read a Genotype Panel -#' @description Read genotype data (VCF, plink1, plink2 or GDS) as a +#' @description Read genotype data (VCF, plink1, plink2, GDS or BGEN) as a #' \code{RangedSummarizedExperiment}: variants in \code{rowRanges}, #' samples in \code{colData}, and dosages in a \code{DelayedArray} assay #' that reads from the file only when something touches it. Nothing is read #' here beyond the variant and sample metadata. #' #' The panel is the same shape a \code{QtlDataset} uses for its genotypes -#' and the same shape a collection carries as its \code{ldSketch}, so the +#' and the same shape a collection carries as its \code{ldReference}, so the #' Bioconductor and tidy surfaces apply throughout: subset it with #' \code{panel[i, j]}, ask \code{rowRanges()} for the variants, #' \code{colnames()} for the samples, and hand it to @@ -135,11 +137,35 @@ setGeneric("computeLdScores", function(ldRef, annotations = NULL, ...) { #' with \code{chroms}), or \code{ldMeta} plus \code{region} to resolve a #' block out of an LD-meta table. Exactly one source may be given. #' @param path Character, path to a single self-describing genotype file -#' (\code{.vcf}, \code{.vcf.gz}, \code{.vcf.bgz}, \code{.bcf} or -#' \code{.gds}). Omit it when naming the panel by one of the keyword -#' sources described above. -#' @param format Character, one of "vcf", "plink1", "plink2", "gds". If NULL, -#' inferred from file extension. +#' (\code{.vcf.gz}, \code{.vcf.bgz}, \code{.gds} or \code{.bgen}). Omit it +#' when naming the panel by one of the keyword sources described above. +#' +#' A VCF must be bgzip-compressed with its tabix index (\code{.tbi}) beside +#' it, through which its records are read a block at a time; a BCF cannot +#' be read. Its dosage is its FORMAT/DS when the header declares one, else +#' the copies of the ALT allele in each GT call, a call with a missing +#' allele being missing. +#' +#' A GDS file is SNPRelate's, whose genotype codes count the first of each +#' SNP's two alleles (A1), or SeqArray's, read like a VCF: its FORMAT/DS +#' when it stores one, else its genotype calls. +#' +#' A BGEN file of any layout (BGEN 1.1, 1.2 or 1.3), phased or unphased +#' and of any ploidy, needs its \code{.bgi} index beside it and its sample +#' ids in the file or in a \code{.sample} file beside it. Its dosage is the +#' expected count of A1, from 0 to the sample's ploidy. +#' +#' A PLINK1 fileset, a \code{.bed} with its \code{.bim} and \code{.fam} +#' beside it, is read through the PlinkMatrix package: its dosage counts +#' each variant's A1 allele, the \code{.bim}'s fifth column, a missing +#' call being missing. Its samples are named by the \code{.fam}'s +#' individual ids. +#' +#' In a VCF, a SeqArray GDS or a BGEN file, A2 is each record's first (REF) +#' allele, and a record with more than one other allele is read, with a +#' warning, as one variant per other allele, each counting its own. +#' @param format Character, one of "vcf", "plink1", "plink2", "gds", "bgen". +#' If NULL, inferred from file extension. #' @param ... The keyword source arguments described above, plus any further #' arguments forwarded to the format-specific reader. #' @param plink1Prefix,plink2Prefix Character. Stem of a plink1 @@ -151,7 +177,8 @@ setGeneric("computeLdScores", function(ldRef, annotations = NULL, ...) { #' plink2 triplet, when they do not share a stem. #' @param genoMeta One-file-per-chromosome specification: a path to a #' \code{#chr,path} meta file, or a named character vector (names = -#' chromosomes, values = payload paths / prefixes). +#' chromosomes, values = payload paths / prefixes). Every file must be of +#' one format and hold the same samples in the same order. #' @param chroms Optional character vector of chromosomes. Only the #' \code{genoMeta} shards whose chromosome is listed are read; chromosome #' labels are compared canonically. Meaningful only with \code{genoMeta}. @@ -162,7 +189,8 @@ setGeneric("computeLdScores", function(ldRef, annotations = NULL, ...) { #' \code{"chr:start-end"} string, or a one-row data.frame with #' \code{chrom}, \code{start}, \code{end}. #' @param vcfArgs Optional named list of arguments forwarded to -#' \code{VariantAnnotation::readVcf} when the source is a VCF. +#' \code{VariantAnnotation::readVcf} when a VCF's records are read (its +#' genotypes are read a block at a time, as they are needed). #' @return A \code{RangedSummarizedExperiment} of variants x samples. #' @seealso \code{\link{computeLd}} #' @examples @@ -272,8 +300,9 @@ setGeneric("z", function(x, ...) standardGeneric("z")) #' @title Get Sample Sizes #' @description Extract sample sizes: the per-variant sample-size vector of a #' \code{GwasSumStats} or \code{QtlSumStats} entry, selected by its identity -#' tuple, or the number of samples carried by a \code{GenotypeHandle}. -#' @param x A \code{GwasSumStats}, \code{QtlSumStats} or +#' tuple, the number of samples carried by a \code{GenotypeHandle}, or the +#' panel size an \code{\link{LdMatrix}} records. +#' @param x A \code{GwasSumStats}, \code{QtlSumStats}, \code{LdMatrix} or #' \code{GenotypeHandle} object. #' @param ... Class-specific selection arguments. #' @param studyName Character (length 1) or \code{NULL}. Restrict the selection @@ -287,7 +316,8 @@ setGeneric("z", function(x, ...) standardGeneric("z")) #' @param ranges A \code{GRanges} or \code{NULL} (default). Return only the #' variants overlapping these ranges; \code{NULL} returns everything. #' @return Numeric vector of sample sizes for a summary-statistic entry; -#' integer of length 1 for a \code{GenotypeHandle}. +#' integer of length 1 for a \code{GenotypeHandle} or an \code{LdMatrix} +#' (\code{NA} when the matrix records no size). #' @details The genotype handle is the seed layer behind a panel and is not #' part of the public interface; obtain a panel from #' \code{readGenotypes()} and ask it directly. @@ -435,16 +465,17 @@ setGeneric("nSamples<-", function(x, ..., value) { #' @title Get Effect-Allele Frequencies #' @description Extract the directional effect-allele (A1) frequency vector -#' for a \code{QtlDataset}, or for one entry of a \code{GwasSumStats} / -#' \code{QtlSumStats} collection. Unlike \code{\link{maf}}, the value is -#' \emph{not} folded to the minor allele: it is the frequency of the -#' dosage-counted allele (A1, the effect allele), matching the allele the -#' marginal effect sizes and the fine-mapping \code{af} column report. For -#' a summary-statistic collection this is \code{NULL} when the source +#' for a \code{QtlDataset}, an \code{\link{LdMatrix}}, or one entry of a +#' \code{GwasSumStats} / \code{QtlSumStats} collection. Unlike +#' \code{\link{maf}}, the value is \emph{not} folded to the minor allele: it +#' is the frequency of the dosage-counted allele (A1, the effect allele), +#' matching the allele the marginal effect sizes and the fine-mapping +#' \code{af} column report. For a summary-statistic collection this is +#' \code{NULL} when the source #' carried no frequency column -- see \code{\link{variantColumnReplace}} #' for supplying one afterwards. -#' @param x A \code{QtlDataset}, \code{GwasSumStats} or \code{QtlSumStats} -#' object. +#' @param x A \code{QtlDataset}, \code{LdMatrix}, \code{GwasSumStats} or +#' \code{QtlSumStats} object. #' @param ... Class-specific selection arguments (e.g., \code{traitId}, #' \code{region}, \code{cisWindow}, \code{samples} for \code{QtlDataset}). #' @param traitId Character or \code{NULL}. Molecular trait / feature identifier @@ -777,47 +808,46 @@ setGeneric("qcDiagnostics", function(x, entry = 1L, ...) { standardGeneric("qcDiagnostics") }) -#' @title Get LD Sketch -#' @description Return the genotype panel carrying the LD reference for -#' this collection. Defined on classes that embed an \code{ldSketch} slot: -#' \code{GwasSumStats}, \code{QtlSumStats}, \code{FineMappingResult}, -#' \code{TwasWeights}. Returns \code{NULL} when the slot is unset (e.g. a -#' \code{TwasWeights} fit from individual-level data via \code{QtlDataset}). -#' @param x An S4 object that carries an \code{ldSketch} slot. +#' @title Get LD Reference +#' @description Return the LD reference of this object: a genotype panel +#' (see \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or +#' an \code{\link{LdMatrix}}. Defined on classes that embed an +#' \code{ldReference} slot: \code{GwasSumStats}, \code{QtlSumStats}, +#' \code{FineMappingResultBase}, \code{TwasWeights}, +#' \code{ColocResultBase} and \code{LdStatistic}, whose reference is the +#' panel it reads its blocks' LD from. Returns \code{NULL} when the slot is +#' unset (e.g. a \code{TwasWeights} fit from individual-level data via +#' \code{QtlDataset}, or an \code{LdStatistic} built from \code{LdMatrix} +#' blocks). +#' @param x An S4 object that carries an \code{ldReference} slot. #' @param ... Unused. -#' @return A \code{RangedSummarizedExperiment} genotype panel, or -#' \code{NULL}. +#' @return A \code{RangedSummarizedExperiment} genotype panel, a +#' \code{GenotypeMixture}, an \code{LdMatrix}, or \code{NULL}. #' @export -setGeneric("ldSketch", function(x, ...) standardGeneric("ldSketch")) +setGeneric("ldReference", function(x, ...) standardGeneric("ldReference")) # ============================================================================= -# LdData accessor generics +# LD accessor generics # ============================================================================= #' @title Get LD Correlation Matrix -#' @description Extract the LD correlation matrix from an \code{LdData} object. -#' If only a genotype handle is available, recomputes R from genotypes on the -#' fly. -#' @param x An \code{LdData} object. -#' @return A correlation matrix, or a list of per-block matrices. +#' @description Extract the LD correlation matrix an \code{\link{LdMatrix}} +#' holds, labelled by its variant ids. +#' @param x An \code{LdMatrix}. +#' @return A correlation matrix. #' @examples #' data(eqtlRegionExample) #' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' ldMatrix(ld) +#' ld <- LdMatrix(cor(X), nSamples = nrow(X)) +#' ldMatrix(ld)[1:3, 1:3] #' @export setGeneric("ldMatrix", function(x) standardGeneric("ldMatrix")) #' @title Get Genotype Matrix #' @description Extract a genotype matrix from an object that carries genotype -#' data. For an \code{LdData}, returns the underlying genotype matrix via its -#' handle (or \code{NULL} if no handle is available). For a \code{QtlDataset}, -#' returns the genotype matrix for a selected set of traits or region (see -#' method documentation for the per-class selection arguments). +#' data. For a \code{QtlDataset}, returns the genotype matrix for a selected +#' set of traits or region (see method documentation for the per-class +#' selection arguments). #' @param x The object to extract from. #' @param ... Class-specific selection arguments (e.g., \code{traitId}, #' \code{region}, \code{cisWindow} for \code{QtlDataset}). @@ -837,26 +867,9 @@ setGeneric("ldMatrix", function(x) standardGeneric("ldMatrix")) #' @export setGeneric("genotypes", function(x, ...) standardGeneric("genotypes")) -#' @title Check Genotype Availability -#' @description Check whether an \code{LdData} object has a genotype handle for -#' extracting raw genotypes. -#' @param x An \code{LdData} object. -#' @return Logical. -#' @examples -#' data(eqtlRegionExample) -#' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' hasGenotypes(ld) -#' @export -setGeneric("hasGenotypes", function(x) standardGeneric("hasGenotypes")) - #' @title Get Variant IDs #' @description Extract variant ID vector from an object that carries one (e.g., -#' \code{LdData}, \code{FineMappingRow}, \code{TwasWeightsRow}) or from +#' \code{LdMatrix}, \code{FineMappingRow}, \code{TwasWeightsRow}) or from #' one entry of a collection class selected by its identity tuple. #' @param x The object. #' @param ... Class-specific selection arguments. @@ -920,17 +933,18 @@ setGeneric("molecularTraits", function(x, ...) { standardGeneric("molecularTraits") }) # ============================================================================= -# FineMappingResult accessor generics +# Fine-mapping result accessor generics # ============================================================================= #' @title Get a Single Fine-Mapping Entry -#' @description Return the \code{FineMappingRow} for one \code{(study, -#' context, trait, method)} row of a \code{FineMappingResult} collection. -#' @param x A \code{FineMappingResult} object. +#' @description Return one row of a \code{QtlFineMappingResult} or +#' \code{GwasFineMappingResult}, selected by its identity, as a single-row +#' collection of the same class. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param studyName,context,trait,method Single character identifiers. All #' required when the collection has more than one row; optional when the #' collection has a single row. -#' @return A \code{FineMappingRow} object. +#' @return A single-row collection of the same class as \code{x}. #' @examples #' data(qtlFineMappingExample) #' fineMappingResult(qtlFineMappingExample, studyName = "study_1", @@ -955,8 +969,8 @@ setGeneric( #' \item The user declined to impute missing variants in a GWAS #' \code{SumStats}, so a downstream fine-mapping result needs #' PIPs restricted to the GWAS-covered intersection. -#' \item Colocalization between a GWAS \code{FineMappingResult} and a -#' QTL \code{FineMappingResult} computed on different variant +#' \item Colocalization between a \code{GwasFineMappingResult} and a +#' \code{QtlFineMappingResult} computed on different variant #' sets -- the GWAS PIPs (or QTL PIPs) get renormalized to the #' common variant set. #' } @@ -972,10 +986,10 @@ setGeneric("adjustPips", function(x, keepVariants, ...) { }) #' @title Get PIP Values -#' @description Extract posterior inclusion probabilities from a single -#' \code{FineMappingRow} or from one entry of a \code{FineMappingResult} -#' (selected by its identity tuple). -#' @param x A \code{FineMappingRow} or \code{FineMappingResult}. +#' @description Extract posterior inclusion probabilities from a +#' \code{QtlFineMappingResult} or \code{GwasFineMappingResult}, narrowed to +#' the entries its identity selectors pick. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param ... Class-specific selection arguments. #' @param studyName Character (length 1) or \code{NULL}. Restrict the selection #' to this study; \code{NULL} matches all studies. @@ -1002,7 +1016,8 @@ setGeneric("pip", function(x, ...) standardGeneric("pip")) #' result. The fit may be the trimmed view (when the pipeline ran with the #' default \code{trim = TRUE}) or the full untrimmed \code{susie()} return #' (when \code{trim = FALSE}). -#' @param x A \code{FineMappingRow} or \code{FineMappingResult}. +#' @param x A \code{FineMappingRow}, \code{QtlFineMappingResult} or +#' \code{GwasFineMappingResult}. #' @param ... Class-specific selection arguments. #' @param studyName Character (length 1) or \code{NULL}. Restrict the selection #' to this study; \code{NULL} matches all studies. @@ -1027,14 +1042,14 @@ setGeneric("susieFit", function(x, ...) standardGeneric("susieFit")) #' \code{\link{fineMappingRow}} on demand from an LD source. The correlation #' is a view over the fit-time LD, which lives on the data object --- a #' \code{QtlDataset}'s genotypes or a \code{QtlSumStats} / \code{GwasSumStats} -#' LD sketch --- and is deliberately never stored on the fit. An +#' LD reference --- and is deliberately never stored on the fit. An #' \code{ldSource} is therefore always required. #' @param x A \code{\link{fineMappingRow}} carrying the fit (credible-set #' membership and PIP) and its variant ids. #' @param ldSource The object supplying the LD: a \code{QtlDataset} #' (individual-level; genotypes via \code{\link{genotypes}}) or a #' \code{QtlSumStats} / \code{GwasSumStats} (summary statistics; LD via -#' \code{\link{ldSketch}}). Any other type is an error. +#' \code{\link{ldReference}}). Any other type is an error. #' @param ... Passed to methods. #' @return A numeric \code{m x m} between-credible-set correlation matrix #' (\code{m} = number of credible sets), or \code{NULL} when the fit has @@ -1070,13 +1085,13 @@ setGeneric("computeCsCorrelation", function(x, ldSource, ...) { #' @title Get Cross-Validation Result #' @description Extract the cross-validation payload stored on a #' \code{FineMappingRow} (or the matching entry of a -#' \code{FineMappingResult}). The payload is a list with components +#' \code{QtlFineMappingResult}). The payload is a list with components #' \code{samplePartition} (a \code{data.frame} of \code{Sample}/\code{Fold} #' assignments), \code{predictions} (a named list of per-method out-of-fold #' prediction matrices), and \code{performance} (a named list of per-method #' metric matrices). \code{NULL} when fine-mapping was run without -#' cross-validation (\code{cvFolds <= 1}). -#' @param x A \code{FineMappingRow} or \code{FineMappingResult}. +#' cross-validation. +#' @param x A \code{FineMappingRow} or \code{QtlFineMappingResult}. #' @param ... Class-specific selection arguments. #' @param studyName Character (length 1) or \code{NULL}. Restrict the selection #' to this study; \code{NULL} matches all studies. @@ -1099,7 +1114,7 @@ setGeneric("cvResult", function(x, ...) standardGeneric("cvResult")) #' columns (\code{variant_id, chrom, pos, A1, A2}), context (\code{N, MAF}), #' and the marginal effect columns (\code{beta, se, z, p}). Populated #' uniformly across the individual-level and RSS paths. -#' @param x A \code{FineMappingRow} or \code{FineMappingResult}. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param maxPval Optional numeric (length 1). When non-\code{NULL}, filter rows #' where \code{p > maxPval}. Default \code{NULL} (no filter). #' @param ... Class-specific selection arguments. @@ -1131,7 +1146,7 @@ setGeneric("marginalEffects", function(x, maxPval = NULL, ...) { #' \code{pip}, and credible-set membership columns (\code{cs_95}, etc.). Rows #' are filtered by PIP by default -- set \code{signalCutoff = 0} to return #' every variant. -#' @param x A \code{FineMappingRow} or \code{FineMappingResult}. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param signalCutoff Numeric (length 1). Drop rows where \code{pip <= #' signalCutoff}. Default \code{0.025}. Use \code{signalCutoff = 0} to keep #' every variant. @@ -1171,7 +1186,7 @@ setGeneric( #' @title Get Credible Sets #' @description Extract credible set assignments at the requested coverage. -#' @param x A \code{FineMappingRow} or \code{FineMappingResult}. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param ... Class-specific selection arguments plus \code{coverage}. #' @param studyName Character (length 1) or \code{NULL}. Restrict the selection #' to this study; \code{NULL} matches all studies. @@ -1204,15 +1219,16 @@ setGeneric("credibleSets", function(x, ...) standardGeneric("credibleSets")) #' single weight-source entry, so cTWAS and #' \code{\link{causalInferencePipeline}} extract weights identically whether #' the source is a \code{TwasWeights} collection (its learned weight vector) -#' or a \code{FineMappingResult} one (its topLoci posterior effect). The -#' selectors pin the single row the weights come from. -#' @param x A \code{TwasWeights} or a \code{FineMappingResult}. +#' or a fine-mapping one (its topLoci posterior effect). The selectors pin +#' the single row the weights come from. +#' @param x A \code{TwasWeights}, \code{QtlFineMappingResult} or +#' \code{GwasFineMappingResult}. #' @param studyName,context,trait,method Optional length-1 selectors pinning one #' row of a collection, as elsewhere; each \code{NULL} (default) leaves that #' part of the tuple unconstrained. Ignored when \code{x} is already a row. #' @param region Character (length 1, \code{"chr:start-end"}) or \code{NULL}. -#' A further selector for a \code{FineMappingResult}, whose rows also carry -#' a region; \code{NULL} (default) leaves it unconstrained. +#' A further selector for a fine-mapping result, whose rows also carry a +#' region; \code{NULL} (default) leaves it unconstrained. #' @param ... Reserved for future use. #' @return A \code{list} with \code{variantIds} (character) and \code{weights} #' (numeric) of equal length; both empty when no usable weights are present. @@ -1345,8 +1361,8 @@ setGeneric("cvFits", function(x, ...) standardGeneric("cvFits")) #' @title Get Method Names #' @description Extract the method name(s) an object was produced with: one #' per entry for a collection, a single name for one estimate. -#' @param x A \code{FineMappingResult}, \code{TwasWeights}, -#' \code{CtwasResult} or \code{H2Estimate} object. +#' @param x A \code{QtlFineMappingResult}, \code{GwasFineMappingResult}, +#' \code{TwasWeights}, \code{CtwasResult} or \code{H2Estimate} object. #' @return Character vector. #' @examples #' data(h2EstimateExample) @@ -1377,9 +1393,6 @@ setGeneric("weightsDataType", function(x, ...) { standardGeneric("weightsDataType") }) -# ============================================================================= -# AlleleQcResult accessor generics -# QcResult accessor generics # ============================================================================= # Published-output generics (VCF/BCF and BED) # ============================================================================= @@ -1397,7 +1410,7 @@ setGeneric("weightsDataType", function(x, ...) { #' \describe{ #' \item{\code{GwasSumStats}}{\code{ES} / \code{SS} / \code{AF} for one #' study.} -#' \item{\code{FineMappingResult}}{the marginal fields (\code{ES}, +#' \item{a fine-mapping result}{the marginal fields (\code{ES}, #' \code{SE}, \code{LP}, \code{SS}, \code{AF}) plus the posterior ones #' (\code{PIP}, \code{LBF}, \code{LFSR}, and a \code{CS}/\code{PUR} pair #' per credible-set coverage present).} @@ -1417,9 +1430,10 @@ setGeneric("weightsDataType", function(x, ...) { #' \code{splitByContext} / \code{splitByTrait} opt out of that and write one #' single-sample file per fit, at a path decorated with the axis value. #' -#' @param x A \code{GwasSumStats}, \code{FineMappingResult} or -#' \code{TwasWeights} object, or a \code{list} of \code{FineMappingResult} -#' / \code{TwasWeights} objects to combine first. +#' @param x A \code{GwasSumStats}, \code{QtlFineMappingResult}, +#' \code{GwasFineMappingResult} or \code{TwasWeights} object, or a +#' \code{list} of fine-mapping results or of \code{TwasWeights} objects to +#' combine first. #' @param outputPath File path for output. Extension determines format: #' \code{.vcf.gz} or \code{.vcf.bgz} for bgzipped VCF, \code{.bcf} for BCF, #' \code{.vcf} for uncompressed VCF. @@ -1459,7 +1473,7 @@ setGeneric("exportToVcf", function(x, outputPath, sampleName = NULL, ...) { #' observation. #' #' A \code{TwasWeights} writes one table, a row per (variant, weight column). -#' A \code{FineMappingResult} offers two, selected with \code{schema}: +#' A fine-mapping result offers two, selected with \code{schema}: #' \describe{ #' \item{\code{"topLoci"}}{(default) the posterior view joined to the #' marginal one, a row per (variant, context).} @@ -1483,12 +1497,12 @@ setGeneric("exportToVcf", function(x, outputPath, sampleName = NULL, ...) { #' \code{twasWeightsPipeline(fitFullData = FALSE)} produces) is refused rather #' than written out empty. #' -#' @param x A \code{FineMappingResult} or \code{TwasWeights} object, or a -#' \code{list} of either, to combine first. +#' @param x A \code{QtlFineMappingResult}, \code{GwasFineMappingResult} or +#' \code{TwasWeights} object, or a \code{list} of them, to combine first. #' @param outputPath Output path. A \code{.gz} / \code{.bgz} extension selects #' bgzip + tabix (via Rsamtools, so no htslib binary is needed); any other #' extension writes a plain table. -#' @param schema \code{FineMappingResult} only: which published schema to +#' @param schema Fine-mapping results only: which published schema to #' write, \code{"topLoci"} (default) or \code{"fsusie"}. #' @param coverage Which credible-set coverage levels to publish, as #' proportions (\code{c(0.95, 0.5)}). \code{NULL} (default) publishes every @@ -1505,9 +1519,9 @@ setGeneric("exportToVcf", function(x, outputPath, sampleName = NULL, ...) { #' written by default; naming one drops the other's columns, leaving a strict #' subset of the full table. #' @param signalCutoff PIP cutoff passed to \code{\link{topLoci}} -#' (\code{FineMappingResult}, \code{"topLoci"} schema only). Default 0.025. +#' (fine-mapping results, \code{"topLoci"} schema only). Default 0.025. #' @param minPurity Optional credible-set purity cutoff passed to -#' \code{\link{topLoci}} (\code{FineMappingResult}, \code{"topLoci"} schema +#' \code{\link{topLoci}} (fine-mapping results, \code{"topLoci"} schema #' only). Default \code{NULL} for no purity filter. #' @param columns Which of the SOURCE view's own variant-level columns to #' append after the published schema. \code{NULL} (default) appends none; @@ -1583,7 +1597,7 @@ setGeneric("traitNames", function(x) standardGeneric("traitNames")) #' @title Get Per-Row Analysis Windows #' @description Return the genomic interval each row was analysed over, as a #' \code{GRanges} with one range per row -- the trait's own region for a -#' \code{TwasWeights}, and for a \code{FineMappingResult} the window the fit +#' \code{TwasWeights}, and for a fine-mapping result the window the fit #' was run over (the requested region, or the trait's cis-window when the fit #' was driven by \code{traitId} + \code{cisWindow}). This is location #' provenance (e.g. for cTWAS LD-block placement). @@ -1642,8 +1656,8 @@ setGeneric("blockId", function(x, ...) standardGeneric("blockId")) #' @description Return the molecular feature's OWN genomic coordinates (the gene #' / peak range; TSS = \code{start()}) for each row of a per-tuple collection, #' as a \code{GRanges} with one range per row. Distinct from -#' \code{\link{genomicRegion}} (the fine-mapping window for a -#' \code{FineMappingResult}): the true trait position cannot be inferred from +#' \code{\link{genomicRegion}} (the window a fine-mapping result was fit +#' over): the true trait position cannot be inferred from #' summary statistics, so it is threaded from the QtlDataset \code{rowRanges} #' or an explicit QtlSumStats trait-position and carried as provenance onto #' \code{QtlFineMappingResult} and \code{TwasWeights}. @@ -1679,7 +1693,7 @@ setGeneric("traitPosition", function(x, ...) { #' around the trait; \code{NULL} uses the dataset default. #' @param samples Character vector or \code{NULL}. Restrict to these sample IDs; #' \code{NULL} uses all samples. -#' @param residualizationArgs What the data is residualized on, built with +#' @param residualizationParam What the data is residualized on, built with #' \code{\link{ResidualizationParam}}: \code{phenotypeCovariates} and #' \code{genotypeCovariates} choose the covariate sets, #' \code{residualizePhenotype} / \code{residualizeGenotype} turn each @@ -1711,7 +1725,7 @@ setGeneric("residualizedGenotypes", function(x, ...) { #' @param region Character (length 1, \code{"chr:start-end"}) or \code{NULL}. #' Restrict variants to this region; \code{NULL} uses the full cis window / #' all regions. -#' @param residualizationArgs What the data is residualized on, built with +#' @param residualizationParam What the data is residualized on, built with #' \code{\link{ResidualizationParam}}: \code{phenotypeCovariates} and #' \code{genotypeCovariates} choose the covariate sets, #' \code{residualizePhenotype} / \code{residualizeGenotype} turn each @@ -1779,7 +1793,7 @@ setGeneric("scaleResiduals", function(x) { }) # ============================================================================= -# GenotypeHandle / LD-statistic / Annotation / LdData / H2Estimate accessors +# GenotypeHandle / LD-statistic / LdMatrix / Annotation / H2Estimate accessors # ============================================================================= #' @title Get SNP Info @@ -1800,7 +1814,7 @@ setGeneric("snpInfo", function(x) standardGeneric("snpInfo")) #' @title Get Genotype Storage Format #' @description Return the detected genotype storage format. #' @param x A \code{GenotypeHandle}. -#' @return Character (length 1): one of "gds", "vcf", "plink1", "plink2". +#' @return Character (length 1): one of "plink1", "plink2", "bgen". #' @details The genotype handle is the seed layer behind a panel and #' is not part of the public interface; obtain a panel from #' \code{readGenotypes()} and ask it directly. @@ -1829,10 +1843,16 @@ setGeneric("sampleIds", function(x) standardGeneric("sampleIds")) setGeneric("pgenPtr", function(x) standardGeneric("pgenPtr")) #' @title Get Per-Block Eigendecompositions -#' @description Return the per-block eigendecomposition list carried by an -#' \code{LdEigen} object. +#' @description Return the per-block eigendecompositions of an +#' \code{LdEigen}: the stored ones, or, for one built from a genotype +#' panel (which stores none), every block's computed from the panel now. +#' That holds all of them in memory at once, which the estimators avoid by +#' decomposing one block at a time. #' @param x An \code{LdEigen}. -#' @return List of per-block eigen decompositions. +#' @return A list with one element per block, each a list with +#' \code{values}, \code{vectors} and \code{snpIdx}. One computed from a +#' panel also carries \code{nNull}: the number of the block's directions +#' whose eigenvalue is exactly zero, which it does not list. #' @examples #' data(ldEigenExample) #' eigenList(ldEigenExample) @@ -1841,18 +1861,12 @@ setGeneric("eigenList", function(x) standardGeneric("eigenList")) #' @title Get LD Reference Panel Size #' @description Return the reference-panel sample size used to compute an -#' \code{LdStatistic} or carried by an \code{LdData}. -#' @param x An \code{LdStatistic} or \code{LdData}. +#' \code{LdStatistic}. +#' @param x An \code{LdStatistic}. #' @return Integer (length 1). #' @examples -#' data(eqtlRegionExample) -#' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' ldPanelNSamples(ld) +#' data(ldEigenExample) +#' ldPanelNSamples(ldEigenExample) #' @export setGeneric("ldPanelNSamples", function(x) standardGeneric("ldPanelNSamples")) @@ -1868,10 +1882,14 @@ setGeneric("ldPanelNSamples", function(x) standardGeneric("ldPanelNSamples")) setGeneric("inSample", function(x) standardGeneric("inSample")) #' @title Get Per-Block LD Matrix List -#' @description Return the list of per-block LD (R^2) matrices used for the FGLS -#' residual covariance in g-LDSC. +#' @description Return the per-block LD correlation matrices g-LDSC forms its +#' FGLS residual covariance from: the stored ones, or, for an +#' \code{LdScore} built from a genotype panel (which stores none), every +#' block's computed from the panel now. That holds all of them in memory at +#' once, which the estimators avoid by reading one block at a time. #' @param x An \code{LdScore}. -#' @return List of matrices (empty list for S-LDSC). +#' @return A list with one element per block, each a list with \code{R} and +#' \code{snpIdx}; empty for a scores-only \code{LdScore}. #' @examples #' data(ldScoreExample) #' ldMatrixList(ldScoreExample) @@ -1890,13 +1908,13 @@ setGeneric("ldMatrixList", function(x) standardGeneric("ldMatrixList")) setGeneric("ldBlocks", function(x) standardGeneric("ldBlocks")) -#' @title Get GenotypeHandle from LdData -#' @description Return the \code{GenotypeHandle} (or list of handles for mixture -#' panels) carried by an \code{LdData}. -#' @param x An \code{LdData}. -#' @return A \code{GenotypeHandle}, a list of them, or NULL. For a -#' \code{QtlDataset} this is the handle itself; \code{genotypes()} -#' extracts a dosage block from it instead. +#' @title Get GenotypeHandle +#' @description Return the \code{GenotypeHandle} behind a \code{QtlDataset}'s +#' genotype experiment. +#' @param x A \code{QtlDataset}. +#' @return A \code{GenotypeHandle}, or NULL for genotypes read through no +#' handle (a VCF, GDS or PLINK1 file, or held in memory); +#' \code{genotypes()} extracts a dosage block instead. #' @details The genotype handle is the seed layer behind a panel and #' is not part of the public interface; obtain a panel from #' \code{readGenotypes()} and ask it directly. @@ -1906,92 +1924,25 @@ setGeneric("genotypeHandle", function(x) { }) #' @title Get Mixture Weights -#' @description Return the per-panel mixing proportions carried by an -#' \code{LdData} when its \code{genotypeHandle} slot is a list of panels. NULL -#' for single-panel objects. -#' @param x An \code{LdData}. +#' @description Return the weights a mixture's components are combined +#' with: an \code{\link{LdMatrix}} mixture's (\code{NULL} for a single +#' panel's LD), or a \code{\link{GenotypeMixture}}'s, named after its +#' panels. +#' @param x An \code{LdMatrix} or a \code{GenotypeMixture}. #' @return Numeric vector or NULL. #' @examples #' data(eqtlRegionExample) #' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' mixtureWeights(ld) +#' half <- seq_len(nrow(X) / 2) +#' mix <- LdMatrix( +#' list(LdMatrix(cor(X[half, ])), LdMatrix(cor(X[-half, ]))), +#' mixtureWeights = c(0.75, 0.25)) +#' mixtureWeights(mix) #' @export setGeneric("mixtureWeights", function(x) { standardGeneric("mixtureWeights") }) -#' @title Get SNP Indices -#' @description Return the integer indices into the handle's snpInfo carried by -#' an \code{LdData}. -#' @param x An \code{LdData}. -#' @return Integer vector or NULL. -#' @examples -#' data(eqtlRegionExample) -#' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' snpIdx(ld) -#' @export -setGeneric("snpIdx", function(x) standardGeneric("snpIdx")) - -#' @title Get Variant GRanges -#' @description Return the variant metadata \code{GRanges} of an \code{LdData}. -#' @param x An \code{LdData}. -#' @return A \code{GRanges}. -#' @examples -#' data(eqtlRegionExample) -#' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' variantInfo(ld) -#' @export -setGeneric("variantInfo", function(x) standardGeneric("variantInfo")) - -#' @title Get Block Metadata -#' @description Return the block metadata (\code{GRanges} or \code{data.frame}) -#' carried by an \code{LdData}. -#' @param x An \code{LdData}. -#' @return A \code{GRanges} or \code{data.frame}. -#' @examples -#' data(eqtlRegionExample) -#' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' blockMetadata(ld) -#' @export -setGeneric("blockMetadata", function(x) standardGeneric("blockMetadata")) - -#' @title Get Reference Panel (data.frame) -#' @description Flatten the variant \code{GRanges} of an \code{LdData} into a -#' reference-panel data.frame. -#' @param x An \code{LdData}. -#' @return A data.frame. -#' @examples -#' data(eqtlRegionExample) -#' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' refPanel(ld) -#' @export -setGeneric("refPanel", function(x) standardGeneric("refPanel")) - #' @title Get Per-Block tau Matrix #' @description Return the per-block jackknife tau matrix carried by an #' \code{H2Estimate}. @@ -2283,7 +2234,7 @@ setGeneric("sldscResults", function(x, trait = NULL, ...) { #' the five posterior probabilities, i.e. colocalization is the favoured #' hypothesis rather than merely a probable one. #' @param minAbsCorr Minimum absolute correlation between credible-set members -#' (purity). Requires the object's \code{ldSketch}; when that is absent, +#' (purity). Requires the object's \code{ldReference}; when that is absent, #' purity is reported as \code{NA} and no set is dropped. #' @param ... Additional arguments passed on to methods. #' @return A tibble at the requested granularity. @@ -2403,7 +2354,7 @@ setGeneric("chromPaths", function(x, ...) standardGeneric("chromPaths")) #' Across entries with different effect counts the collection method NA-fills #' the ragged columns. #' -#' @param x A \code{FineMappingRow} or \code{FineMappingResultBase}. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param ... Ignored. #' @return A \code{tibble}: \code{variant_id} + \code{lbf_L1..lbf_LL} (the #' collection method also carries the entry identity columns). @@ -2421,7 +2372,7 @@ setGeneric("lbf", function(x, ...) standardGeneric("lbf")) #' per-variant \code{\link{credibleSets}}. Replaces the legacy #' per-effect `effect.tsv`. #' -#' @param x A \code{FineMappingRow} or \code{FineMappingResultBase}. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param coverage Credible-set coverage to summarise. Default 0.95. #' @param ... Ignored. #' @return A \code{tibble}: \code{cs, effect_id, coverage, n_variants, @@ -2450,7 +2401,7 @@ setGeneric("credibleSetSummary", function(x, ...) { #' them from an untrimmed fit. Returns zero rows when the functional fields #' are absent. #' -#' @param x A \code{FineMappingRow} or \code{FineMappingResultBase}. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param ... Ignored. #' @return A long \code{tibble}: \code{cs, chrom, pos, effect, lower, upper, #' halfwidth} -- \code{halfwidth} being \code{(upper - lower) / 2}, the @@ -2479,7 +2430,7 @@ setGeneric("fsusieCredibleBand", function(x, ...) { #' index-aligned because both describe the same joint group. Returns zero rows #' when the functional fields are absent. #' -#' @param x A \code{FineMappingRow} or \code{FineMappingResultBase}. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param ... Ignored. #' @return A long \code{tibble}: \code{cs, chrom, jointTrait, traitPos, #' effect} (the collection method additionally carries the entry identity @@ -2503,7 +2454,7 @@ setGeneric("fsusieTraitEffects", function(x, ...) { #' (\code{"pos"}/\code{"neg"}; upstream discards it). Uses retained functional #' summaries or an untrimmed fit; empty when those fields are absent. #' -#' @param x A \code{FineMappingRow} or \code{FineMappingResultBase}. +#' @param x A \code{QtlFineMappingResult} or \code{GwasFineMappingResult}. #' @param ... Ignored. #' @return A \code{GRanges} of affected intervals with mcols \code{cs}, #' \code{purity}, \code{direction} (plus entry identity for the collection). diff --git a/R/AnnotationMatrix.R b/R/AnnotationMatrix.R index af272e841..3f066e345 100644 --- a/R/AnnotationMatrix.R +++ b/R/AnnotationMatrix.R @@ -195,9 +195,9 @@ AnnotationMatrix <- function( # Annotations of one tier. A column subset: SummarizedExperiment keeps the # rows, the assay and the per-annotation table aligned, where the previous # implementation rebuilt the object from three separately-subset pieces. -# @noRd # Callers are methods dispatching on AnnotationMatrix, so the class is # already guaranteed here -- no assertClass. +# @noRd .annotTier <- function(x, tier) { x[, SummarizedExperiment::colData(x)$tier == tier] } diff --git a/R/ColocBoostResult.R b/R/ColocBoostResult.R index 181c947c2..3db3f54a9 100644 --- a/R/ColocBoostResult.R +++ b/R/ColocBoostResult.R @@ -23,7 +23,7 @@ # # What the two DO share is the LD panel they were computed against and the # projection surface, so both extend ColocResultBase -- which carries the -# ldSketch slot and its accessor -- and both answer colocPairs() and +# ldReference slot and its accessor -- and both answer colocPairs() and # colocVariants(). # # element the member variants of one CoS @@ -32,7 +32,7 @@ # CharacterList -- native variable arity), purity, cosNpc, # minNpcOutcome, topVariable, topVariableVcp, focalOutcome, # isColocalized -# slots ldSketch, outcomeInfo, regionVcp, computingTime +# slots ldReference, outcomeInfo, regionVcp, computingTime # # Uncolocalized (outcome-specific) sets are elements too, flagged # isColocalized = FALSE, rather than living in a second container: they have @@ -204,7 +204,7 @@ setMethod("show", "ColocBoostResult", function(object) { #' naming the GWAS study for the per-study \code{separate_gwas} runs. #' @param outcomeInfo Data frame mapping outcome \code{name} to \code{study}, #' \code{context}, \code{trait} and \code{dataForm}. -#' @param ldSketch Optional genotype panel (see +#' @param ldReference Optional genotype panel (see #' \code{\link{readGenotypes}}). #' @param computingTime Optional list of per-analysis timings. #' @param includeUncolocalized Keep the outcome-specific (uncolocalized) sets @@ -232,7 +232,7 @@ ColocBoostResult <- function( analysis, gwasStudy = NA_character_, outcomeInfo = NULL, - ldSketch = NULL, + ldReference = NULL, computingTime = list(), includeUncolocalized = TRUE ) { @@ -257,7 +257,7 @@ ColocBoostResult <- function( rows, outcomeInfo = as.data.frame(outcomeInfo), regionVcp = .cbrRegionVcp(results), - ldSketch = .asLdSketch(ldSketch), + ldReference = .asLdRef(ldReference), computingTime = computingTime ) } @@ -430,8 +430,6 @@ ColocBoostResult <- function( if (is.null(v) || length(v) == 0L) NA_real_ else as.numeric(v)[[1L]] } -# @noRd - # `FALSE` means "no focal outcome was set"; anything else is the trait name. # @noRd .cbrFocalOutcome <- function(v) { @@ -471,7 +469,7 @@ ColocBoostResult <- function( rows, outcomeInfo, regionVcp, - ldSketch, + ldReference, computingTime ) { elements <- map(rows, .cbrElementFor) @@ -482,7 +480,7 @@ ColocBoostResult <- function( obj <- new( "ColocBoostResult", grl, - ldSketch = .asLdSketch(ldSketch), + ldReference = .asLdRef(ldReference), outcomeInfo = outcomeInfo, regionVcp = regionVcp, computingTime = computingTime diff --git a/R/ColocResult.R b/R/ColocResult.R index 867e1640b..08188b439 100644 --- a/R/ColocResult.R +++ b/R/ColocResult.R @@ -17,7 +17,7 @@ # inner mcols per-variant: SNP.PP.H4 # outer mcols per-pair: identity tuple, blockId, PP.H0..PP.H4, nSnps, # retained alpha mass (both sides), enrichment / p12Used -# slots ldSketch, for the credible-set view's purity calculation +# slots ldReference, for the credible-set view's purity calculation # # Nothing is filtered at write time (spec 5.3): every testable pair and every # variant is kept, and all narrowing is a view decision. Purity in particular @@ -46,28 +46,29 @@ NULL #' projection surface: both answer \code{\link{colocPairs}} and #' \code{\link{colocVariants}}, so code that only needs "which variants #' colocalized, and how strongly" works against either. -#' @slot ldSketch The LD reference panel the underlying fits were computed -#' against, or \code{NULL}. Shared by both subclasses: coloc and ColocBoost -#' report different statistics, but both are computed against a panel and -#' both are asked for it the same way. +#' @slot ldReference The LD reference the underlying fits were computed +#' against (a genotype panel, a \code{GenotypeMixture} or an +#' \code{LdMatrix}), or \code{NULL}. Shared by both subclasses: coloc and +#' ColocBoost report different statistics, but both are computed against a +#' reference and both are asked for it the same way. #' @export setClass( "ColocResultBase", contains = c("VIRTUAL", "RangedTupleList"), - representation(ldSketch = "LdSketch_OR_NULL"), - prototype(ldSketch = NULL) + representation(ldReference = "LdReference_OR_NULL"), + prototype(ldReference = NULL) ) -#' @rdname ldSketch +#' @rdname ldReference #' @export -setMethod("ldSketch", "ColocResultBase", function(x) x@ldSketch) +setMethod("ldReference", "ColocResultBase", function(x) x@ldReference) #' @title Colocalization Result #' @description A collection of colocalization results, one element per tested #' (QTL credible set, GWAS credible set, block) pair. Build the four views #' with \code{\link{colocPairs}}, \code{\link{colocVariants}}, #' \code{\link{colocCredibleSets}} and \code{\link{colocGenes}}. -#' @details The inherited \code{ldSketch} is what +#' @details The inherited \code{ldReference} is what #' \code{colocCredibleSets()} uses to compute credible-set purity. #' @seealso \code{\link{colocPipeline}} #' @export @@ -197,7 +198,7 @@ setMethod("show", "ColocResult", function(object) { #' columns are \code{NA} for it. #' @param variants A list, parallel to \code{pairs}' rows, of per-pair data #' frames with a \code{variant_id} column and a \code{SNP.PP.H4} column. -#' @param ldSketch Optional genotype panel (see \code{\link{readGenotypes}}) +#' @param ldReference Optional genotype panel (see \code{\link{readGenotypes}}) #' for the LD reference, used by \code{\link{colocCredibleSets}} to #' compute purity. #' @return A \code{ColocResult}. @@ -217,7 +218,7 @@ setMethod("show", "ColocResult", function(object) { #' )) #' ColocResult(pairs, variants) #' @export -ColocResult <- function(pairs, variants, ldSketch = NULL) { +ColocResult <- function(pairs, variants, ldReference = NULL) { pairs <- as.data.frame(pairs, stringsAsFactors = FALSE) if (!is.list(variants) || length(variants) != nrow(pairs)) { msg <- glue( @@ -237,7 +238,7 @@ ColocResult <- function(pairs, variants, ldSketch = NULL) { !!!c(as.list(pairs), list(check.names = FALSE)) ) ) - obj <- new("ColocResult", grl, ldSketch = .asLdSketch(ldSketch)) + obj <- new("ColocResult", grl, ldReference = .asLdRef(ldReference)) validObject(obj) obj } @@ -461,7 +462,7 @@ setMethod( } out <- bind_rows(sets) .crApplyPurityFilter( - mutate(out, purity = .crPuritiesFor(out, ldSketch(x))), + mutate(out, purity = .crPuritiesFor(out, ldReference(x))), minAbsCorr ) } @@ -535,23 +536,23 @@ setMethod( # one LD extraction per set (section 3.7). NA when there is no LD reference to # extract from -- reported honestly rather than defaulted to a passing value. # @noRd -.crPuritiesFor <- function(out, ldSketch) { - if (is.null(ldSketch)) { +.crPuritiesFor <- function(out, ldReference) { + if (is.null(ldReference)) { return(rep(NA_real_, nrow(out))) } - map_dbl(out$csVariants, .crPurityOne, ldSketch = ldSketch) + map_dbl(out$csVariants, .crPurityOne, ldReference = ldReference) } # @noRd #' @importFrom rlang try_fetch -.crPurityOne <- function(ids, ldSketch) { +.crPurityOne <- function(ids, ldReference) { # A singleton set has no pair to correlate, and susie treats it as pure. if (length(ids) < 2L) { return(1) } ld <- try_fetch( - .ldFromSketch( - ldSketch, + .ldFromRef( + ldReference, ids, label = "colocCredibleSets", onMissing = "drop" diff --git a/R/CtwasResult.R b/R/CtwasResult.R index 59bcfdb3c..cbe7b156e 100644 --- a/R/CtwasResult.R +++ b/R/CtwasResult.R @@ -8,7 +8,7 @@ # run, so genes live inside the payload. Optional joint columns (jointStudies, # jointContexts) tag rows born from a multi-study / multi-context cTWAS run and # participate in the uniqueness key, exactly as in the TwasWeights / -# FineMappingResult families. +# FineMappingResultBase families. # ============================================================================= #' @include AllGenerics.R tupleSelectors.R diff --git a/R/GenotypeDosageSeed.R b/R/GenotypeDosageSeed.R new file mode 100644 index 000000000..1cb587989 --- /dev/null +++ b/R/GenotypeDosageSeed.R @@ -0,0 +1,383 @@ +# ============================================================================= +# GenotypeDosageSeed -- a DelayedArray backend over a VCF, GDS or PLINK1 file +# ----------------------------------------------------------------------------- +# A VCF, GDS or PLINK1 panel reads its dosages through this seed, as a PLINK2 +# or BGEN panel reads through a GenotypeHandle's GhSeed. Each row is one +# variant: one ALT allele of one record of the file, so a record with several +# ALT alleles is read as one variant per ALT allele. The seed knows where each +# row's record is in the file and which allele the row counts, and turns what +# the file stores into that allele's dosage. What it reads is its `field`: +# +# vcfGt a VCF's GT calls, through VariantAnnotation +# vcfDs a VCF's FORMAT/DS values, through VariantAnnotation +# snpGdsSnpOrder a SNPRelate GDS's genotype codes, stored SNP by SNP +# snpGdsSampleOrder or sample by sample, through GDSArray +# seqArrayGt a SeqArray GDS's allele indices, through GDSArray +# seqArrayDs a SeqArray GDS's annotation/format/DS, through GDSArray +# plinkBed a PLINK1 .bed's genotype codes, through PlinkMatrix +# +# VCFArray's seed is not built on. It reads a row through every record that +# overlaps the row's range, so in a VCF whose records overlap -- a split +# multi-allelic site, a deletion spanning a SNP -- it returns more rows than +# were asked for and DelayedArray rejects the block. The VCF fields are read +# through VariantAnnotation, as VCFArray reads them, and each record is +# picked out of what its range returns. +# +# Orientation: variants x samples, as for GhSeed. +# ============================================================================= + +#' @include AllGenerics.R +NULL + +#' DelayedArray seed over a VCF, GDS or PLINK1 file +#' +#' Lets the dosages of a VCF, GDS or PLINK1 file back a \code{DelayedArray} +#' that reads the file a block at a time, as \code{GhSeed} does for the +#' formats a \code{GenotypeHandle} reads. +#' +#' @slot backend What the file is read through: a +#' \code{VariantAnnotation::VcfFile}, the \code{GDSArray} seed of the +#' GDS node holding the field, or the stem of a PLINK1 fileset, which +#' \code{PlinkMatrix::read_bed_subset()} reads. +#' @slot field Which field is read, and so how it becomes a dosage: +#' \code{"vcfGt"}, \code{"vcfDs"}, \code{"snpGdsSnpOrder"}, +#' \code{"snpGdsSampleOrder"}, \code{"seqArrayGt"}, +#' \code{"seqArrayDs"} or \code{"plinkBed"}. +#' @slot rowRecord Integer; for each row, the element of \code{records} it +#' reads. +#' @slot rowAllele Integer; for each row, the ALT allele it counts, 1 for a +#' record's first ALT allele. +#' @slot records Where each record is in the file. For a VCF, a +#' \code{GRanges} of each record's REF allele, with \code{posRank}, the +#' record's rank among those starting at its position; for a GDS, an +#' \code{IRanges} of the columns (or SeqArray's 2-bit planes) of the node +#' holding it; for a PLINK1 fileset, an \code{IRanges} of each variant's +#' position in the \code{.bed}. +#' @slot dn List of dimnames (variant ids, sample ids). +#' @name GenotypeDosageSeed-class +#' @keywords internal +#' @importClassesFrom S4Vectors Vector +#' @importFrom BiocGenerics width +setClass( + "GenotypeDosageSeed", + representation( + backend = "ANY", + field = "character", + rowRecord = "integer", + rowAllele = "integer", + records = "Vector", + dn = "list" + ) +) + +#' @rdname GenotypeDosageSeed-class +setMethod("dim", "GenotypeDosageSeed", function(x) { + c(length(x@rowRecord), length(x@dn[[2L]])) +}) + +#' @rdname GenotypeDosageSeed-class +setMethod("dimnames", "GenotypeDosageSeed", function(x) x@dn) + +#' @rdname GenotypeDosageSeed-class +#' @importFrom S4Arrays extract_array +setMethod("extract_array", "GenotypeDosageSeed", function(x, index) { + # A NULL index means everything along that dimension, as for GhSeed. + dm <- dim(x) + i <- index[[1L]] %||% seq_len(dm[[1L]]) + j <- index[[2L]] %||% seq_len(dm[[2L]]) + if (length(i) == 0L || length(j) == 0L) { + return(matrix(numeric(0), length(i), length(j))) + } + # Each distinct row and sample is read once, in file order, and the block + # then laid out as asked: DelayedArray may ask in any order, and repeat. + rows <- sort(unique(i)) + samples <- sort(unique(j)) + block <- .gdSeedRead(x, rows, samples) + block[match(i, rows), match(j, samples), drop = FALSE] +}) + +# Internal accessors: GenotypeDosageSeed is not exported, so these add no +# public surface, and they keep the `@` next to the class. +# @noRd +.gdSeedBackend <- function(x) x@backend + +# @noRd +.gdSeedField <- function(x) x@field + +# @noRd +.gdSeedRowRecord <- function(x) x@rowRecord + +# @noRd +.gdSeedRowAllele <- function(x) x@rowAllele + +# @noRd +.gdSeedRecords <- function(x) x@records + +# The dosages of rows `rows` for samples `samples`, both ascending and +# distinct: a length(rows) x length(samples) matrix. A GDS seed's backend is +# GDSArray's, whose methods are there only once its namespace is loaded, +# which reading a saved panel back does not do. +# @noRd +.gdSeedRead <- function(x, rows, samples) { + if (str_starts(.gdSeedField(x), "snpGds|seqArray")) { + loadNamespace("GDSArray") + } + switch( + .gdSeedField(x), + vcfGt = , + vcfDs = .vcfSeedRead(x, rows, samples), + snpGdsSnpOrder = , + snpGdsSampleOrder = .snpGdsSeedRead(x, rows, samples), + seqArrayGt = .seqArrayGtSeedRead(x, rows, samples), + seqArrayDs = .seqArrayDsSeedRead(x, rows, samples), + plinkBed = .plinkBedSeedRead(x, rows, samples) + ) +} + +# The seed cut to rows `keep` (ascending positions), carrying only the +# records those rows read, so a panel cut to a few variants does not carry +# every record of the file. +# @noRd +.gdSeedRows <- function(seed, keep) { + rowRecord <- .gdSeedRowRecord(seed)[keep] + used <- sort(unique(rowRecord)) + methods::initialize( + seed, + rowRecord = match(rowRecord, used), + rowAllele = .gdSeedRowAllele(seed)[keep], + records = .gdSeedRecords(seed)[used], + dn = list(dimnames(seed)[[1L]][keep], dimnames(seed)[[2L]]) + ) +} + +# ---- VCF ------------------------------------------------------------------- + +# The VCF FORMAT field a seed reads. +# @noRd +.vcfSeedFieldName <- function(x) { + if (.gdSeedField(x) == "vcfGt") "GT" else "DS" +} + +# A VCF seed's rows: the records they read are fetched by their ranges, each +# picked out of what its range returns, and the field turned into the +# dosage of each row's ALT allele. +# @noRd +.vcfSeedRead <- function(x, rows, samples) { + recordOfRow <- .gdSeedRowRecord(x)[rows] + wanted <- sort(unique(recordOfRow)) + spans <- .gdSeedRecords(x)[wanted] + queryRanges <- IRanges::reduce(spans) + queryRanges <- `names<-`( + queryRanges, + as.character(seq_along(queryRanges)) + ) + sampleIds <- dimnames(x)[[2L]][samples] + vcf <- VariantAnnotation::readVcf( + .vcfSeedFile(x), + param = VariantAnnotation::ScanVcfParam( + fixed = NA, + info = NA, + geno = .vcfSeedFieldName(x), + samples = sampleIds, + which = queryRanges + ) + ) + returned <- .vcfMatchRecords(spans, queryRanges, vcf, x) + values <- VariantAnnotation::geno(vcf)[[.vcfSeedFieldName(x)]] + picked <- values[ + returned[match(recordOfRow, wanted)], + match(sampleIds, colnames(values)), + drop = FALSE + ] + alleles <- .gdSeedRowAllele(x)[rows] + if (.gdSeedField(x) == "vcfGt") { + return(vcfGtDosageCpp(picked, alleles)) + } + .vcfDsDosage(picked, alleles) +} + +# A fresh VcfFile over the seed's file and index. The one the seed holds is +# not read through directly: one saved with the panel and loaded again would +# carry a stale connection. +# @noRd +.vcfSeedFile <- function(x) { + file <- .gdSeedBackend(x) + VariantAnnotation::VcfFile(path(file), index = Rsamtools::index(file)) +} + +# Which row of `vcf` holds each record of `spans`. +# +# A range returns every record overlapping it, so a record is not simply the +# n-th row returned. Each record lies in exactly one of the disjoint +# `queryRanges`, and that range returns every record starting at the +# record's position, in file order; so the record is the row from its range +# that starts at its position with its rank there, which `posRank` recorded +# when the panel was read. Matching on integers keeps the seed free of a +# string per record. +# @noRd +.vcfMatchRecords <- function(spans, queryRanges, vcf, x) { + got <- SummarizedExperiment::rowRanges(vcf) + have <- tibble( + range = as.integer(as.character(got$paramRangeID)), + start = as.integer(start(got)) + ) |> + mutate( + rank = row_number(), + .by = all_of(c("range", "start")) + ) + want <- tibble( + range = findOverlaps(spans, queryRanges, select = "first"), + start = as.integer(start(spans)), + rank = as.integer(S4Vectors::mcols(spans)$posRank) + ) + hit <- vctrs::vec_match(want, have) + if (anyNA(hit)) { + abort(c( + glue( + "{path(.gdSeedBackend(x))} no longer holds the records the ", + "panel read from it." + ), + i = "Read it again with readGenotypes()." + )) + } + hit +} + +# The dosage of each row's ALT allele from a block of FORMAT/DS values: one +# value per ALT allele (Number=A, list cells), or one per record (Number=1), +# which is then its first ALT allele's. A row asking for an allele the +# record holds no value for is missing. +# @noRd +.vcfDsDosage <- function(values, alleles) { + if (!is.list(values)) { + return(unname(`[<-`(values, alleles != 1L, , value = NA_real_))) + } + # A list keeps its dim through as.vector(); drop it to walk the cells. + cells <- `dim<-`(values, NULL) + nValues <- lengths(cells) + allele <- rep(alleles, times = ncol(values)) + held <- allele <= nValues + dosage <- rep(NA_real_, length(cells)) + if (any(held)) { + flat <- as.numeric(list_c(cells)) + at <- cumsum(nValues) - nValues + allele + dosage <- replace(dosage, held, flat[at[held]]) + } + matrix(dosage, nrow = nrow(values)) +} + +# ---- SNPRelate GDS --------------------------------------------------------- + +# A SNPRelate GDS stores genotype codes 0-2 counting the first of a SNP's two +# alleles, which is therefore A1, and 3 for a missing call. +# @noRd +.snpGdsSeedRead <- function(x, rows, samples) { + variant <- start(.gdSeedRecords(x))[.gdSeedRowRecord(x)[rows]] + codes <- if (.gdSeedField(x) == "snpGdsSnpOrder") { + extract_array(.gdSeedBackend(x), list(variant, samples)) + } else { + t(extract_array(.gdSeedBackend(x), list(samples, variant))) + } + replace(`storage.mode<-`(codes, "double"), codes == 3L, NA_real_) +} + +# ---- SeqArray GDS ---------------------------------------------------------- + +# A SeqArray GDS stores each call's allele indices (0 for REF, k for the +# k-th ALT allele) in 2-bit planes, ploidy x samples x planes: one plane per +# record, or more for a record with too many alleles for one, its index then +# the planes' sum weighted by 1, 4, 16, .... A missing allele sets every bit +# of the record's planes. A call with a missing allele is missing, as +# SeqArray's own dosages read it. +# @noRd +.seqArrayGtSeedRead <- function(x, rows, samples) { + recordOfRow <- .gdSeedRowRecord(x)[rows] + wanted <- sort(unique(recordOfRow)) + planes <- .gdSeedRecords(x)[wanted] + bits <- extract_array( + .gdSeedBackend(x), + list(NULL, samples, sequence(width(planes), start(planes))) + ) + last <- cumsum(width(planes)) + calls <- map2( + last - width(planes) + 1L, + width(planes), + .seqArrayRecordCalls, + bits = bits + ) + copies <- map2( + calls[match(recordOfRow, wanted)], + .gdSeedRowAllele(x)[rows], + .seqArrayCopies + ) + matrix(list_c(copies), nrow = length(rows), byrow = TRUE) +} + +# One record's allele indices, ploidy x samples, from its `n` planes starting +# at plane `first` of `bits`; NA where an allele is missing. +# @noRd +.seqArrayRecordCalls <- function(first, n, bits) { + weighted <- map( + seq_len(n), + .seqArrayPlaneValue, + first = first, + bits = bits + ) + value <- reduce(weighted, `+`) + replace(value, value == 4^n - 1, NA) +} + +# Plane `k` of a record whose planes start at `first`, weighted by its place. +# @noRd +.seqArrayPlaneValue <- function(k, first, bits) { + plane <- matrix(bits[,, first + k - 1L], nrow = dim(bits)[[1L]]) + plane * 4^(k - 1L) +} + +# The copies of ALT allele `allele` in each sample's call; NA for a call with +# a missing allele. +# @noRd +.seqArrayCopies <- function(calls, allele) { + colSums(calls == allele) +} + +# A SeqArray GDS stores FORMAT/DS as samples x values, each record's values +# in consecutive columns: one per ALT allele (Number=A), or one (Number=1), +# which is then its first ALT allele's. A missing value is stored as NaN. +# @noRd +.seqArrayDsSeedRead <- function(x, rows, samples) { + spans <- .gdSeedRecords(x)[.gdSeedRowRecord(x)[rows]] + alleles <- .gdSeedRowAllele(x)[rows] + column <- start(spans) + alleles - 1L + held <- alleles <= width(spans) + missing <- matrix(NA_real_, length(rows), length(samples)) + if (!any(held)) { + return(missing) + } + columns <- sort(unique(column[held])) + values <- t(extract_array(.gdSeedBackend(x), list(samples, columns))) + dosage <- `[<-`( + missing, + held, + , + value = values[match(column[held], columns), , drop = FALSE] + ) + replace(dosage, is.nan(dosage), NA_real_) +} + +# ---- PLINK1 ---------------------------------------------------------------- + +# A PLINK1 .bed stores 2-bit genotype codes SNP by SNP. PlinkMatrix reads them +# as the copies of each variant's A1 allele -- the .bim's fifth column, as +# pecotmr counts it -- with NA for a missing call. It is not told the sample +# count: it counts the .fam itself, and a wrong count would misread silently. +# The stem may be a bundled example's portable reference, resolved here. +# @noRd +.plinkBedSeedRead <- function(x, rows, samples) { + variant <- start(.gdSeedRecords(x))[.gdSeedRowRecord(x)[rows]] + PlinkMatrix::read_bed_subset( + .resolveGenotypeResourcePath(.gdSeedBackend(x)), + variant, + samples + ) +} diff --git a/R/GenotypeHandle.R b/R/GenotypeHandle.R index c424eb36c..133fe4418 100644 --- a/R/GenotypeHandle.R +++ b/R/GenotypeHandle.R @@ -1,9 +1,11 @@ # ============================================================================= # GenotypeHandle S4 class # ----------------------------------------------------------------------------- -# Lazy file handle for genotype data (PLINK1, PLINK2, VCF/BCF, GDS). Opens -# the file for metadata only (sample IDs and SNP info); dosage extraction -# is deferred until extractBlockGenotypes() is called. +# Lazy file handle for genotype data (PLINK2, BGEN). Opens the file for +# metadata only (sample IDs and SNP info); dosage extraction is deferred until +# extractBlockGenotypes() is called. A VCF, GDS or PLINK1 file is read as a +# panel directly, through a GenotypeDosageSeed (genotypeIo.R), not through a +# handle. # ============================================================================= #' @include AllGenerics.R @@ -11,13 +13,13 @@ NULL #' @title Genotype File Handle #' @description S4 container holding a path + format + metadata for lazy -#' genotype access. Supports PLINK1 (.bed/.bim/.fam), PLINK2 -#' (.pgen/.pvar/.psam), VCF/BCF, and GDS. +#' genotype access. Supports PLINK2 (.pgen/.pvar/.psam) and BGEN (.bgen +#' with its .bgi index); a VCF, GDS or PLINK1 file is read as a panel by +#' \code{readGenotypes()} without one. #' @slot path Character, file path. For a one-file-per-chromosome handle this is #' the chrom-meta file path (a display/provenance value); the per-chromosome #' payload files live in \code{chromPaths}. -#' @slot format Character, one of \code{"plink1"}, \code{"plink2"}, -#' \code{"vcf"}, \code{"gds"}. +#' @slot format Character, \code{"plink2"} or \code{"bgen"}. #' @slot snpInfo data.frame, SNP metadata read from the index/sidecar. For a #' sharded handle this is the union across chromosomes (row-bound in the order #' the shards were supplied), so \code{snpIdx} stays a single global index @@ -49,7 +51,7 @@ setClass( chromPaths = character(0) ), validity = function(object) { - validFormats <- c("gds", "vcf", "plink1", "plink2") + validFormats <- c("plink2", "bgen") nm <- names(object@chromPaths) badChromPaths <- length(object@chromPaths) > 0L && (is.null(nm) || any(str_length(nm) == 0L) || anyDuplicated(nm)) @@ -100,54 +102,51 @@ setMethod("show", "GenotypeHandle", function(object) { #' forms. Exactly one of the following must be specified: #' \describe{ #' \item{\code{path}}{A single file path with a recognized extension: -#' \code{.vcf}, \code{.vcf.gz}, \code{.vcf.bgz}, \code{.bcf}, or -#' \code{.gds}. Format is auto-detected from the extension.} -#' \item{\code{plink1Prefix}}{A path prefix; the constructor appends -#' \code{.bed}, \code{.bim}, and \code{.fam} to locate the triplet.} +#' \code{.bgen} or \code{.pgen}. Format is auto-detected from the +#' extension. A BGEN file needs its \code{.bgi} index beside it, and +#' its sample ids in the file or in a \code{.sample} file beside it. A +#' VCF, GDS or PLINK1 file is refused: it is read as a panel by +#' \code{readGenotypes()}.} #' \item{\code{plink2Prefix}}{A path prefix; the constructor appends #' \code{.pgen}, \code{.pvar} (or \code{.pvar.zst}), and \code{.psam} #' to locate the triplet.} -#' \item{\code{bed} + \code{bim} + \code{fam}}{Explicit PLINK1 triplet. -#' The three files must share a stem; if they don't, use -#' \code{plink1Prefix} or arrange symlinks at a common stem.} #' \item{\code{pgen} + \code{pvar} + \code{psam}}{Explicit PLINK2 -#' triplet. Same shared-stem requirement.} +#' triplet. The three files must share a stem; if they don't, use +#' \code{plink2Prefix} or arrange symlinks at a common stem.} #' \item{\code{genoMeta}}{One genotype file per chromosome. Either a path #' to a whitespace/TSV meta file whose first column is the chromosome #' (\code{#chr}) and second column the per-chromosome payload -#' (a \code{.bed}/\code{.pgen}/\code{.vcf[.gz]}/\code{.bcf}/\code{.gds} -#' file or a PLINK prefix; relative paths resolve against the meta -#' file's directory), or a named character vector mapping chromosome to -#' payload. All shards must share one format and identical sample IDs in -#' the same order. Extraction is routed to the correct file by the -#' requested region's chromosome.} +#' (a \code{.pgen}/\code{.bgen} file or a PLINK2 prefix; +#' relative paths resolve against the meta file's directory), or a +#' named character vector mapping chromosome to payload. All shards +#' must share one format and identical sample IDs in the same order. +#' Extraction is routed to the correct file by the requested region's +#' chromosome.} #' } #' The constructor opens the file for metadata only (sample IDs and SNP #' info); dosage extraction is deferred until \code{extractBlockGenotypes()} #' is called. #' -#' @param path Single file path (.vcf/.vcf.gz/.vcf.bgz/.bcf/.gds), or -#' \code{NULL}. -#' @param plink1Prefix Path prefix for a PLINK1 triplet, or \code{NULL}. +#' @param path Single file path (.bgen/.pgen), or \code{NULL}. #' @param plink2Prefix Path prefix for a PLINK2 triplet, or \code{NULL}. -#' @param bed,bim,fam Explicit paths to the PLINK1 triplet, or all \code{NULL}. #' @param pgen,pvar,psam Explicit paths to the PLINK2 triplet, or all #' \code{NULL}. #' @param ldMeta Path to an LD-meta TSV file (columns \code{chrom}, #' \code{start}, \code{end}, \code{path}; the \code{path} column may be #' comma-separated as \code{ld_file,bim_file}). Requires \code{region}. The #' constructor resolves the row covering \code{region}, then delegates to the -#' appropriate file-based handler. When the resolved row points at PLINK1 / -#' PLINK2 / VCF / GDS files, the corresponding reader is used; \code{.cor.xz} -#' (pre-computed LD-matrix) rows are not supported here -- use -#' \code{\link{loadLdMatrix}} for that case. +#' appropriate file-based handler. When the resolved row points at PLINK2 +#' or BGEN files, the corresponding reader is used; a row pointing +#' at a pre-computed LD matrix (\code{.cor.xz}) is refused, as pecotmr reads +#' no pre-computed LD from disk -- wrap such a matrix in an +#' \code{\link{LdMatrix}} instead. #' @param region Region specification for \code{ldMeta} lookup: #' \code{"chr:start-end"} string or a one-row data.frame with \code{chrom}, #' \code{start}, \code{end}. #' @param genoMeta One-file-per-chromosome specification: a path to a #' \code{#chr,path} meta file or a named character vector (names = #' chromosomes, values = payload paths/prefixes). Optionally pass -#' \code{format} via \code{...} to force a single backend for every shard. +#' \code{format} to force a single backend for every shard. #' @param chroms Optional character vector of chromosomes. With \code{genoMeta} #' (a sharded, one-file-per-chromosome panel) only the shards whose chromosome #' is listed are read, skipping the rest -- an I/O optimisation when the panel @@ -157,18 +156,15 @@ setMethod("show", "GenotypeHandle", function(object) { #' shard is read (so the caller's own absence check can report the mismatch). #' Only meaningful with \code{genoMeta}; supplying it with any other source is #' an error (a single-file panel has no per-chromosome shards to skip). -#' @param vcfArgs Optional named list of arguments forwarded to -#' \code{VariantAnnotation::readVcf} when the source is a VCF. +#' @param format Character, \code{"plink2"} or \code{"bgen"}, forcing the +#' backend of \code{path} or of every \code{genoMeta} shard. If NULL, +#' inferred from the file extension. #' @return A \code{GenotypeHandle} object. #' @keywords internal #' @rdname GenotypeHandle-class GenotypeHandle <- function( path = NULL, - plink1Prefix = NULL, plink2Prefix = NULL, - bed = NULL, - bim = NULL, - fam = NULL, pgen = NULL, pvar = NULL, psam = NULL, @@ -176,13 +172,12 @@ GenotypeHandle <- function( region = NULL, genoMeta = NULL, chroms = NULL, - format = NULL, - vcfArgs = list() + format = NULL ) { flags <- .ghValidateArgs( - bed = bed, - bim = bim, - fam = fam, + bed = NULL, + bim = NULL, + fam = NULL, pgen = pgen, pvar = pvar, psam = psam, @@ -191,28 +186,20 @@ GenotypeHandle <- function( ) sources <- .ghResolveSources( path = path, - plink1Prefix = plink1Prefix, + plink1Prefix = NULL, plink2Prefix = plink2Prefix, ldMeta = ldMeta, genoMeta = genoMeta, chroms = chroms, flags = flags ) - # .ghResolveSources() has already enforced that exactly one is TRUE, so - # the chosen name is well defined and the switch is total. switch( names(sources)[which(sources)], - path = .readGenotypeHandle(path, format = format, vcfArgs = vcfArgs), - plink1Prefix = .makePlink1Handle(plink1Prefix), + path = .readGenotypeHandle(path, format = format), plink2Prefix = .makePlink2Handle(plink2Prefix), - plink1Triplet = .genotypeHandleFromPlink1Triplet(bed, bim, fam), plink2Triplet = .genotypeHandleFromPlink2Triplet(pgen, pvar, psam), - ldMeta = .genotypeHandleFromLdMeta(ldMeta, region, vcfArgs = vcfArgs), - genoMeta = .genotypeHandleFromChromMeta( - genoMeta, - chroms = chroms, - format = format - ) + ldMeta = .genotypeHandleFromLdMeta(ldMeta, region), + genoMeta = .genotypeHandleFromChromMeta(genoMeta, chroms, format) ) } @@ -250,7 +237,8 @@ GenotypeHandle <- function( } # Build the exactly-one-source indicator vector; error unless exactly one input -# source was supplied, and gate `chroms` to the genoMeta path. +# source was supplied, and gate `chroms` to the genoMeta path. Because exactly +# one entry is TRUE, GenotypeHandle()'s switch on its name is total. # @noRd .ghResolveSources <- function( path, @@ -290,9 +278,8 @@ GenotypeHandle <- function( sources } -.genotypeHandleFromLdMeta <- function(ldMeta, region, vcfArgs = list()) { - ldPath <- .ghResolveLdPath(ldMeta, region) - .ghLdPathToHandle(ldPath, vcfArgs = vcfArgs) +.genotypeHandleFromLdMeta <- function(ldMeta, region) { + .ghLdPathToHandle(.ghResolveLdPath(ldMeta, region)) } # Resolve the single genotype-payload path for `region` from an LD meta, @@ -303,7 +290,7 @@ GenotypeHandle <- function( if (length(ldPaths) == 0L) { regionStr <- deparse(region) msg <- glue( - "GenotypeHandle: no LD-meta row covers region {regionStr} ", + "ldMeta: no LD-meta row covers region {regionStr} ", "in {ldMeta}." ) abort(msg) @@ -311,10 +298,9 @@ GenotypeHandle <- function( if (length(ldPaths) > 1L) { regionStr <- deparse(region) msg <- glue( - "GenotypeHandle: region {regionStr} spans multiple LD-meta ", - "rows; the GenotypeHandle constructor only resolves single-row ", - "regions. Use loadLdMatrix() for multi-row regions, or restrict ", - "the region to a single LD block." + "ldMeta: region {regionStr} spans multiple LD-meta rows; ", + "only a region within one row is resolved. Restrict the region ", + "to a single LD block." ) abort(msg) } @@ -322,53 +308,49 @@ GenotypeHandle <- function( if (str_detect(ldPath, regex("\\.cor(\\.xz)?$", ignore_case = TRUE))) { regionStr <- deparse(region) msg <- glue( - "GenotypeHandle: the LD-meta row for region {regionStr} points ", - "at a pre-computed correlation matrix ({ldPath}). Use ", - "loadLdMatrix() for .cor.xz inputs; ", - "GenotypeHandle accepts only genotype payloads (VCF/GDS/PLINK)." + "ldMeta: the LD-meta row for region {regionStr} points at a ", + "pre-computed correlation matrix ({ldPath}). pecotmr does not ", + "read pre-computed LD from disk; wrap the matrix in an LdMatrix ", + "instead. An LD-meta row names genotypes (VCF/GDS/BGEN/PLINK)." ) abort(msg) } ldPath } -# Dispatch a resolved genotype path to the format-specific reader by extension. +# The handle of the genotype file or PLINK prefix an LD-meta row names, for a +# format a GenotypeHandle reads. # @noRd -.ghLdPathToHandle <- function(ldPath, vcfArgs = list()) { - lower <- str_to_lower(ldPath) - if (str_detect(lower, "\\.vcf(\\.b?gz)?$") || str_ends(lower, "\\.bcf")) { - return(.readGenotypeHandle(ldPath, format = "vcf", vcfArgs = vcfArgs)) - } - if (str_ends(lower, "\\.gds")) { - return(.readGenotypeHandle(ldPath, format = "gds")) - } - if (str_ends(lower, "\\.bed")) { - return(.makePlink1Handle( - str_remove(ldPath, regex("\\.bed$", ignore_case = TRUE)) - )) - } - if (str_ends(lower, "\\.pgen")) { - return(.makePlink2Handle( - str_remove(ldPath, regex("\\.pgen$", ignore_case = TRUE)) - )) - } - msg <- glue( - "GenotypeHandle: unsupported LD-meta file extension on ", - "'{ldPath}'. Expected one of ", - ".vcf/.vcf.gz/.vcf.bgz/.bcf/.gds/.bed/.pgen." +.ghLdPathToHandle <- function(ldPath) { + .readGenotypeHandle( + ldPath, + format = .requireGenotypeFormat(ldPath, "ldMeta") + ) +} + +# The panel of the genotype file or PLINK prefix an LD-meta row names, in any +# format. +# @noRd +.ghLdPathToPanel <- function(ldPath, vcfArgs = list()) { + .readGenotypePanel( + ldPath, + format = .requireGenotypeFormat(ldPath, "ldMeta"), + vcfArgs = vcfArgs ) - abort(msg) } +# The stem the three files of a PLINK1 triplet share, which is how its panel +# is read. +# @noRd #' @importFrom checkmate assertString -.genotypeHandleFromPlink1Triplet <- function(bed, bim, fam) { +.plink1TripletStem <- function(bed, bim, fam) { assertString(bed) assertString(bim) assertString(fam) stems <- c( - bed = file_path_sans_ext(bed), - bim = file_path_sans_ext(bim), - fam = file_path_sans_ext(fam) + bed = .genotypeStem(bed), + bim = .genotypeStem(bim), + fam = .genotypeStem(fam) ) if (n_distinct(stems) != 1L) { stemList <- str_c(" ", names(stems), ": ", stems, collapse = "\n") @@ -382,7 +364,7 @@ GenotypeHandle <- function( ) abort(msg) } - .makePlink1Handle(unname(stems[1L])) + unname(stems[1L]) } .genotypeHandleFromPlink2Triplet <- function(pgen, pvar, psam) { @@ -391,11 +373,10 @@ GenotypeHandle <- function( abort("Each of `pgen`, `pvar`, `psam` must be a single file path.") } } - pvarStem <- str_remove(pvar, regex("\\.zst$", ignore_case = TRUE)) stems <- c( - pgen = file_path_sans_ext(pgen), - pvar = file_path_sans_ext(pvarStem), - psam = file_path_sans_ext(psam) + pgen = .genotypeStem(pgen), + pvar = .genotypeStem(pvar), + psam = .genotypeStem(psam) ) if (n_distinct(stems) != 1L) { stemList <- str_c(" ", names(stems), ": ", stems, collapse = "\n") @@ -447,7 +428,7 @@ GenotypeHandle <- function( if (length(multi) > 0L) { multiStr <- str_flatten(multi, ", ") msg <- glue( - "GenotypeHandle(genoMeta): chromosome(s) {multiStr} map to ", + "genoMeta: chromosome(s) {multiStr} map to ", "multiple genotype payloads; each chromosome must map to ", "exactly one payload (no sub-chromosomal blocks)." ) @@ -480,7 +461,7 @@ GenotypeHandle <- function( ))) } msg <- glue( - "GenotypeHandle(genoMeta): expected a path to a `#chr,path` meta ", + "genoMeta: expected a path to a `#chr,path` meta ", "file or a named character vector (names = chromosomes, ", "values = paths)." ) @@ -508,7 +489,7 @@ GenotypeHandle <- function( ) if (ncol(meta) < 2L) { msg <- glue( - "GenotypeHandle(genoMeta): meta file '{genoMeta}' must have ", + "genoMeta: meta file '{genoMeta}' must have ", "at least 2 columns (chromosome, path)." ) abort(msg) @@ -536,51 +517,50 @@ GenotypeHandle <- function( map_chr(pth, .ghResolvePath, base = base) } -# Build a single-file GenotypeHandle for one shard payload, dispatching to the -# right reader. `format` (optional) forces a backend; otherwise it is detected -# from the file extension, falling back to PLINK prefix probing. +# The format of one shard payload: `format` when it is forced, else told by +# the file extension, falling back to probing a PLINK prefix's sidecars. +# Nothing is read, so a set's shards can be checked to share one format +# before any of them is. +# @noRd +.shardFormat <- function(p, format = NULL) { + format %||% .requireGenotypeFormat(p, "genoMeta") +} + +# Build a single-file GenotypeHandle for one shard payload, of a format a +# handle reads. `format` (optional) forces a backend; otherwise it is detected +# as .shardFormat() detects it. #' @keywords internal .resolveGenotypeShard <- function(p, format = NULL) { - lower <- str_to_lower(p) - if (!is.null(format)) { - if (format == "plink1") { - return(.makePlink1Handle(p)) - } - if (format == "plink2") { - return(.makePlink2Handle(p)) - } - return(.readGenotypeHandle(p, format = format)) - } - if (str_detect(lower, "\\.vcf(\\.b?gz)?$") || str_ends(lower, "\\.bcf")) { - return(.readGenotypeHandle(p, format = "vcf")) - } - if (str_ends(lower, "\\.gds")) { - return(.readGenotypeHandle(p, format = "gds")) - } - if (str_ends(lower, "\\.bed")) { - return(.makePlink1Handle( - str_remove(p, regex("\\.bed$", ignore_case = TRUE)) - )) + .readGenotypeHandle(p, format = .shardFormat(p, format)) +} + +# The panel of a per-chromosome meta. A VCF, GDS or PLINK1 set is its +# shards' panels stacked (.stackDosagePanels()); a set in a format a handle +# reads, one sharded handle's panel. `chroms` (optional) restricts the read +# to the shards for those chromosomes, as for .genotypeHandleFromChromMeta(). +# @noRd +.chromMetaPanel <- function(genoMeta, chroms = NULL, format = NULL) { + parsed <- .parseChromMeta(genoMeta) + if (nrow(parsed) == 0L) { + abort( + "genoMeta: no chromosomes found in the meta input." + ) } - if (str_ends(lower, "\\.pgen")) { - return(.makePlink2Handle( - str_remove(p, regex("\\.pgen$", ignore_case = TRUE)) + selected <- .chromMetaSelect(parsed, chroms) + formats <- map_chr(selected$path, .shardFormat, format = format) + shared <- .chromMetaCheckFormats(formats) + if (!is_in(shared, .DOSAGE_PANEL_FORMATS)) { + return(.genotypeExperiment( + .genotypeHandleFromChromMeta(genoMeta, chroms, format) )) } - # No recognized extension: treat as a PLINK prefix, probe for the sidecar. - if (file.exists(str_c(p, ".bed"))) { - return(.makePlink1Handle(p)) - } - if (file.exists(str_c(p, ".pgen"))) { - return(.makePlink2Handle(p)) - } - msg <- glue( - "GenotypeHandle(genoMeta): cannot determine genotype format for ", - "'{p}'. Use a recognized extension ", - "(.bed/.pgen/.vcf[.gz]/.bcf/.gds), a PLINK prefix, or pass ", - "`format=`." - ) - abort(msg) + panels <- map2(selected$path, formats, .readDosagePanel) + .withSourceProjection(.stackDosagePanels( + panels, + shared, + .chromMetaPath(genoMeta), + selected$path + )) } # Assemble a sharded handle from a per-chromosome meta. Reads each shard's @@ -599,12 +579,12 @@ GenotypeHandle <- function( parsed <- .parseChromMeta(genoMeta) if (nrow(parsed) == 0L) { abort( - "GenotypeHandle(genoMeta): no chromosomes found in the meta input." + "genoMeta: no chromosomes found in the meta input." ) } selected <- .chromMetaSelect(parsed, chroms) shards <- map(selected$path, .resolveGenotypeShard, format = format) - sharedFormat <- .chromMetaCheckFormats(shards) + sharedFormat <- .chromMetaCheckFormats(map_chr(shards, .ghFormat)) .chromMetaCheckSamples(shards, selected) unifiedSnpInfo <- bind_rows(map(shards, .ghSnpInfo)) new( @@ -635,14 +615,13 @@ GenotypeHandle <- function( if (any(keep)) filter(parsed, keep) else parsed } -# Require a single shared format across shards; returns it. +# Require a single shared format across shards' `formats`; returns it. # @noRd -.chromMetaCheckFormats <- function(shards) { - formats <- map_chr(shards, .ghFormat) +.chromMetaCheckFormats <- function(formats) { if (n_distinct(formats) != 1L) { formatStr <- str_flatten(unique(formats), ", ") msg <- glue( - "GenotypeHandle(genoMeta): all per-chromosome files must share ", + "genoMeta: all per-chromosome files must share ", "one format; got: {formatStr}." ) abort(msg) @@ -658,7 +637,7 @@ GenotypeHandle <- function( if (!identical(shards[[i]]@sampleIds, sample0)) { mismatchPath <- parsed$path[[i]] msg <- glue( - "GenotypeHandle(genoMeta): all per-chromosome files must ", + "genoMeta: all per-chromosome files must ", "have identical sample IDs in the same order (mismatch ", "at '{mismatchPath}')." ) @@ -683,7 +662,7 @@ GenotypeHandle <- function( return(invisible(NULL)) } msg <- glue( - "GenotypeHandle(genoMeta): chromosome '{ch}' appears ", + "genoMeta: chromosome '{ch}' appears ", "in more than one per-chromosome file." ) abort(msg) diff --git a/R/GenotypeMixture.R b/R/GenotypeMixture.R new file mode 100644 index 000000000..4456c2104 --- /dev/null +++ b/R/GenotypeMixture.R @@ -0,0 +1,515 @@ +# ============================================================================= +# GenotypeMixture S4 class +# ----------------------------------------------------------------------------- +# A mixture of genotype panels used as one LD reference: a +# MultiAssayExperiment with one experiment per cohort, that cohort's panel. +# Its LD is the weighted average of the panels' LD, sum_k w_k R_k. The panels +# stay as they are -- lazy, on disk -- and are combined only when a step +# reads LD for some variants, so each keeps answering for its own samples: +# allele frequencies, sample size, missingness. +# ============================================================================= + +#' @include AllGenerics.R +NULL + +#' @title Mixture of Genotype Panels +#' @description A \code{GenotypeMixture} uses several genotype panels (see +#' \code{\link{readGenotypes}}) as one LD reference whose LD is the +#' weighted average of theirs, \eqn{\sum_k w_k R_k}. That is the LD of +#' summary statistics meta-analysed across cohorts, with each cohort's +#' weight its share of the GWAS sample size. +#' +#' Unlike an \code{\link{LdMatrix}} mixture, which is combined once when it +#' is built, the panels stay lazy and on disk: LD is read from each and +#' combined only for the variants a step asks about, and every panel keeps +#' answering for its own samples. A step that needs allele frequencies, +#' missingness or minor-allele counts judges each panel on its own and +#' drops a variant that fails in any of them. +#' +#' It \strong{is} a \code{MultiAssayExperiment} with one experiment per +#' cohort: that cohort's genotype panel, cut to the variants every panel +#' carries, in the first panel's order. The mixture's variants, as +#' \code{\link{variantIds}} gives them, are the first panel's. Each panel +#' keeps its own samples and allele coding, and its \code{rowData} column +#' \code{mixtureSign} is \code{1} where it codes a variant's alleles as the +#' first panel does and \code{-1} where it codes them the other way round. +#' The \code{sampleMap} names each sample \code{_}, since +#' cohorts can reuse sample ids, and the weights are +#' \code{metadata(x)$mixtureWeights} (see \code{\link{mixtureWeights}}). +#' +#' Row subsetting, \code{x[i, ]} by position, variant id or \code{GRanges}, +#' cuts every panel to the same variants, and column subsetting narrows +#' the panels' samples. A panel cannot be dropped, since the weights +#' describe all of them: build a new \code{GenotypeMixture} over the panels +#' wanted instead. +#' @seealso \code{\link{GenotypeMixture}} to build one, \code{\link{LdMatrix}}. +#' @importClassesFrom MultiAssayExperiment MultiAssayExperiment +#' @export +setClass("GenotypeMixture", contains = "MultiAssayExperiment") + +setValidity("GenotypeMixture", function(object) { + problems <- .gmCheckComponents(object) + if (length(problems) == 0L) { + problems <- c(.gmCheckSign(object), .gmCheckWeights(object)) + } + if (length(problems) == 0L) TRUE else problems +}) + +# @noRd +.gmCheckComponents <- function(object) { + comps <- .gmComponents(object) + if (length(comps) < 2L || !all(map_lgl(comps, .gmIsPanel))) { + return("the experiments must be at least two genotype panels.") + } + if (length(unique(map_int(comps, nrow))) != 1L) { + return("every panel must hold exactly the mixture's variants.") + } + character() +} + +# A panel as a mixture holds it: the RSE readGenotypes() returns. +# @noRd +.gmIsPanel <- function(x) { + methods::is(x, "RangedSummarizedExperiment") +} + +# @noRd +.gmCheckSign <- function(object) { + signs <- map(.gmComponents(object), .gmComponentSign) + if (all(map_lgl(signs, .gmIsSign)) && all(signs[[1L]] == 1)) { + return(character()) + } + str_c( + "every panel's rowData must carry `mixtureSign`, +1 or -1 per ", + "variant, and the first panel's must be all +1." + ) +} + +# @noRd +.gmIsSign <- function(s) { + !is.null(s) && all(is_in(s, c(-1, 1))) +} + +# @noRd +.gmCheckWeights <- function(object) { + w <- mixtureWeights(object) + named <- identical(names(w), names(.gmComponents(object))) + c( + .ldmCheckWeights(w), + if (length(w) == 0L || !named) { + "`mixtureWeights` must hold one weight per panel, named after it." + } + ) +} + +#' @title Build a GenotypeMixture +#' @description Combine genotype panels into one LD reference whose LD is +#' the weighted average of theirs. See \code{\link{GenotypeMixture-class}}. +#' +#' The panels are matched by position and alleles and put into the first +#' panel's allele coding, so they need not share variant ids or allele +#' orientation. The mixture covers the variants every panel carries; a +#' variant some panel lacks is left out, with a message. +#' @param panels A named list of at least two genotype panels (see +#' \code{\link{readGenotypes}}). The names are the cohorts: they name the +#' mixture's experiments and prefix its samples. +#' @param mixtureWeights One non-negative weight per panel, summing to 1; +#' typically each cohort's share of the GWAS sample size. Matched to the +#' panels by name when named, by position otherwise. +#' @return A \code{GenotypeMixture}. +#' @examples +#' panel <- readGenotypes(plink2Prefix = file.path(system.file("extdata", +#' "ld_reference", "chr22", package = "pecotmr"), +#' "protocol_example.LD.chr22")) +#' half <- seq_len(ncol(panel) / 2) +#' mix <- GenotypeMixture(list(EUR = panel[, half], AFR = panel[, -half]), +#' mixtureWeights = c(EUR = 0.7, AFR = 0.3)) +#' mix +#' @rdname GenotypeMixture-constructor +#' @importFrom MultiAssayExperiment MultiAssayExperiment +#' @export +GenotypeMixture <- function(panels, mixtureWeights) { + .gmAssertPanels(panels) + panels <- map(panels, .asLdRef) + if (!all(map_lgl(panels, .gmIsPanel))) { + abort("Every element of `panels` must be a genotype panel.") + } + w <- .gmNamedWeights(mixtureWeights, names(panels)) + ref <- panels[[1L]] + matched <- map(panels, .ldmMatchTo, ref = ref) + common <- sort(reduce(map(matched, "refIdx"), intersect)) + if (length(common) == 0L) { + abort("The genotype panels share no variant to combine over.") + } + .ldmReportDropped( + length(.ldRefRanges(ref)) - length(common), + length(common), + what = "GenotypeMixture" + ) + aligned <- map(matched, .ldmAlignedOn, common = common) + experiments <- map2(panels, aligned, .gmAlignedComponent) + mae <- MultiAssayExperiment( + experiments = experiments, + colData = .gmColData(experiments), + sampleMap = .gmSampleMap(experiments), + metadata = list(mixtureWeights = w) + ) + obj <- methods::new("GenotypeMixture", mae) + validObject(obj) + obj +} + +# @noRd +.gmAssertPanels <- function(panels) { + if (!is.list(panels) || length(panels) < 2L) { + abort("`panels` must be a list of at least two genotype panels.") + } + nms <- names(panels) + if (is.null(nms) || anyNA(nms) || any(!nzchar(nms)) || anyDuplicated(nms)) { + abort(c( + str_c( + "`panels` must be a named list: each name is a cohort, which ", + "names its experiment and prefixes its samples." + ), + i = "For example, list(EUR = eurPanel, AFR = afrPanel)." + )) + } + invisible(NULL) +} + +# The weights, one per panel and named after it: matched by name when they +# are named, by position otherwise. +# @noRd +.gmNamedWeights <- function(mixtureWeights, cohorts) { + given <- names(mixtureWeights) + w <- .ldmAssertWeights(mixtureWeights, length(cohorts)) + if (is.null(given)) { + return(set_names(w, cohorts)) + } + if (anyDuplicated(given) || !setequal(given, cohorts)) { + abort(glue( + "Named `mixtureWeights` must name each panel once: ", + "{str_flatten(cohorts, ', ')}." + )) + } + set_names(w, given)[cohorts] +} + +# A panel cut to the shared variants in the first panel's order, recording +# how it codes each one's alleles relative to the first panel. +# @noRd +.gmAlignedComponent <- function(panel, aligned) { + .gmWithSign(.ldRefSubset(panel, aligned$idx), aligned$sign) +} + +# @noRd +.gmWithSign <- function(panel, sign) { + rd <- SummarizedExperiment::rowData(panel) + rd$mixtureSign <- as.numeric(sign) + SummarizedExperiment::`rowData<-`(panel, value = rd) +} + +# The primary name of each of a cohort's samples. Cohorts can reuse sample +# ids, and every stochastic sketch does, so the cohort is part of the name. +# @noRd +.gmPrimaryIds <- function(cohort, ids) { + str_c(cohort, "_", ids) +} + +# One row per sample of every panel, recording its cohort. +# @noRd +.gmColData <- function(experiments) { + exec(rbind, !!!unname(imap(experiments, .gmColDataPart))) +} + +# @noRd +.gmColDataPart <- function(panel, cohort) { + ids <- as.character(colnames(panel)) + S4Vectors::DataFrame( + cohort = rep(cohort, length(ids)), + row.names = .gmPrimaryIds(cohort, ids) + ) +} + +# The sampleMap: each panel's samples under their cohort-prefixed names. +# @noRd +.gmSampleMap <- function(experiments) { + exec(rbind, !!!unname(imap(experiments, .gmSampleMapPart))) +} + +# @noRd +.gmSampleMapPart <- function(panel, cohort) { + ids <- as.character(colnames(panel)) + S4Vectors::DataFrame( + assay = factor(rep(cohort, length(ids)), levels = cohort), + primary = .gmPrimaryIds(cohort, ids), + colname = ids + ) +} + +# ---- subsetting ------------------------------------------------------------- + +# Row subsetting cuts every panel to the same variants. Every subscript is +# turned into positions on the first panel, whose ids and alleles the +# mixture's are, and those positions are applied to all of them: the panels' +# own ids can differ in allele order, so matching each on its own could cut +# them to different variants. +#' @importFrom MultiAssayExperiment subsetByRow +setMethod("subsetByRow", c("GenotypeMixture", "ANY"), function(x, y, ...) { + .gmRowsAt(x, .gmRowIndex(x, y)) +}) + +# The weights describe every panel, so none can be dropped. +#' @importFrom MultiAssayExperiment subsetByAssay +setMethod("subsetByAssay", "GenotypeMixture", function(x, y) { + abort(c( + str_c( + "A GenotypeMixture's panels cannot be subset: its weights ", + "describe all of them." + ), + i = "Build a new GenotypeMixture() over the panels wanted." + )) +}) + +# The positions of the variants a row subscript names: a position, a logical +# mask, a variant id (the first panel's row names) or a GRanges. +# @noRd +#' @importFrom IRanges overlapsAny +.gmRowIndex <- function(x, y) { + ranges <- .gmRanges(x) + if (methods::is(y, "GenomicRanges")) { + return(which(overlapsAny(ranges, y))) + } + if (is.character(y)) { + at <- match(y, names(ranges)) + return(at[!is.na(at)]) + } + if (is.logical(y) || is.numeric(y)) { + return(seq_along(ranges)[y]) + } + abort(str_c( + "A GenotypeMixture's rows are subset by position, logical mask, ", + "variant id or GRanges." + )) +} + +# The mixture with every panel cut to the variants at positions `i`, in +# that order. +# @noRd +#' @importFrom MultiAssayExperiment ExperimentList +.gmRowsAt <- function(x, i) { + comps <- map(.gmComponents(x), .gmComponentRowsAt, i = i) + MultiAssayExperiment::`experiments<-`(x, value = ExperimentList(comps)) +} + +# One panel's rows `i`. With no rows left it is held in memory with its +# samples, which the sampleMap names, so an empty mixture does not carry its +# panels' seeds (see .emptyPanel()). +# @noRd +.gmComponentRowsAt <- function(panel, i) { + sign <- .gmComponentSign(panel)[i] + rows <- if (length(i) == 0L) { + .emptyPanel(panel, keepSamples = TRUE) + } else { + .ldRefSubset(panel, i) + } + .gmWithSign(rows, sign) +} + +# ---- accessors -------------------------------------------------------------- + +#' @rdname mixtureWeights +#' @export +setMethod("mixtureWeights", "GenotypeMixture", function(x) { + S4Vectors::metadata(x)$mixtureWeights +}) + +#' @rdname variantIds +#' @export +setMethod("variantIds", "GenotypeMixture", function(x, ...) { + .ldRefVariantIds(x) +}) + +#' @rdname GenotypeMixture-class +#' @param object A \code{GenotypeMixture}. +#' @export +setMethod("show", "GenotypeMixture", function(object) { + w <- mixtureWeights(object) + comps <- .gmComponents(object) + cat(glue( + "GenotypeMixture: {.gmNVariants(object)} variants from ", + "{length(comps)} genotype panels\n", + .trim = FALSE + )) + cat(glue( + " panels {str_flatten(names(comps), ', ')}; weights ", + "{str_flatten(signif(w, 3), ', ')}; panel sizes ", + "{str_flatten(map_int(comps, ncol), ', ')}\n", + .trim = FALSE + )) +}) + +# ---- the LD reference answers, combined over the panels --------------------- + +# The panels, named after their cohorts. +# @noRd +#' @importFrom MultiAssayExperiment experiments +.gmComponents <- function(x) { + as.list(experiments(x)) +} + +# @noRd +.gmComponentSign <- function(panel) { + SummarizedExperiment::rowData(panel)$mixtureSign +} + +# A +1 / -1 matrix with one row per variant and one column per panel: each +# panel's allele coding relative to the first's. +# @noRd +.gmSign <- function(x) { + exec(cbind, !!!unname(map(.gmComponents(x), .gmComponentSign))) +} + +# The mixture's variants: the first panel's ranges, with the SNP / A1 / A2 +# columns that identify them. +# @noRd +.gmRanges <- function(x) { + rr <- SummarizedExperiment::rowRanges(.gmComponents(x)[[1L]]) + S4Vectors::`mcols<-`( + rr, + value = S4Vectors::mcols(rr)[, c("SNP", "A1", "A2")] + ) +} + +# @noRd +.gmNVariants <- function(x) { + nrow(.gmComponents(x)[[1L]]) +} + +# 1 / sum(w^2 / n_k) over the panels' sizes as `nOf` measures them: the size +# of one panel whose LD would be as noisy as the weighted average. +# @noRd +.gmPooledN <- function(x, nOf) { + n <- map_int(.gmComponents(x), nOf) + as.integer(round(1 / sum(mixtureWeights(x)^2 / n))) +} + +# The mixture's LD among its variants at `idx`: each panel's LD, re-signed +# into the first panel's allele coding, averaged with the weights. One +# panel's block is held at a time. +# @noRd +.gmLdAt <- function(x, idx) { + reduce( + seq_along(.gmComponents(x)), + .gmAddLd, + .init = NULL, + x = x, + idx = idx + ) +} + +# @noRd +.gmAddLd <- function(acc, k, x, idx) { + s <- .gmSign(x)[idx, k] + term <- mixtureWeights(x)[[k]] * + unname(.ldRefLdAt(.gmComponents(x)[[k]], idx)) * + outer(s, s) + if (is.null(acc)) term else acc + term +} + +# Per-variant statistics a panel filter judges, from each panel on its own +# terms: a variant's MAF is its smallest in any panel and its missingness its +# largest, so a cutoff drops a variant that fails in any of them. +# @noRd +.gmVariantStats <- function(x, idx, needMissing, step) { + each <- map( + .gmComponents(x), + .ldRefVariantStats, + idx = idx, + needMissing = needMissing, + step = step + ) + list( + maf = exec(pmin, !!!unname(map(each, "maf"))), + missRate = if (needMissing) exec(pmax, !!!unname(map(each, "missRate"))) + ) +} + +# The mixture's A1 frequency, sum_k w_k p_k with each panel's frequency put +# into the first panel's coding, or NULL unless every panel records one. +# @noRd +.gmAf <- function(x) { + afs <- map(.gmComponents(x), .ldRefAf) + if (any(map_lgl(afs, is.null))) { + return(NULL) + } + signs <- .gmSign(x) + aligned <- map(seq_along(afs), .gmAlignedAf, afs = afs, signs = signs) + reduce(map2(unname(mixtureWeights(x)), aligned, `*`), `+`) +} + +# @noRd +.gmAlignedAf <- function(k, afs, signs) { + if_else(signs[, k] > 0, afs[[k]], 1 - afs[[k]]) +} + +# Per-variant genotype variance averaged over the panels with the weights -- +# the average within-panel variance, which is what the averaged LD describes. +# The variance of the pooled frequency, 2p(1 - p), would also count the +# spread of frequencies between panels, which the mixture LD leaves out. +# @noRd +.gmVariance <- function(x, idx) { + each <- map(.gmComponents(x), .ldRefVariance, idx = idx) + reduce(map2(unname(mixtureWeights(x)), each, `*`), `+`) +} + +# A factor of the mixture's LD over `idx`: each panel's standardized +# dosages, re-signed and scaled by sqrt(w_k / (m_k - 1)), stacked by rows, +# then scaled so the crossproduct is (N - 1) R for N rows -- the same +# convention as a single panel's scaled dosages. Its crossproduct is the +# weighted average of the panels' LD exactly, .gmLdAt()'s. +# @noRd +.gmFactor <- function(x, idx) { + blocks <- map(seq_along(.gmComponents(x)), .gmFactorBlock, x = x, idx = idx) + stacked <- exec( + rbind, + !!!blocks, + !!!map2(blocks, unname(mixtureWeights(x)), .gmMonomorphicRows) + ) + stacked * sqrt(nrow(stacked) - 1) +} + +# A variant monomorphic in a panel has an all-zero column in that panel's +# block, which would leave the mixture's LD short of a unit diagonal. A +# panel's LD treats such a variant as independent of every other (see +# .ldCleanCorrelation()), so in the average it contributes w_k to the +# variant's own variance and nothing else: two rows per such variant, holding +# +/- sqrt(w_k / 2) in its column, so the column keeps a zero mean and an +# engine that centres the factor's rows itself still sees the same LD. +# @noRd +.gmMonomorphicRows <- function(block, w) { + dead <- which(colSums(block^2) == 0) + .ldPaddingRows(dead, ncol(block), sqrt(w / 2)) +} + +# @noRd +.gmFactorBlock <- function(k, x, idx) { + scaled <- .ldRefStandardized(.gmComponents(x)[[k]], idx) + signed <- sweep(scaled, 2L, .gmSign(x)[idx, k], `*`) + signed * sqrt(mixtureWeights(x)[[k]] / (nrow(scaled) - 1)) +} + +# TRUE when two mixtures average the same panels -- the same samples, in the +# same order -- with the same weights. Their variant sets may differ, as two +# QC'd subsets of one mixture's do. +# @noRd +.gmSameMixture <- function(x, y) { + cx <- .gmComponents(x) + cy <- .gmComponents(y) + length(cx) == length(cy) && + isTRUE(all.equal(mixtureWeights(x), mixtureWeights(y))) && + identical( + map(cx, .ldRefSampleIds), + map(cy, .ldRefSampleIds) + ) +} diff --git a/R/GwasFineMappingResult.R b/R/GwasFineMappingResult.R index 1a2aefd9c..db4978363 100644 --- a/R/GwasFineMappingResult.R +++ b/R/GwasFineMappingResult.R @@ -8,7 +8,7 @@ # range -- the genome-wide-across-blocks shape that # qtlEnrichmentPipeline / colocPipeline expect when sweeping a study # across LD blocks. Class-level slots: -# * ldSketch GenotypeHandle for the LD reference; required for the +# * ldReference GenotypeHandle for the LD reference; required for the # LD-block-indexed susieRSS workflow. # Methods that take per-row selectors accept (study, method) and ignore # context/trait (GWAS has no per-tuple context or trait axis). @@ -133,7 +133,7 @@ setClass( #' @param dataSource Optional per-row label for the kind of input the fit saw, #' \code{"individual"} or \code{"sumstat"}. Recorded rather than derived: a #' combined collection can hold rows of both kinds, and its single -#' \code{ldSketch} cannot say which row came from which. +#' \code{ldReference} cannot say which row came from which. #' @param weightStandardized Optional per-row flag: whether the posterior #' effect is on the STANDARDIZED genotype scale. The posterior effect IS the #' prediction weight, so it carries the same scale question as a TWAS @@ -141,7 +141,7 @@ setClass( #' it standardizes genotypes before fitting, and the posterior mean is #' reported on the scale of the \code{X} the fit was given. The argument #' exists for a collection assembled by hand, where only the caller knows. -#' @param ldSketch An optional genotype panel (see +#' @param ldReference An optional genotype panel (see #' \code{\link{readGenotypes}}). #' @param traitPos Optional per-row trait genomic anchor (a \code{GRanges} or #' \code{NULL}), carried forward as provenance; not part of the identity key. @@ -169,7 +169,7 @@ GwasFineMappingResult <- function( dataType = NULL, dataSource = NULL, weightStandardized = NULL, - ldSketch = NULL + ldReference = NULL ) { n <- length(studyName) if (length(method) != n || length(entry) != n) { @@ -200,7 +200,7 @@ GwasFineMappingResult <- function( obj <- new( "GwasFineMappingResult", grl, - ldSketch = .asLdSketch(ldSketch) + ldReference = .asLdRef(ldReference) ) validObject(obj) obj @@ -334,10 +334,10 @@ setMethod("show", "GwasFineMappingResult", function(object) { .trim = FALSE )) } - ldSrc <- if (is.null(object@ldSketch)) { + ldSrc <- if (is.null(object@ldReference)) { "NULL" } else { - .ldSketchLabel(object@ldSketch) + .ldRefLabel(object@ldReference) } - cat(glue(" LD sketch: {ldSrc}\n", .trim = FALSE)) + cat(glue(" LD reference: {ldSrc}\n", .trim = FALSE)) }) diff --git a/R/H2Estimate.R b/R/H2Estimate.R index 104382844..1f162578e 100644 --- a/R/H2Estimate.R +++ b/R/H2Estimate.R @@ -8,6 +8,9 @@ # integration with the sLDSC postprocessing pipeline. # ============================================================================= +#' @include MethodParam.R +NULL + #' @title Heritability Estimate #' @description Container for univariate heritability estimation results. Holds #' global, local, and annotation-stratified estimates. @@ -56,10 +59,10 @@ setClass( estimateSe = "numeric", intercept = "numeric", interceptSe = "numeric", - localBlocks = "ANY", # data.frame or NULL - enrichment = "ANY", # data.frame or NULL - annotationJackknifeCoefs = "ANY", # matrix or NULL - scoreStats = "ANY", # list or NULL + localBlocks = "data.frame_OR_NULL", + enrichment = "data.frame_OR_NULL", + annotationJackknifeCoefs = "matrix_OR_NULL", + scoreStats = "list_OR_NULL", method = "character", nSnps = "integer", traitName = "character" diff --git a/R/JointGroup.R b/R/JointGroup.R index 33404b177..3be7be749 100644 --- a/R/JointGroup.R +++ b/R/JointGroup.R @@ -95,7 +95,11 @@ setClass( # any of them is fitted. The sketch is a lazy panel, so the matrix is # derived once per group at fit time -- which is what the univariate RSS # path in fineMappingPipeline already does. - representation(Z = "matrix", ldSketch = "LdSketch_OR_NULL", N = "numeric"), + representation( + Z = "matrix", + ldReference = "LdReference_OR_NULL", + N = "numeric" + ), validity = function(object) { errors <- c( if (ncol(object@Z) != nrow(object@conditions)) { @@ -140,8 +144,9 @@ setClass( # ---- Pipeline markers ------------------------------------------------------- # Not empty: the `config` list carries the per-pipeline parameter tail -# (coverage/cvFolds/samplePartition/fitFullData/fitRetention/... for fm; -# fitRetention/cvFolds/... for twas), and dispatch on the concrete +# (credibleSetParam/crossValidationParam/fitRetention/... for fm; +# crossValidationParam/ensembleParam/fitRetention/... for twas), and dispatch on +# the concrete # class selects the result type via `construct()`. setClass("JointPipeline", contains = "VIRTUAL", representation(config = "list")) @@ -175,7 +180,7 @@ setClass("TwasJointPipeline", contains = "JointPipeline") .jgZ <- function(g) g@Z # @noRd -.jgLdSketch <- function(g) g@ldSketch +.jgLdRef <- function(g) g@ldReference # The variants a summary-statistics group covers, in Z's row order -- which is # the order the derived LD matrix comes back in. @@ -189,8 +194,8 @@ setClass("TwasJointPipeline", contains = "JointPipeline") # would recompute the same matrix for every condition. # @noRd .jgLdMatrix <- function(g) { - .ldFromSketch( - .jgLdSketch(g), + .ldFromRef( + .jgLdRef(g), .jgVariantIds(g), label = "jointEngine" ) @@ -199,6 +204,38 @@ setClass("TwasJointPipeline", contains = "JointPipeline") # @noRd .jgN <- function(g) g@N +# The group restricted to the conditions `keep` marks: each condition's Y / Z +# column goes with it, as does its trait position or sample size where those +# are per condition. NULL when none is kept. Rebuilt through initialize(), so +# the result is validated like any constructed group. +# @noRd +.jgKeepConditions <- function(g, keep) { + if (all(keep)) { + return(g) + } + if (!any(keep)) { + return(NULL) + } + idx <- which(keep) + cond <- slice(g@conditions, idx) + if (is(g, "IndividualJointGroup")) { + traitPos <- if (length(g@traitPos) > 0L) g@traitPos[idx] else g@traitPos + return(methods::initialize( + g, + conditions = cond, + Y = g@Y[, idx, drop = FALSE], + traitPos = traitPos + )) + } + N <- if (length(g@N) == length(keep)) g@N[idx] else g@N + methods::initialize( + g, + conditions = cond, + Z = g@Z[, idx, drop = FALSE], + N = N + ) +} + # @noRd .jcPattern <- function(cell) cell@pattern diff --git a/R/LdData.R b/R/LdData.R deleted file mode 100644 index 07a119ff8..000000000 --- a/R/LdData.R +++ /dev/null @@ -1,723 +0,0 @@ -# ============================================================================= -# LdData S4 class -# ----------------------------------------------------------------------------- -# Container for LD information used by fine-mapping / colocalization. Holds -# a pre-computed correlation matrix (or a list of per-block matrices) and/or -# a GenotypeHandle (or list of handles for mixture panels) for on-demand -# correlation computation. -# ============================================================================= - -#' @include GenotypeHandle.R AllClasses.R -NULL - -#' @title LD Data Container -#' @description S4 container for LD information. Stores either a pre-computed -#' correlation matrix or a \code{GenotypeHandle} (or list of handles for -#' mixture panels) for lazy genotype/correlation access. -#' -#' An \code{LdData} \strong{is} a \code{GRanges} over the variants it -#' covers -- carrying A1, A2, variant_id and optionally allele_freq, -#' variance and n_nomiss as metadata columns -- so \code{length()}, -#' \code{seqnames()} and \code{start()} answer directly, as they do for -#' \code{\link{LdScore}} and \code{\link{LdEigen}}. The correlation may -#' be NULL, in which case those ranges are the object's only record of which -#' variants it describes. -#' -#' @slot correlation A correlation matrix, a list of per-block matrices -#' (block-diagonal LD), or NULL if genotypes are available and R should be -#' computed on demand. -#' @slot genotypeHandle Where genotypes are read from: a genotype handle, a -#' list of them (for mixture panels), a matrix of dosages already extracted -#' and filtered, or NULL when only pre-computed R is available. Pass a -#' genotype panel (see \code{\link{readGenotypes}}) to the constructor and -#' it is unwrapped to its handle. -#' @slot snpIdx Integer vector of 1-based SNP indices into the handle's -#' \code{snpInfo}. NULL when correlation is pre-computed, or when the -#' source is a matrix (which is already the subset). -#' @slot blockMetadata A \code{GRanges} with one range per block: its -#' \code{seqnames} and range are the block's chromosome and genomic span, -#' and \code{mcols} carry \code{blockId}, \code{size}, \code{startIdx} -#' and \code{endIdx} -- the index range into the correlation matrix the -#' block addresses. Extra columns the caller supplied are kept as mcols. -#' A block whose index range is out of bounds has no span to derive and -#' gets a width-0 range. -#' @slot nSamples Integer, reference panel sample size. -#' @slot mixtureWeights NULL when \code{genotypeHandle} is a single -#' \code{GenotypeHandle}; a numeric vector of mixing proportions (one per -#' panel, summing to 1) when \code{genotypeHandle} is a list of -#' \code{GenotypeHandle}s. Used by \code{ldMatrix()} to compute a -#' weighted-average mixture LD matrix; required whenever \code{genotypeHandle} -#' is a list and \code{ldMatrix()} will be called. -#' @export -setClass( - "LdData", - contains = "GRanges", - representation( - correlation = "LdCorrelation", - genotypeHandle = "LdGenotypeSource", - snpIdx = "LdSnpIndex", - blockMetadata = "GRanges", - nSamples = "integer", - mixtureWeights = "LdMixtureWeights" - ), - validity = function(object) { - errors <- c( - if (is.null(object@correlation) && is.null(object@genotypeHandle)) { - str_c( - "At least one of 'correlation' or ", - "'genotypeHandle' must be non-NULL" - ) - }, - if (length(object) == 0) "an LdData must cover >= 1 variant", - .ldCheckGenotypeSource(object@genotypeHandle), - .ldCheckCorrelation(object@correlation), - .ldCheckMixtureWeights(object), - .ldCheckBlockMetadata(object) - ) - if (length(errors) == 0) TRUE else errors - } -) - -# Mixture weights are only meaningful over a LIST of panels, and must then be -# a proper simplex over them. NULL weights are always valid. -# @noRd -.ldCheckMixtureWeights <- function(object) { - if (is.null(object@mixtureWeights)) { - return(NULL) - } - if (!is.list(object@genotypeHandle)) { - return(str_c( - "'mixtureWeights' may only be set when ", - "'genotypeHandle' is a list of panels" - )) - } - w <- object@mixtureWeights - if (!is.numeric(w) || length(w) != length(object@genotypeHandle)) { - return(str_c( - "'mixtureWeights' must be numeric of length ", - "equal to the genotypeHandle list" - )) - } - if (any(w < 0) || abs(sum(w) - 1) > 1e-6) { - return("'mixtureWeights' must be non-negative and sum to 1") - } - NULL -} - -#' @describeIn LdData-class Refused. Subsetting would narrow the variants -#' while \code{correlation} -- variant-by-variant, and \code{snpIdx}, which -#' indexes the reference panel -- stayed as they were, leaving an LD matrix -#' describing variants the object no longer has. Build the LdData over the -#' variant set you want instead. -#' @param x An \code{LdData}. -#' @param i,j,... Subscripts; any use is an error. -#' @param drop Ignored. -#' @return Nothing: this method always signals an error. -#' @export -setMethod("[", "LdData", function(x, i, j, ..., drop = TRUE) { - abort(glue( - "an LdData cannot be subset: its correlation is variant-by-variant ", - "and its snpIdx addresses the reference panel, so narrowing the ", - "variants would leave both describing variants that are no longer ", - "present. Build the LdData over the variant set you want instead." - )) -}) - -#' @rdname show-methods -#' @export -setMethod("show", "LdData", function(object) { - n_var <- length(object) - has_R <- !is.null(object@correlation) - has_geno <- !is.null(object@genotypeHandle) - r_type <- if (has_R && is.list(object@correlation)) { - "block-diagonal" - } else { - "single" - } - cat(glue("LdData: {n_var} variants\n", .trim = FALSE)) - cat(glue( - " Correlation: {if (has_R) r_type else 'NULL'}, ", - "Genotype handle: {if (has_geno) 'available' else 'NULL'}\n", - .trim = FALSE - )) - cat(glue(" Reference N: {object@nSamples}\n", .trim = FALSE)) -}) - -# One element of a mixture list: unwrap a panel, pass anything else through -# for validity to judge. -# @noRd -.ldDataSourceElement <- function(x) { - open <- .openGenotypeHandle(x) - if (is.null(open)) x else open -} - -# Normalise whatever the caller passed for `genotypeHandle` into one of the -# shapes the slot admits. A panel is the public shape (readGenotypes()); the -# slot stores the handle behind it, because `snpIdx` selects into the handle's -# whole snpInfo and a panel adds nothing the handle does not already carry. -# Anything else is passed through for the slot's class union to accept or -# reject -- this is a normaliser, not a second type check. -# @noRd -.ldDataGenotypeSource <- function(x) { - if (is.null(x) || is.matrix(x)) { - return(x) - } - if (is.list(x)) { - # Unwrap the panels but do not reject here: validity reports a bad - # mixture element by its index, which a purrr-wrapped abort would bury. - return(map(x, .ldDataSourceElement)) - } - open <- .openGenotypeHandle(x) - if (!is.null(open)) { - return(open) - } - # The slot's class union would reject this anyway, but its message names - # the union rather than what to pass instead. - hint <- if (methods::is(x, "DelayedArray")) { - str_c( - " -- pass the panel it came from rather than its assay, so the ", - "variant selection in `snpIdx` still means something" - ) - } else { - "" - } - abort(glue( - "`genotypeHandle` must be a genotype panel from readGenotypes(), a ", - "list of them, a matrix of dosages, or NULL (got ", - "{class(x)[[1L]]}){hint}." - )) -} - -# --- block metadata -------------------------------------------------------- -# -# Five paths used to build this table -- the two region loaders, the two -# bare-matrix wrappers, and callers handing over a bare genomic span -- and -# each emitted a different set of columns, so consumers read `startIdx`, -# `size` and `chrom` that were there by luck rather than by contract. One -# normaliser at the only door (LdData()) fills whatever is absent and stores -# a single shape, the way .ldDataGenotypeSource() does for the handle. -# -# These are the columns consumers actually read. `blockStart` / `blockEnd` -# are deliberately NOT required: the two region loaders record a block's -# genomic span, nothing reads it, and demanding it would reject every -# multi-block table built without it. Supplied columns are carried through. -# @noRd -.ldBlockColumns <- c("blockId", "chrom", "size", "startIdx", "endIdx") - -# What survives as mcols once chrom / blockStart / blockEnd become the -# GRanges itself. -# @noRd -.ldBlockMcols <- c("blockId", "size", "startIdx", "endIdx") - -# Supplied values are never replaced: a caller handing over deliberately -# out-of-range indices keeps them, because partitionLdMatrix() still has to -# reject those and substituting valid ones would hide the error. -# @noRd -.ldBlockMetadata <- function(blockMetadata, variants) { - if (length(variants) == 0L) { - # Nothing to derive a block from. Validity rejects the object for - # covering no variant; crashing here would pre-empt that message. - return(GenomicRanges::GRanges()) - } - tbl <- .ldBlockMetadataTibble(blockMetadata) - if (nrow(tbl) == 0L) { - # A placeholder table means "one block", not "no blocks": an LdData - # always covers at least one variant. - tbl <- tibble(blockId = 1L) - } - tbl <- .ldBlockMetadataIndices(tbl, length(variants)) - tbl <- .ldBlockMetadataOrder(.ldBlockMetadataDerive(tbl, variants)) - .ldBlockMetadataGRanges(tbl, variants) -} - -# The stored shape: one range per block, so the invariants the merge logic -# depends on are the ones GRanges already answers -- isDisjoint() for -# overlap, sort() for order, seqnames() for the same-chromosome guard. The -# index payload the matrix is addressed by rides along in mcols. -# @noRd -.ldBlockMetadataGRanges <- function(tbl, variants) { - span <- .ldBlockSpans(tbl, variants) - gr <- GenomicRanges::GRanges( - seqnames = tbl$chrom, - ranges = IRanges::IRanges(start = span$start, end = span$end) - ) - keep <- c( - intersect(.ldBlockMcols, names(tbl)), - setdiff(names(tbl), c(.ldBlockColumns, "blockStart", "blockEnd")) - ) - S4Vectors::mcols(gr) <- S4Vectors::DataFrame( - as.data.frame(select(tbl, all_of(keep))) - ) - gr -} - -# Each block's genomic span. Supplied blockStart / blockEnd win; otherwise -# the span is read off the variants the index range covers. -# -# A block whose index range is out of bounds has no span to read, and a -# GRanges cannot hold an NA one (seqnames reject NAs outright). It gets a -# WIDTH-0 range at the first variant instead: legal, and visibly not an -# interval, so nothing downstream mistakes it for a real span. -# partitionLdMatrix() rejects the block on its indices regardless. -# @noRd -.ldBlockSpans <- function(tbl, variants) { - if (is_in("blockStart", names(tbl)) && is_in("blockEnd", names(tbl))) { - return(list(start = tbl$blockStart, end = tbl$blockEnd)) - } - parts <- map( - seq_len(nrow(tbl)), - .ldBlockSpanAt, - tbl = tbl, - variants = variants - ) - list( - start = map_dbl(parts, "start"), - end = map_dbl(parts, "end") - ) -} - -# @noRd -.ldBlockSpanAt <- function(i, tbl, variants) { - s <- tbl$startIdx[[i]] - e <- tbl$endIdx[[i]] - if (.ldBlockRangeUsable(s, e, length(variants))) { - part <- variants[seq.int(s, e)] - return(list( - start = min(GenomicRanges::start(part)), - end = max(GenomicRanges::end(part)) - )) - } - anchor <- GenomicRanges::start(variants)[[1L]] - list(start = anchor, end = anchor - 1L) -} - -# @noRd -.ldBlockRangeUsable <- function(s, e, n) { - !is.na(s) && !is.na(e) && s >= 1L && e >= s && e <= n -} - -# Any accepted shape as a tibble. `start`/`end` is how a bare genomic span -# arrives; they name the BLOCK's span, so they become blockStart/blockEnd. -# @noRd -.ldBlockMetadataTibble <- function(x) { - if (!is(x, "GRanges") && !is.data.frame(x) && !is(x, "DataFrame")) { - # as.data.frame() would happily turn a string into a one-column - # table, so the shapes have to be named rather than coerced blindly. - abort(glue( - "`blockMetadata` must be a GRanges, a data.frame or a ", - "DataFrame; got {class(x)[[1L]]}" - )) - } - tbl <- if (is(x, "GRanges")) { - .ldBlockMetadataFromGranges(x) - } else { - as_tibble(as.data.frame(x)) - } - .ldBlockMetadataRenameSpan(tbl) -} - -# @noRd -.ldBlockMetadataFromGranges <- function(x) { - md <- as_tibble(as.data.frame(S4Vectors::mcols(x))) - # An LdBlocks-style GRanges carries chrom / blockStart / blockEnd in its - # mcols as well as in its ranges. The mcols win: binding both would - # duplicate the names and leave no column called `chrom` at all. - fromRanges <- tibble( - chrom = as.character(GenomicRanges::seqnames(x)), - blockStart = GenomicRanges::start(x), - blockEnd = GenomicRanges::end(x) - ) - bind_cols( - md, - select( - fromRanges, - all_of(setdiff(names(fromRanges), names(md))) - ) - ) -} - -# @noRd -.ldBlockMetadataRenameSpan <- function(tbl) { - if (!is_in("blockStart", names(tbl)) && is_in("start", names(tbl))) { - tbl <- rename(tbl, blockStart = "start") - } - if (!is_in("blockEnd", names(tbl)) && is_in("end", names(tbl))) { - tbl <- rename(tbl, blockEnd = "end") - } - tbl -} - -# The index range is the one thing that cannot be derived for several -# blocks: only the caller knows which variants each covers. A single block -# covers all of them. -# @noRd -.ldBlockMetadataIndices <- function(tbl, n) { - absent <- setdiff(c("startIdx", "endIdx"), names(tbl)) - if (length(absent) == 0L) { - return(tbl) - } - if (length(absent) == 1L) { - abort(glue( - "`blockMetadata` gives one of startIdx / endIdx but not the ", - "other; supply both or neither." - )) - } - if (is_in("size", names(tbl))) { - # Blocks are taken in order, so the sizes give the ranges. This is - # the LdBlocks shape: a block table that says how many variants each - # block holds but not where they sit in the matrix. - sizes <- as.integer(tbl$size) - ends <- cumsum(sizes) - return(mutate(tbl, startIdx = ends - sizes + 1L, endIdx = ends)) - } - if (nrow(tbl) > 1L) { - abort(glue( - "`blockMetadata` describes {nrow(tbl)} blocks but gives neither ", - "an index range nor `size`, so which variants each block covers ", - "cannot be determined." - )) - } - mutate(tbl, startIdx = 1L, endIdx = n) -} - -# `blockId`, `chrom` and `size` all follow from the index range, for one -# block or many. -# @noRd -.ldBlockMetadataDerive <- function(tbl, variants) { - absent <- setdiff(.ldBlockColumns, names(tbl)) - if (length(absent) == 0L) { - return(tbl) - } - derived <- tibble( - blockId = seq_len(nrow(tbl)), - chrom = .ldBlockChroms(tbl, variants), - size = as.integer(tbl$endIdx - tbl$startIdx + 1L), - startIdx = tbl$startIdx, - endIdx = tbl$endIdx - ) - bind_cols(tbl, select(derived, all_of(absent))) -} - -# @noRd -.ldBlockChroms <- function(tbl, variants) { - map_chr( - seq_len(nrow(tbl)), - .ldBlockChromAt, - tbl = tbl, - variants = variants - ) -} - -# An out-of-range block falls back to the data's own chromosome rather than -# NA: seqnames cannot hold NA, and partitionLdMatrix() rejects the block on -# its indices anyway. Its range is width 0 (see .ldBlockSpanAt). -# @noRd -.ldBlockChromAt <- function(i, tbl, variants) { - s <- tbl$startIdx[[i]] - e <- tbl$endIdx[[i]] - part <- if (.ldBlockRangeUsable(s, e, length(variants))) { - variants[seq.int(s, e)] - } else { - variants - } - as.character(GenomicRanges::seqnames(part))[[1L]] -} - -# Canonical columns first, anything the caller added after. -# @noRd -.ldBlockMetadataOrder <- function(tbl) { - select(tbl, all_of(.ldBlockColumns), everything()) -} - -# Every LdData describes at least one block, with the columns its consumers -# read. The normaliser guarantees both; this is the backstop that keeps a -# future construction path from quietly dropping one. -# @noRd -.ldCheckBlockMetadata <- function(object) { - bm <- object@blockMetadata - absent <- setdiff(.ldBlockMcols, names(S4Vectors::mcols(bm))) - if (length(absent) > 0L) { - return(str_c( - "'blockMetadata' is missing required column(s): ", - str_flatten(absent, ", ") - )) - } - if (length(bm) == 0L) { - return("'blockMetadata' must describe at least one block") - } - NULL -} - -#' @title Create an LdData Object -#' @description Construct an \code{LdData} from a correlation matrix and/or -#' genotype handle, plus variant metadata as a GRanges. -#' @param correlation A correlation matrix, list of matrices, or NULL. -#' @param genotypeHandle A genotype panel (see -#' \code{\link{readGenotypes}}), a list of panels for a mixture reference, -#' a matrix of already-extracted dosages, or NULL. -#' @param snpIdx Vector of 1-based SNP indices, coerced to integer, or NULL. -#' @param variants A GRanges with variant metadata (must have variant_id in -#' mcols, plus A1, A2). -#' @param blockMetadata Block boundaries: a \code{GRanges}, a -#' \code{data.frame} or a \code{DataFrame}. Whatever is absent for a -#' single block is derived from \code{variants}, so a bare genomic span -#' -- or nothing at all -- is enough; with several blocks either the index -#' columns or \code{size} must be supplied. Normalised on the way in and -#' stored as a \code{GRanges} (see the \code{blockMetadata} slot). -#' @param nSamples Integer, reference panel sample size. -#' @param mixtureWeights Optional numeric vector of mixing proportions, one per -#' panel in \code{genotypeHandle} when it is a list. Must be non-negative and -#' sum to 1. Required whenever \code{genotypeHandle} is a list and downstream -#' code will call \code{ldMatrix()}. -#' @return An \code{LdData} object. -#' @examples -#' data(eqtlRegionExample) -#' X <- eqtlRegionExample$X[, 1:8] -#' gr <- GenomicRanges::GRanges("22", -#' IRanges::IRanges(seq(1L, by = 100L, length.out = 8), width = 1L)) -#' ld <- LdData(correlation = cor(X), variants = gr, -#' blockMetadata = S4Vectors::DataFrame( -#' chrom = "22", start = 1L, end = 1000L)) -#' ld -#' @importFrom checkmate assert checkMatrix checkList checkNull -#' @importFrom checkmate assertNumeric -#' @export -LdData <- function( - correlation = NULL, - genotypeHandle = NULL, - snpIdx = NULL, - variants, - blockMetadata, - nSamples = 0L, - mixtureWeights = NULL -) { - # correlation is documented as a matrix OR a list of matrices OR NULL, - # so this must be an or-combination, not assertMatrix. - assert( - checkMatrix(correlation), - checkList(correlation), - checkNull(correlation), - .var.name = "correlation" - ) - assertNumeric(mixtureWeights, null.ok = TRUE) - obj <- new( - "LdData", - variants, - correlation = correlation, - genotypeHandle = .ldDataGenotypeSource(genotypeHandle), - snpIdx = if (is.null(snpIdx)) NULL else as.integer(snpIdx), - blockMetadata = .ldBlockMetadata(blockMetadata, variants), - nSamples = as.integer(nSamples), - mixtureWeights = mixtureWeights - ) - validObject(obj) - obj -} - -# Internal: convert a refPanel data.frame (chrom/pos/A1/A2/variant_id, with -# optional allele_freq/variance/n_nomiss) into the GRanges an LdData is. -.refPanelToGranges <- function(refPanel) { - chr <- withChrPrefix(refPanel$chrom) - pos <- as.integer(refPanel$pos) - - gr <- GRanges( - seqnames = chr, - ranges = IRanges(start = pos, width = 1L) - ) - - mcolsData <- DataFrame( - variant_id = refPanel$variant_id, - A1 = refPanel$A1, - A2 = refPanel$A2 - ) - - optional <- intersect( - c("allele_freq", "variance", "n_nomiss"), - names(refPanel) - ) - `mcols<-`( - gr, - value = cbind( - mcolsData, - DataFrame(refPanel[optional], check.names = FALSE) - ) - ) -} - -# One panel scaled by its mixture weight. -# @noRd -.ldScalePanel <- function(w, panel) { - w * panel -} - -#' @rdname ldMatrix -#' @export -setMethod("ldMatrix", "LdData", function(x) { - if (!is.null(x@correlation)) { - return(x@correlation) - } - if (is.null(x@genotypeHandle)) { - abort("No correlation matrix or genotype handle available") - } - if (is.list(x@genotypeHandle)) { - if (is.null(x@mixtureWeights)) { - msg <- glue( - "Cannot compute mixture LD: `mixtureWeights` is NULL. ", - "Construct LdData with mixtureWeights = ", - "when supplying a list of GenotypeHandles." - ) - abort(msg) - } - perPanel <- map(x@genotypeHandle, .ldPanelLd, snpIdx = x@snpIdx) - dims <- map_int(perPanel, nrow) - if (length(unique(dims)) != 1L) { - msg <- glue( - "Mixture panels yielded LD matrices of differing ", - "dimensions: {str_flatten(dims, ', ')}. All panels ", - "must be aligned on the same variant subset." - ) - abort(msg) - } - # The mixture is a weighted sum over the panels, so it is a fold. - weighted <- map2(x@mixtureWeights, perPanel, .ldScalePanel) - return(`dimnames<-`( - reduce( - weighted, - `+`, - .init = matrix(0, nrow = dims[[1L]], ncol = dims[[1L]]) - ), - dimnames(perPanel[[1L]]) - )) - } - computeLd(.ldSourceDosages(x@genotypeHandle, x@snpIdx), method = "sample") -}) - -#' @rdname genotypes -#' @export -setMethod("genotypes", "LdData", function(x) { - if (is.null(x@genotypeHandle)) { - return(NULL) - } - if (is.list(x@genotypeHandle)) { - # A mixture list may hold matrices as well as handles. - map(x@genotypeHandle, .ldSourceDosages, x@snpIdx) - } else { - .ldSourceDosages(x@genotypeHandle, x@snpIdx) - } -}) - -#' @rdname hasGenotypes -#' @export -setMethod("hasGenotypes", "LdData", function(x) { - !is.null(genotypeHandle(x)) -}) - -#' @rdname variantIds -#' @export -setMethod("variantIds", "LdData", function(x) { - mcols(x)$variant_id -}) - -#' @rdname variantInfo -#' @export -setMethod("variantInfo", "LdData", function(x) { - # A plain GRanges view: callers subset and re-wrap it, and carrying the - # LD payload along would make those copies quietly expensive. - as(x, "GRanges") -}) - -#' @rdname blockMetadata -#' @export -setMethod("blockMetadata", "LdData", function(x) { - x@blockMetadata -}) - -#' @rdname refPanel -#' @export -setMethod("refPanel", "LdData", function(x) { - mutate( - as_tibble(as.data.frame(mcols(x))), - chrom = as.character(seqnames(x)), - pos = start(x) - ) -}) - -#' @rdname genotypeHandle -#' @keywords internal -setMethod("genotypeHandle", "LdData", function(x) x@genotypeHandle) - -#' @rdname mixtureWeights -#' @export -setMethod("mixtureWeights", "LdData", function(x) x@mixtureWeights) - -#' @rdname snpIdx -#' @export -setMethod("snpIdx", "LdData", function(x) x@snpIdx) - -#' @rdname ldPanelNSamples -#' @export -setMethod("ldPanelNSamples", "LdData", function(x) x@nSamples) - -# ---- map/apply helpers (lambda-free callbacks) --------------------------- - -# Sample-LD matrix for one mixture panel's genotype handle (over `snpIdx`). -# @noRd -.ldPanelLd <- function(h, snpIdx) { - computeLd(.ldSourceDosages(h, snpIdx), method = "sample") -} - -# Dosages from a genotype source. A matrix IS the dosages already -- extracted -# and filtered upstream, with `snpIdx` NULL because the matrix is the subset -- -# so it is returned as-is rather than fed to the file readers, which would -# dispatch on it and fail. -# @noRd -.ldSourceDosages <- function(x, snpIdx) { - if (is.matrix(x)) { - return(x) - } - .dosageMatrix(x, snpIdx) -} - -# Every element of a mixture list has to be something LD can be computed from. -# The class union admits any list, so the elements are checked here. -# @noRd -.ldCheckGenotypeSource <- function(x) { - if (!is.list(x)) { - return(character()) - } - ok <- map_lgl(x, .ldIsGenotypeSource) - if (all(ok)) { - return(character()) - } - str_c( - "'genotypeHandle' list elements must each be a genotype panel or a ", - "dosage matrix; element(s) ", - str_flatten(which(!ok), ", "), - " are not." - ) -} - -# @noRd -.ldIsGenotypeSource <- function(x) { - methods::is(x, "GenotypeHandle") || is.matrix(x) -} - -# Block-diagonal LD is one matrix per block. The union admits any list, so the -# elements are checked here -- a list of something else would otherwise sail -# through and fail later inside the LD arithmetic. -# @noRd -.ldCheckCorrelation <- function(x) { - if (!is.list(x)) { - return(character()) - } - ok <- map_lgl(x, is.matrix) - if (all(ok)) { - return(character()) - } - str_c( - "'correlation' list elements must each be a matrix (block-diagonal ", - "LD is one matrix per block); element(s) ", - str_flatten(which(!ok), ", "), - " are not." - ) -} diff --git a/R/LdEigen.R b/R/LdEigen.R index 1d37f3e70..b1daeb3dc 100644 --- a/R/LdEigen.R +++ b/R/LdEigen.R @@ -1,8 +1,9 @@ # ============================================================================= # LdEigen S4 class # ----------------------------------------------------------------------------- -# Pre-computed per-block eigendecompositions of the LD correlation matrix. -# Consumed by LDER / HDL / sHDL h2 estimators. +# Per-block eigendecompositions of the LD correlation matrix: stored for +# LdMatrix input, computed from the genotype panel one block at a time for +# panel input. Consumed by LDER / HDL / sHDL h2 estimators. # ============================================================================= #' @include LdStatistic.R @@ -18,7 +19,16 @@ NULL #' narrow when the ranges do, subsetting an \code{LdEigen} is refused #' rather than allowed to produce an object whose decompositions no longer #' describe its variants. -#' @slot eigenList A list of length \code{nBlocks}, each element a list with +#' +#' An \code{LdEigen} built from a genotype panel stores no +#' decompositions: each block's is computed from the panel's factor (its +#' standardized genotypes) when an estimator reaches the block. That gives +#' the block's nonzero eigenvalues and their eigenvectors; when the panel +#' has fewer samples than the block has variants, the block's remaining +#' eigenvalues are exactly zero, and the estimators account for those +#' directions without listing them. +#' @slot eigenList A list of length \code{nBlocks} (empty for an +#' \code{LdEigen} built from a genotype panel), each element a list with #' components: #' \describe{ #' \item{values}{Numeric vector of eigenvalues} @@ -47,9 +57,11 @@ setClass( if (!isTRUE(parentCheck)) { coll$push(parentCheck) } + # One decomposition per block, or none for an LdEigen that computes them + # from its genotype panel. assertList( object@eigenList, - len = length(object@ldBlocks), + len = if (is.null(object@ldReference)) length(object@ldBlocks) else 0L, .var.name = "eigenList", add = coll ) @@ -117,51 +129,58 @@ LdEigen <- function( } #' @importFrom checkmate assertCount assertFlag -#' @title Build an LdEigen from loaded LD -#' @description Eigendecompose already-loaded LD, block by block, into the +#' @title Build an LdEigen from an LD reference +#' @description Eigendecompose an LD reference, block by block, into the #' \code{LdEigen} that \code{\link{estimateH2}} consumes for #' \code{method = "lder"} and \code{method = "hdl"}. This is the supported #' route from an LD reference to an h2 input: \code{\link{LdEigen}} itself #' is the low-level constructor and expects the decompositions to have been #' computed already. #' -#' No file I/O happens here. Read the LD first with -#' \code{\link{loadLdMatrix}} -- passing a vector of regions returns one -#' \code{LdData} per block -- then pass the result in. -#' @param ldBlockData An \code{\link{LdData}}, or a list of them, covering -#' the reference variants. An \code{LdData} whose correlation is a list of -#' per-block matrices contributes one LD block per matrix; one whose -#' correlation is a single matrix contributes one block. Blocks are kept in -#' the order given, and that order defines the variant order of the result. +#' @param ldReference The LD reference, block by block: a list of +#' \code{\link{LdMatrix}} objects, one per LD block (a single +#' \code{LdMatrix} is one block), or a genotype panel (see +#' \code{\link{readGenotypes}}) together with \code{ldBlocks}. Blocks are +#' kept in the order given, and that order defines the variant order of the +#' result. #' #' Block structure matters downstream: \code{\link{estimateH2}} takes its #' standard error from a delete-one-block jackknife, so it requires at -#' least two blocks and wants many more. Loading a whole region as one -#' dense matrix yields a single block. -#' @param nSamples Integer, the LD reference panel sample size. Defaults to the -#' size recorded by the supplied \code{LdData}; required when they record -#' none, and an error when they disagree. +#' least two blocks and wants many more. +#' @param ldBlocks With a genotype panel, the LD blocks to cut it into: a +#' \code{GRanges}, or a data frame with columns \code{chrom}, \code{start} +#' and \code{end}. Each block's LD is computed from the panel variants +#' inside it; variants outside every block are left out. Not used with +#' \code{LdMatrix} blocks. +#' @param nSamples Integer, the number of samples the reference's LD was +#' estimated from. Defaults to a panel's sample count (a stochastic +#' sketch's effective size), or the size the \code{LdMatrix} blocks record; +#' required when they record none, and an error when they disagree. #' @param inSample Logical, whether the reference is the GWAS cohort itself. #' Selects LDER's weighting and HDL's finite-reference correction. #' @param genome Character, genome build. Defaults to the build recorded by -#' the supplied \code{LdData}, which \code{\link{loadLdMatrix}} leaves -#' unset. +#' the reference's variants. #' @param eigenvalueTruncation Numeric in (0, 1]; the proportion of each #' block's eigenvalue mass to retain. \code{1} (the default) keeps every #' component; HDL conventionally uses \code{0.9}. -#' @return An \code{LdEigen} over every variant in \code{ldBlockData}. +#' @return An \code{LdEigen} over every variant in the reference's blocks. +#' One built from a genotype panel keeps the panel and decomposes each +#' block when it is needed, rather than storing the decompositions. #' @seealso \code{\link{buildLdScore}} for the g-LDSC input, -#' \code{\link{estimateH2}}, \code{\link{loadLdMatrix}} +#' \code{\link{estimateH2}}, \code{\link{LdMatrix}} #' @examples -#' meta <- system.file("extdata", "ld_reference", "ld_meta_file.tsv", -#' package = "pecotmr") -#' ld <- loadLdMatrix(meta, region = "chr22:10000000-19000000") -#' ldEigen <- buildLdEigen(ld, genome = "hg38") +#' panel <- readGenotypes(plink2Prefix = file.path(system.file("extdata", +#' "ld_reference", "chr22", package = "pecotmr"), +#' "protocol_example.LD.chr22")) +#' blocks <- data.frame(chrom = "chr22", start = c(10000000, 15000001), +#' end = c(15000000, 19000000)) +#' ldEigen <- buildLdEigen(panel, ldBlocks = blocks, genome = "hg38") #' ldEigen #' length(eigenList(ldEigen)) #' @export buildLdEigen <- function( - ldBlockData, + ldReference, + ldBlocks = NULL, nSamples = NULL, inSample = FALSE, genome = NA_character_, @@ -169,7 +188,10 @@ buildLdEigen <- function( ) { assertCount(nSamples, positive = TRUE, null.ok = TRUE) assertFlag(inSample) - prep <- .ldRefPrepare(ldBlockData, nSamples, genome) + prep <- .h2RefPrepare(ldReference, ldBlocks, nSamples, genome) + if (!is.null(prep$ldReference)) { + return(.ldEigenFromPanel(prep, inSample, eigenvalueTruncation)) + } eigenList <- map2( prep$blocks, prep$snpIdx, @@ -187,6 +209,28 @@ buildLdEigen <- function( ) } +# An LdEigen over a genotype panel: the panel and each variant's row in it, +# and no decompositions, which are computed a block at a time when needed. +# Built in one step because an empty eigenList is valid only alongside the +# panel it is read from. +# @noRd +.ldEigenFromPanel <- function(prep, inSample, truncation) { + gr <- .ldStatRanges(prep$snpInfo, prep$genome) + S4Vectors::mcols(gr)$panelRow <- prep$panelRow + obj <- methods::new( + "LdEigen", + gr, + ldBlocks = .asLdBlockRanges(prep$ldBlocks), + nSamples = as.integer(prep$nRef), + inSample = isTRUE(inSample), + eigenList = list(), + eigenvalueTruncation = as.numeric(truncation), + ldReference = prep$ldReference + ) + validObject(obj) + obj +} + # One block's decomposition, truncated to the leading components carrying # `truncation` of its eigenvalue mass. # @noRd @@ -226,28 +270,67 @@ buildLdEigen <- function( #' @return Nothing: this method always signals an error. #' @export setMethod("[", "LdEigen", function(x, i, j, ..., drop = TRUE) { + .ldEigenRefuseSubset() +}) + +# head(), tail(), window() and subset() reach the rows through extractROWS() +# without passing `[`, so the refusal is made there as well. +setMethod("extractROWS", "LdEigen", function(x, i) { + .ldEigenRefuseSubset() +}) + +# @noRd +.ldEigenRefuseSubset <- function() { abort(glue( "an LdEigen cannot be subset: its eigendecompositions are per LD ", "block, so narrowing the variants would leave them describing ", "variants that are no longer present. Recompute over the subset." )) -}) +} #' @rdname show-methods #' @export setMethod("show", "LdEigen", function(object) { cat(glue( "LdEigen: {length(object)} SNPs across ", - "{length(object@eigenList)} blocks\n", + "{length(object@ldBlocks)} blocks\n", .trim = FALSE )) cat(sprintf(" Eigenvalue truncation: %.2f\n", object@eigenvalueTruncation)) + if (!is.null(object@ldReference)) { + cat(" Decompositions: computed from the genotype panel when needed\n") + } cat(glue( " Reference N: {object@nSamples}, In-sample: {object@inSample}\n", .trim = FALSE )) }) +# The block records of an LdEigen (see .ldStatLazyBlocks()). +# @noRd +.ldEigenBlocks <- function(x) { + if (length(x@eigenList) > 0L) { + return(map(x@eigenList, .ldEigenStoredBlock)) + } + .ldStatLazyBlocks(x) +} + +# @noRd +.ldEigenStoredBlock <- function(block) { + list(idx = block$snpIdx, eigen = block) +} + +# One block's decomposition, at the truncation the LdEigen records. +# @noRd +.ldEigenBlockAt <- function(block, x) { + .ldStatBlockEigen(block, x@ldReference, x@eigenvalueTruncation) +} + #' @rdname eigenList #' @export -setMethod("eigenList", "LdEigen", function(x) x@eigenList) +setMethod("eigenList", "LdEigen", function(x) { + if (is.null(x@ldReference)) { + return(x@eigenList) + } + map(.ldEigenBlocks(x), .ldEigenBlockAt, x = x) +}) diff --git a/R/LdMatrix.R b/R/LdMatrix.R new file mode 100644 index 000000000..590234958 --- /dev/null +++ b/R/LdMatrix.R @@ -0,0 +1,652 @@ +# ============================================================================= +# LdMatrix S4 class +# ----------------------------------------------------------------------------- +# An LD correlation matrix the caller supplies, over the variants it +# describes. This is one of the two ways LD enters pecotmr. The other is a +# genotype panel (readGenotypes()), which is read lazily from disk; pecotmr +# never reads a pre-computed matrix from disk, so a matrix is built in memory +# by the caller and handed over whole. +# ============================================================================= + +#' @include AllGenerics.R +NULL + +#' @title LD Correlation Matrix +#' @description An \code{LdMatrix} carries one LD correlation matrix together +#' with the variants it describes, and can stand wherever pecotmr takes an +#' LD reference (the \code{ldReference} of a summary-statistics collection, +#' \code{\link{raiss}}, the heritability builders). It \strong{is} a +#' \code{GRanges} over those variants, in the order of the matrix's rows, +#' with metadata columns \code{SNP} (the variant id), \code{A1} (the allele +#' whose dosage the correlations are signed by), \code{A2} and, optionally, +#' \code{AF} (the frequency of \code{A1}). +#' +#' The other kind of LD reference is a genotype panel (see +#' \code{\link{readGenotypes}}), which pecotmr reads from disk on demand. A +#' matrix carries no genotypes, so the steps that need them are not +#' available with one: the panel missingness filter, and RAiSS's +#' singular-value route (RAiSS falls back to the matrix itself). Steps that +#' need the panel's sample size or allele frequencies -- MAF / MAC panel +#' filters, DENTIST, ldsc's unbiased \eqn{r^2}, HDL -- use \code{nSamples} +#' and \code{AF} when they are supplied and refuse otherwise. +#' +#' Validity checks the matrix's shape and labels, that its diagonal is 1, +#' that its entries lie in \eqn{[-1, 1]}, and its symmetry on an evenly +#' spaced subset of entries. It does not check positive semi-definiteness, +#' which costs an eigendecomposition; run \code{\link{checkLd}} for that. +#' +#' @slot correlation The correlation matrix, its dimnames the variant ids. +#' @slot nSamples Integer, the number of independent samples the LD is +#' worth, or \code{NA} when unknown. For one panel this is its sample size. +#' For a mixture it is \eqn{1 / \sum_k w_k^2 / n_k}, the size of a single +#' panel whose LD would be as noisy. +#' @slot mixtureWeights The weights a mixture's components were combined +#' with; empty for a single panel's LD. +#' @seealso \code{\link{LdMatrix}} to build one, \code{\link{checkLd}}. +#' @importClassesFrom GenomicRanges GRanges +#' @importFrom S4Vectors extractROWS normalizeSingleBracketSubscript +#' @export +setClass( + "LdMatrix", + contains = "GRanges", + slots = c( + correlation = "matrix", + nSamples = "integer", + mixtureWeights = "numeric" + ), + prototype = prototype( + correlation = matrix(numeric(0), 0L, 0L), + nSamples = NA_integer_, + mixtureWeights = numeric(0) + ) +) + +setValidity("LdMatrix", function(object) { + problems <- c( + .ldmCheckColumns(object), + .ldmCheckShape(object), + .ldmCheckValues(object@correlation), + .ldmCheckAf(object), + .ldmCheckN(object@nSamples), + .ldmCheckWeights(object@mixtureWeights) + ) + if (length(problems) == 0L) TRUE else problems +}) + +# @noRd +.ldmCheckColumns <- function(object) { + missingCols <- setdiff( + c("SNP", "A1", "A2"), + colnames(S4Vectors::mcols(object)) + ) + if (length(missingCols) == 0L) { + return(character()) + } + str_c( + "the variants carry no ", + str_flatten(missingCols, ", "), + " column(s)." + ) +} + +# The matrix must be square over exactly the object's variants, labelled by +# their ids: every consumer looks LD up by id, so a matrix whose labels drift +# from the variant table would hand back some other variant's LD. +# @noRd +.ldmCheckShape <- function(object) { + R <- object@correlation + p <- length(object) + if (!is.numeric(R) || nrow(R) != p || ncol(R) != p) { + return(glue( + "`correlation` is {nrow(R)} x {ncol(R)} but there are {p} ", + "variant(s); it must be numeric and square over them." + )) + } + # An empty matrix has no labels to check: subsetting to zero variants + # leaves NULL dimnames, which is as labelled as nothing gets. + ids <- as.character(S4Vectors::mcols(object)$SNP) + if (p == 0L) { + return(character()) + } + if ( + !identical(rownames(R), ids) || + !identical(colnames(R), ids) + ) { + return( + "`correlation`'s row and column names must be the variant ids." + ) + } + character() +} + +# A correlation matrix has a unit diagonal, entries in [-1, 1] and is +# symmetric. Symmetry is checked on an evenly spaced block of rows and +# columns: comparing a large matrix with its transpose costs another copy of +# it, and the strided block catches a wrong input just as well. +# @noRd +.ldmCheckValues <- function(R, tol = 1e-6) { + p <- nrow(R) + if (!is.numeric(R) || p == 0L || nrow(R) != ncol(R)) { + return(character()) + } + if (anyNA(R)) { + return("`correlation` holds missing values.") + } + span <- range(R) + problems <- c( + if (span[[1L]] < -1 - tol || span[[2L]] > 1 + tol) { + "`correlation` holds entries outside [-1, 1]." + }, + if (max(abs(diag(R) - 1)) > tol) { + "`correlation`'s diagonal is not 1." + } + ) + probe <- unique(round(seq(1, p, length.out = min(p, 200L)))) + block <- R[probe, probe, drop = FALSE] + if (max(abs(block - t(block))) > tol) { + problems <- c(problems, "`correlation` is not symmetric.") + } + problems +} + +# @noRd +.ldmCheckAf <- function(object) { + af <- S4Vectors::mcols(object)$AF + if (is.null(af)) { + return(character()) + } + if (!is.numeric(af) || any(af < 0 | af > 1, na.rm = TRUE)) { + return("`AF` must be numeric allele frequencies in [0, 1].") + } + character() +} + +# @noRd +.ldmCheckN <- function(n) { + if (length(n) == 1L && (is.na(n) || n >= 1L)) { + return(character()) + } + "`nSamples` must be one positive count, or NA when unknown." +} + +# @noRd +.ldmCheckWeights <- function(w) { + if (length(w) == 0L) { + return(character()) + } + if (anyNA(w) || any(w < 0) || abs(sum(w) - 1) > 1e-8) { + return("`mixtureWeights` must be non-negative and sum to 1.") + } + character() +} + +# Documented on its own page, LdMatrix-constructor.Rd: the default LdMatrix.Rd +# would collide with the ldMatrix() generic's ldMatrix.Rd on a +# case-insensitive filesystem (macOS), and one would overwrite the other. +#' @title Build an LdMatrix +#' @description Wrap an LD correlation matrix, or combine several into a +#' mixture, for use as an LD reference. See \code{\link{LdMatrix-class}}. +#' +#' \strong{One panel's LD.} Pass the matrix and the variants it covers. +#' The variants may be given as a \code{GRanges} (metadata columns +#' \code{A1}, \code{A2}, and optionally \code{SNP} and \code{AF}), as a +#' data frame with columns \code{chrom}, \code{pos}, \code{A1}, \code{A2} +#' (and optionally \code{SNP}, \code{AF}), or omitted when the matrix's +#' dimnames are \code{chrom:pos:A2:A1} ids, from which they are parsed. The +#' correlations must be signed by the dosage of \code{A1}. +#' +#' \strong{A mixture.} Pass a list of \code{LdMatrix} objects and their +#' \code{mixtureWeights}. The result is their weighted average +#' \eqn{\sum_k w_k R_k}, computed once here, over the variants every +#' component covers; a variant some component lacks is left out, with a +#' message. Components are matched by position and alleles and put into +#' the first component's allele coding, so they need not share variant ids +#' or allele orientation. The mixture's \code{AF} is \eqn{\sum_k w_k AF_k} +#' when every component records \code{AF}, and its \code{nSamples} is +#' \eqn{1 / \sum_k w_k^2 / n_k} when every component records a size. +#' +#' @param correlation A numeric correlation matrix, or a list of +#' \code{LdMatrix} objects to combine into a mixture. +#' @param variants The variants the matrix covers, in its row order: a +#' \code{GRanges} or a data frame (see Description), or \code{NULL} to +#' parse them from the matrix's dimnames. Not used for a mixture. +#' @param nSamples The size of the panel the LD was estimated from, or +#' \code{NA} when unknown. Not used for a mixture. +#' @param mixtureWeights For a mixture, one non-negative weight per +#' component, summing to 1. +#' @return An \code{LdMatrix}. +#' @rdname LdMatrix-constructor +#' @examples +#' data(eqtlRegionExample) +#' X <- eqtlRegionExample$X[, 1:8] +#' ld <- LdMatrix(cor(X), nSamples = nrow(X)) +#' ld +#' ldMatrix(ld)[1:3, 1:3] +#' +#' # A mixture of two panels' LD, weighted 3:1. +#' half <- seq_len(nrow(X) / 2) +#' ldA <- LdMatrix(cor(X[half, ]), nSamples = length(half)) +#' ldB <- LdMatrix(cor(X[-half, ]), nSamples = nrow(X) - length(half)) +#' LdMatrix(list(ldA, ldB), mixtureWeights = c(0.75, 0.25)) +#' @importFrom checkmate assertNumber +#' @export +LdMatrix <- function( + correlation, + variants = NULL, + nSamples = NA_integer_, + mixtureWeights = NULL +) { + if (is.list(correlation) && !is.data.frame(correlation)) { + return(.ldmMixture(correlation, mixtureWeights)) + } + if (!is.null(mixtureWeights)) { + abort(c( + "`mixtureWeights` is for combining LD matrices.", + i = "Pass a list of LdMatrix objects as `correlation`." + )) + } + assertNumber(nSamples, na.ok = TRUE, lower = 1) + R <- .ldmAsMatrix(correlation) + gr <- .ldmVariants(variants, R) + .ldmNew(gr, .ldmLabelled(R, gr), nSamples, numeric(0)) +} + +# @noRd +.ldmNew <- function(gr, R, nSamples, mixtureWeights) { + obj <- methods::new( + "LdMatrix", + gr, + correlation = R, + nSamples = as.integer(round(nSamples)), + mixtureWeights = as.numeric(mixtureWeights) + ) + validObject(obj) + obj +} + +# A plain numeric matrix: a Matrix-package matrix is made dense, as every +# consumer works on one. +# @noRd +#' @importFrom checkmate assertMatrix +.ldmAsMatrix <- function(correlation) { + if (methods::is(correlation, "Matrix")) { + correlation <- as.matrix(correlation) + } + assertMatrix( + correlation, + mode = "numeric", + any.missing = FALSE, + .var.name = "correlation" + ) + correlation +} + +# The matrix labelled by the variant ids. A matrix that already carries +# labels must carry exactly these, in this order: a mismatch means the matrix +# and the variant table describe different variants, or the same ones in a +# different order. +# @noRd +.ldmLabelled <- function(R, gr) { + ids <- as.character(S4Vectors::mcols(gr)$SNP) + if (nrow(R) != length(ids) || ncol(R) != length(ids)) { + abort(glue( + "`correlation` is {nrow(R)} x {ncol(R)} but `variants` lists ", + "{length(ids)} variant(s)." + )) + } + labelled <- !is.null(rownames(R)) || !is.null(colnames(R)) + if ( + labelled && + (!identical(rownames(R), ids) || !identical(colnames(R), ids)) + ) { + abort(c( + "`correlation`'s dimnames do not match the variant ids.", + i = str_c( + "Order the matrix as the variants are ordered, or drop its ", + "dimnames to label it by `variants`." + ) + )) + } + `dimnames<-`(R, list(ids, ids)) +} + +# The variant table, normalised to the GRanges an LdMatrix is: chr-prefixed +# seqnames (as a genotype panel's), names and `SNP` the ids, `A1` / `A2`, and +# `AF` when supplied. +# @noRd +.ldmVariants <- function(variants, R) { + tbl <- if (is.null(variants)) { + .ldmVariantsFromIds(rownames(R)) + } else if (methods::is(variants, "GRanges")) { + .ldmVariantsFromRanges(variants, rownames(R)) + } else if (is.data.frame(variants)) { + .ldmVariantsFromTable(variants, rownames(R)) + } else { + abort(glue( + "`variants` must be a GRanges, a data frame or NULL ", + "(got {class(variants)[[1L]]})." + )) + } + .ldmRangesOf(tbl) +} + +# @noRd +.ldmVariantsFromIds <- function(ids) { + if (is.null(ids)) { + abort(c( + "`variants` is required when the matrix has no dimnames.", + i = "Pass the variants, or label the matrix chrom:pos:A2:A1." + )) + } + parsed <- parseVariantId(ids) + if (anyNA(parsed$pos) || anyNA(parsed$A1)) { + abort(c( + "The matrix's dimnames are not chrom:pos:A2:A1 variant ids.", + i = "Pass `variants` to say which variants it covers." + )) + } + tibble( + SNP = ids, + chrom = parsed$chrom, + pos = parsed$pos, + A1 = parsed$A1, + A2 = parsed$A2 + ) +} + +# @noRd +.ldmVariantsFromRanges <- function(gr, ids) { + mc <- as.data.frame(S4Vectors::mcols(gr)) + .ldmVariantsFromTable( + mutate( + as_tibble(mc), + chrom = as.character(GenomicRanges::seqnames(gr)), + pos = GenomicRanges::start(gr), + SNP = .ldmIdsOf(mc, names(gr), ids) + ), + ids + ) +} + +# @noRd +.ldmVariantsFromTable <- function(tbl, ids) { + missingCols <- setdiff(c("chrom", "pos", "A1", "A2"), colnames(tbl)) + if (length(missingCols) > 0L) { + abort(glue( + "`variants` lacks column(s) {str_flatten(missingCols, ', ')}." + )) + } + snp <- if (is_in("SNP", colnames(tbl))) { + as.character(tbl$SNP) + } else { + .ldmIdsOf(tbl, NULL, ids) + } + out <- tibble( + SNP = snp %||% + formatVariantId( + canonChrom(as.character(tbl$chrom)), + as.integer(tbl$pos), + as.character(tbl$A2), + as.character(tbl$A1) + ), + chrom = as.character(tbl$chrom), + pos = as.integer(tbl$pos), + A1 = as.character(tbl$A1), + A2 = as.character(tbl$A2) + ) + if (is_in("AF", colnames(tbl))) { + mutate(out, AF = as.numeric(tbl$AF)) + } else { + out + } +} + +# The ids a variant table names, in order of preference: its own SNP or +# variant_id column, then the ranges' names, then the matrix's dimnames. +# NULL when none of those exist. +# @noRd +.ldmIdsOf <- function(tbl, rangeNames, ids) { + if (is_in("SNP", colnames(tbl))) { + return(as.character(tbl$SNP)) + } + if (is_in("variant_id", colnames(tbl))) { + return(as.character(tbl$variant_id)) + } + if (!is.null(rangeNames)) { + return(as.character(rangeNames)) + } + ids +} + +# @noRd +.ldmRangesOf <- function(tbl) { + if (anyDuplicated(tbl$SNP) > 0L) { + abort("The variant ids must be unique.") + } + gr <- GenomicRanges::GRanges( + seqnames = withChrPrefix(as.character(tbl$chrom)), + ranges = IRanges::IRanges(as.integer(tbl$pos), width = 1L) + ) + S4Vectors::`mcols<-`( + `names<-`(gr, tbl$SNP), + value = S4Vectors::DataFrame( + select(tbl, -all_of(c("chrom", "pos"))), + row.names = NULL + ) + ) +} + +# ---- mixtures --------------------------------------------------------------- + +# The weighted average of several LD matrices, over the variants they all +# cover, in the first component's allele coding. +# @noRd +.ldmMixture <- function(components, mixtureWeights) { + .ldmAssertComponents(components) + w <- .ldmAssertWeights(mixtureWeights, length(components)) + ref <- components[[1L]] + matched <- map(components, .ldmMatchTo, ref = ref) + common <- sort(reduce(map(matched, "refIdx"), intersect)) + if (length(common) == 0L) { + abort("The LD matrices share no variant to combine over.") + } + .ldmReportDropped(length(ref) - length(common), length(common)) + aligned <- map(matched, .ldmAlignedOn, common = common) + R <- reduce( + seq_along(components), + .ldmAddComponent, + .init = NULL, + components = components, + aligned = aligned, + w = w + ) + ids <- rownames(ref@correlation)[common] + gr <- .ldmMixtureVariants(ref[common], components, aligned, w) + obj <- methods::new( + "LdMatrix", + gr, + correlation = `dimnames<-`(R, list(ids, ids)), + nSamples = .ldmMixtureN(map_int(components, nSamples), w), + mixtureWeights = w + ) + validObject(obj) + obj +} + +# @noRd +.ldmAssertComponents <- function(components) { + isLd <- map_lgl(components, methods::is, class2 = "LdMatrix") + if (length(components) < 2L || !all(isLd)) { + abort(c( + "A mixture needs a list of at least two LdMatrix objects.", + i = str_c( + "Build each component with LdMatrix(), then combine them ", + "with LdMatrix(list(...), mixtureWeights = ...)." + ) + )) + } +} + +# @noRd +.ldmAssertWeights <- function(w, k) { + if ( + is.null(w) || + !is.numeric(w) || + length(w) != k || + anyNA(w) || + any(w < 0) || + abs(sum(w) - 1) > 1e-8 + ) { + abort(glue( + "`mixtureWeights` must be {k} non-negative weights summing to 1, ", + "one per component." + )) + } + as.numeric(w) +} + +# Where each of `ref`'s variants sits in `x`, and with which allele sign. +# @noRd +.ldmMatchTo <- function(x, ref) { + m <- matchVariants( + .ldRefMatchIds(ref), + .ldRefMatchIds(x), + removeStrandAmbiguous = FALSE + ) + list(refIdx = m$idxA, idx = m$idxB, sign = m$sign) +} + +# A component's positions and signs for the shared variants, in `ref` order. +# @noRd +.ldmAlignedOn <- function(matched, common) { + at <- match(common, matched$refIdx) + list(idx = matched$idx[at], sign = matched$sign[at]) +} + +# A component's matrix over the shared variants, re-signed into the first +# component's allele coding: a swapped variant's dosage counts the other +# allele, which negates each of its correlations. +# @noRd +.ldmAlignedR <- function(x, aligned) { + R <- unname(x@correlation[aligned$idx, aligned$idx, drop = FALSE]) + if (all(aligned$sign > 0)) { + return(R) + } + R * outer(aligned$sign, aligned$sign) +} + +# Add the k-th component's weighted, aligned matrix to the running sum, so only +# one aligned copy exists at a time rather than every component's at once. +# @noRd +.ldmAddComponent <- function(acc, k, components, aligned, w) { + term <- w[[k]] * .ldmAlignedR(components[[k]], aligned[[k]]) + if (is.null(acc)) term else acc + term +} + +# The mixture's variant table: the first component's, with AF the weighted +# average of the components' (each put into the first component's coding) +# when every component records one. +# @noRd +.ldmMixtureVariants <- function(gr, components, aligned, w) { + afs <- map2(components, aligned, .ldmAlignedAf) + mc <- S4Vectors::mcols(gr) + mc$AF <- if (any(map_lgl(afs, is.null))) { + NULL + } else { + reduce(map2(w, afs, `*`), `+`) + } + S4Vectors::`mcols<-`(gr, value = mc) +} + +# @noRd +.ldmAlignedAf <- function(x, aligned) { + af <- S4Vectors::mcols(x)$AF + if (is.null(af)) { + return(NULL) + } + af <- as.numeric(af)[aligned$idx] + if_else(aligned$sign > 0, af, 1 - af) +} + +# 1 / sum(w^2 / n): the size of one panel whose LD is as noisy as the mixture. +# @noRd +.ldmMixtureN <- function(n, w) { + if (anyNA(n)) { + return(NA_integer_) + } + as.integer(round(1 / sum(w^2 / n))) +} + +# @noRd +.ldmReportDropped <- function(nDropped, nKept, what = "LdMatrix") { + if (nDropped == 0L) { + return(invisible(NULL)) + } + inform(glue( + "{what}: {nDropped} variant(s) of the first component are not in ", + "every component and are left out; the mixture covers {nKept}." + )) +} + +# ---- subsetting ------------------------------------------------------------- + +# Subsetting the variants subsets the matrix with them, so the two never fall +# out of step. +setMethod("extractROWS", "LdMatrix", function(x, i) { + i <- normalizeSingleBracketSubscript(i, x) + ans <- methods::callNextMethod(x, i) + ans@correlation <- x@correlation[i, i, drop = FALSE] + ans +}) + +# ---- accessors -------------------------------------------------------------- + +#' @rdname ldMatrix +#' @export +setMethod("ldMatrix", "LdMatrix", function(x) x@correlation) + +#' @rdname nSamples +#' @export +setMethod("nSamples", "LdMatrix", function(x, ...) x@nSamples) + +#' @rdname mixtureWeights +#' @export +setMethod("mixtureWeights", "LdMatrix", function(x) { + if (length(x@mixtureWeights) == 0L) NULL else x@mixtureWeights +}) + +#' @rdname variantIds +#' @export +setMethod("variantIds", "LdMatrix", function(x, ...) { + as.character(S4Vectors::mcols(x)$SNP) +}) + +#' @rdname af +#' @export +setMethod("af", "LdMatrix", function(x, ...) { + af <- S4Vectors::mcols(x)$AF + ids <- as.character(S4Vectors::mcols(x)$SNP) + set_names(if (is.null(af)) rep(NA_real_, length(ids)) else af, ids) +}) + +#' @rdname LdMatrix-class +#' @param object An \code{LdMatrix}. +#' @export +setMethod("show", "LdMatrix", function(object) { + n <- if (is.na(object@nSamples)) "unknown" else object@nSamples + cat(glue( + "LdMatrix: {length(object)} variants, panel size {n}\n", + .trim = FALSE + )) + w <- object@mixtureWeights + if (length(w) > 0L) { + cat(glue( + " mixture of {length(w)} panels, weights ", + "{str_flatten(signif(w, 3), ', ')}\n", + .trim = FALSE + )) + } + if (!is_in("AF", colnames(S4Vectors::mcols(object)))) { + cat(" no allele frequencies\n") + } +}) diff --git a/R/LdScore.R b/R/LdScore.R index 80223afc8..6442a3068 100644 --- a/R/LdScore.R +++ b/R/LdScore.R @@ -1,9 +1,10 @@ # ============================================================================= # LdScore S4 class # ----------------------------------------------------------------------------- -# Pre-computed LD scores (sum of r^2) per SNP. Consumed by S-LDSC and -# g-LDSC. Holds the optional per-block LD matrices needed for g-LDSC's -# FGLS residual covariance. +# Pre-computed LD scores (sum of ldsc's unbiased r^2) per SNP. Consumed by +# S-LDSC and g-LDSC. g-LDSC's FGLS residual covariance also needs each block's +# LD: stored per block for LdMatrix input, read from the genotype panel on +# demand for panel input. # ============================================================================= #' @include LdStatistic.R @@ -17,8 +18,17 @@ NULL #' slots, because they are parallel to the variants: as slots they would #' survive a subset unchanged while the ranges narrowed, leaving scores #' describing variants that are no longer there. -#' @slot ldMatrixList For g-LDSC: a list of per-block LD (R^2) matrices used to -#' compute the FGLS residual covariance. Empty for S-LDSC. +#' +#' An \code{LdScore} storing per-block LD matrices (\code{ldMatrixList}) +#' cannot be subset or reordered: each block's matrix indexes its variants +#' by position, so narrowing the variants would leave it describing other +#' variants. A scores-only object, or one reading its LD from a genotype +#' panel, subsets freely. +#' @slot ldMatrixList For g-LDSC: a list of per-block LD correlation (R) +#' matrices used to compute the FGLS residual covariance, kept when the +#' scores were built from \code{LdMatrix} blocks. Empty for a scores-only +#' object, and for one built from a genotype panel, which reads each +#' block's LD from the panel instead (see \code{\linkS4class{LdStatistic}}). #' @export setClass( "LdScore", @@ -50,7 +60,8 @@ setClass( #' @param snpInfo A \code{data.frame} with columns \code{SNP}, \code{CHR}, #' \code{BP}, \code{A1}, \code{A2} (and optionally \code{MAF}). #' @param ldScores A numeric matrix, one row per variant. The first column is -#' the base LD score (sum of r^2); further columns are +#' the base LD score (sum of r^2, which \code{\link{buildLdScore}} takes as +#' ldsc's unbiased r^2, as ldsc's own score files do); further columns are #' annotation-stratified scores. #' @param ldScoreWeights Numeric regression weights, one per variant. #' @param ldBlocks A \code{GRanges} of LD block intervals. @@ -109,56 +120,71 @@ LdScore <- function( } #' @importFrom checkmate assertCount assertFlag -#' @title Build an LdScore from loaded LD -#' @description Compute per-variant LD scores from already-loaded LD, block by +#' @title Build an LdScore from an LD reference +#' @description Compute per-variant LD scores from an LD reference, block by #' block, into the \code{LdScore} that \code{\link{estimateH2}} consumes #' for \code{method = "gldsc"}. This is the supported route from an LD #' reference to an h2 input: \code{\link{LdScore}} itself is the low-level #' constructor and expects the scores to have been computed already. #' -#' The score is \eqn{\ell_j = \sum_k r^2_{jk}} within each block, the same -#' quantity \code{\link{computeLdScores}} reconstructs from an -#' \code{\link{LdEigen}}, so the two agree on a shared reference. +#' The score is \eqn{\ell_j = \sum_k r^2_{jk}} within each block, with +#' each term ldsc's unbiased estimate of \eqn{r^2} (Bulik-Sullivan et al. +#' 2015, \code{ldscore.py}): \code{r^2 - (1 - r^2) / (n - 2)}, where +#' \code{n} is the reference panel sample size. A sample \eqn{r^2} +#' overstates the population value by about \code{(1 - r^2) / (n - 2)}, +#' and a block sums many of them. A variant's own term stays 1, and +#' negative estimates are kept, as in ldsc. \code{\link{computeLdScores}} +#' gives the same scores from an \code{\link{LdEigen}} built on the same +#' reference. #' -#' No file I/O happens here. Read the LD first with -#' \code{\link{loadLdMatrix}} -- passing a vector of regions returns one -#' \code{LdData} per block -- then pass the result in. -#' @param ldBlockData An \code{\link{LdData}}, or a list of them, covering -#' the reference variants. An \code{LdData} whose correlation is a list of -#' per-block matrices contributes one LD block per matrix; one whose -#' correlation is a single matrix contributes one block. Blocks are kept in -#' the order given, and that order defines the variant order of the result. +#' @param ldReference The LD reference, block by block: a list of +#' \code{\link{LdMatrix}} objects, one per LD block (a single +#' \code{LdMatrix} is one block), or a genotype panel (see +#' \code{\link{readGenotypes}}) together with \code{ldBlocks}. Blocks are +#' kept in the order given, and that order defines the variant order of the +#' result. #' #' Block structure matters downstream: \code{\link{estimateH2}} takes its #' standard error from a delete-one-block jackknife, so it requires at -#' least two blocks and wants many more. Loading a whole region as one -#' dense matrix yields a single block. -#' @param nSamples Integer, the LD reference panel sample size. Defaults to the -#' size recorded by the supplied \code{LdData}; required when they record -#' none, and an error when they disagree. +#' least two blocks and wants many more. +#' @param ldBlocks With a genotype panel, the LD blocks to cut it into: a +#' \code{GRanges}, or a data frame with columns \code{chrom}, \code{start} +#' and \code{end}. Each block's LD is computed from the panel variants +#' inside it; variants outside every block are left out. Not used with +#' \code{LdMatrix} blocks. +#' @param nSamples Integer, the number of samples the reference's LD was +#' estimated from. Defaults to a panel's sample count (a stochastic +#' sketch's effective size), or the size the \code{LdMatrix} blocks record; +#' required when they record none, and an error when they disagree. It is +#' also the \code{n} of the unbiased \eqn{r^2}, and it must exceed 2. #' @param inSample Logical, whether the reference is the GWAS cohort itself. #' @param genome Character, genome build. Defaults to the build recorded by -#' the supplied \code{LdData}, which \code{\link{loadLdMatrix}} leaves -#' unset. +#' the reference's variants. #' @param ldScoreWeights Optional numeric regression weights, one per variant. #' Defaults to the conventional \eqn{1/\max(\ell_j, 1)}. -#' @param keepLdMatrices Logical. Keep each block's LD matrix on the result. -#' \code{TRUE} (the default) is required for \code{method = "gldsc"}, -#' which forms its FGLS residual covariance from them and errors without -#' them. \code{FALSE} drops them, leaving a scores-only object. -#' @return An \code{LdScore} over every variant in \code{ldBlockData}. +#' @param keepLdMatrices Logical, for \code{LdMatrix} blocks: keep each +#' block's LD matrix on the result. \code{TRUE} (the default) is required +#' for \code{method = "gldsc"} and for annotation-stratified scores, which +#' need each block's LD. \code{FALSE} drops them, leaving a scores-only +#' object. A genotype panel is never copied into matrices: the result keeps +#' the panel and reads each block's LD from it when it is needed, so it +#' serves every method either way. +#' @return An \code{LdScore} over every variant in the reference's blocks. #' @seealso \code{\link{buildLdEigen}} for the LDER / HDL input, #' \code{\link{estimateH2}}, \code{\link{computeLdScores}} #' @examples -#' meta <- system.file("extdata", "ld_reference", "ld_meta_file.tsv", -#' package = "pecotmr") -#' ld <- loadLdMatrix(meta, region = "chr22:10000000-19000000") -#' ldScore <- buildLdScore(ld, genome = "hg38") +#' panel <- readGenotypes(plink2Prefix = file.path(system.file("extdata", +#' "ld_reference", "chr22", package = "pecotmr"), +#' "protocol_example.LD.chr22")) +#' blocks <- data.frame(chrom = "chr22", start = c(10000000, 15000001), +#' end = c(15000000, 19000000)) +#' ldScore <- buildLdScore(panel, ldBlocks = blocks, genome = "hg38") #' ldScore #' head(S4Vectors::mcols(ldScore)$ldScores) #' @export buildLdScore <- function( - ldBlockData, + ldReference, + ldBlocks = NULL, nSamples = NULL, inSample = FALSE, genome = NA_character_, @@ -168,14 +194,14 @@ buildLdScore <- function( assertCount(nSamples, positive = TRUE, null.ok = TRUE) assertFlag(inSample) assertFlag(keepLdMatrices) - prep <- .ldRefPrepare(ldBlockData, nSamples, genome) - l2 <- .ldScoreVector(prep$blocks, prep$snpIdx, nrow(prep$snpInfo)) - ldMatrixList <- if (isTRUE(keepLdMatrices)) { + prep <- .h2RefPrepare(ldReference, ldBlocks, nSamples, genome) + l2 <- .ldScoreVector(prep, .ldscAssertN(prep$nRef)) + ldMatrixList <- if (isTRUE(keepLdMatrices) && is.null(prep$ldReference)) { map2(prep$blocks, prep$snpIdx, .ldScoreKeepMatrix) } else { list() } - LdScore( + scores <- LdScore( snpInfo = prep$snpInfo, ldScores = matrix(l2, ncol = 1, dimnames = list(NULL, "base_l2")), ldScoreWeights = .ldScoreResolveWeights(ldScoreWeights, l2), @@ -185,27 +211,78 @@ buildLdScore <- function( genome = prep$genome, ldMatrixList = ldMatrixList ) + .ldStatAttachReference(scores, prep) +} + +# Per-variant sum of ldsc's unbiased r^2 within the variant's own block, in +# reference order. The blocks partition the variants, which are stacked +# block by block, so the blocks' sums concatenate into place; a genotype +# panel's blocks are read one at a time. +# @noRd +.ldScoreVector <- function(prep, nRef) { + blocks <- map2(prep$blocks, prep$snpIdx, .ldScoreBuildBlock) + list_c(map( + blocks, + .ldScoreBlockSums, + ldReference = prep$ldReference, + nRef = nRef + )) +} + +# A block of the reference being built, as the record the block readers take. +# @noRd +.ldScoreBuildBlock <- function(block, snpIdx) { + list(idx = snpIdx, R = block$R, rows = block$rows) +} + +# One block's base LD scores: the raw sums of r^2 over the block, corrected to +# ldsc's unbiased r^2 by its summed form. +# @noRd +.ldScoreBlockSums <- function(block, ldReference, nRef) { + ld <- .ldStatBlockLd(block, ldReference) + p <- .ldSourceSize(ld) + raw <- .ldSquaredProduct(ld, matrix(1, p, 1L)) + as.vector(.ldscUnbiasedSum(raw, p, nRef)) } -# Per-variant sum of r^2 within the variant's own block, scattered back into -# reference order. +# ldsc's unbiased estimator of r^2 (ldscore.py, __l2_unbiased__): a sample +# r^2 overstates the population value by about (1 - r^2) / (n - 2). The self +# term (r^2 = 1) stays 1, and negative estimates are kept, as in ldsc. Every +# LD score pecotmr computes goes through this or its summed form below. # @noRd -.ldScoreVector <- function(blocks, snpIdx, nVariants) { - if (length(blocks) == 0L) { - return(numeric(nVariants)) +.ldscUnbiasedR2 <- function(r2, nRef) { + r2 - (1 - r2) / (nRef - 2) +} + +# .ldscUnbiasedR2() summed over a block with weights a_k: +# sum_k a_k (r2_jk - (1 - r2_jk) / (n - 2)) +# = ((n - 1) * sum_k a_k r2_jk - sum_k a_k) / (n - 2). +# Exact, so a route that has the raw weighted sums but not r^2 itself -- an +# eigendecomposition -- corrects them without rebuilding the matrix. +# `rawSums` is variants x columns; `weightSums` holds each column's sum_k a_k +# over the block, which for the unweighted base score is the block size. +# @noRd +.ldscUnbiasedSum <- function(rawSums, weightSums, nRef) { + sweep((nRef - 1) * rawSums, 2L, weightSums) / (nRef - 2) +} + +# ldsc's unbiased r^2 divides by n - 2, so the panel must exceed 2 samples. +# @noRd +.ldscAssertN <- function(nRef) { + if (nRef > 2L) { + return(nRef) } - # The blocks partition the variants, so each variant's score lands in - # exactly one place; variants in no block stay zero. - replace( - numeric(nVariants), - list_c(snpIdx), - list_c(map(blocks, .ldScoreBlockSums)) - ) + abort(glue( + "ldsc's unbiased r^2 divides by n - 2, so the LD reference panel ", + "needs more than 2 samples, and it records {nRef}. Give the panel's ", + "size as `nSamples`." + )) } +# The n of ldsc's unbiased r^2 for a built LD reference: its panel size. # @noRd -.ldScoreBlockSums <- function(block) { - rowSums(block$R^2) +.ldscPanelN <- function(ldRef) { + .ldscAssertN(ldPanelNSamples(ldRef)) } # @noRd @@ -233,17 +310,76 @@ setMethod("show", "LdScore", function(object) { "{ncol(scores)} LD score columns\n", .trim = FALSE )) - cat(glue( - " Full LD matrices: {length(object@ldMatrixList) > 0} ", - "(needed for g-LDSC)\n", - .trim = FALSE - )) + cat(glue(" Block LD: {.ldScoreLdSource(object)}\n", .trim = FALSE)) cat(glue( " Reference N: {object@nSamples}, In-sample: {object@inSample}\n", .trim = FALSE )) }) +# Where an LdScore's per-block LD comes from, as show() describes it. +# @noRd +.ldScoreLdSource <- function(x) { + if (!is.null(x@ldReference)) { + return("read from the genotype panel when needed") + } + if (length(x@ldMatrixList) > 0L) { + return("stored matrices") + } + "none (scores only; g-LDSC and stratified scores need it)" +} + +# TRUE when each block's LD can be had: stored, or read from the panel. +# @noRd +.ldScoreHasLd <- function(x) { + !is.null(x@ldReference) || length(x@ldMatrixList) > 0L +} + +# The block records of an LdScore (see .ldStatLazyBlocks()). +# @noRd +.ldScoreBlocks <- function(x) { + if (length(x@ldMatrixList) > 0L) { + return(map(x@ldMatrixList, .ldScoreStoredBlock)) + } + .ldStatLazyBlocks(x) +} + +# @noRd +.ldScoreStoredBlock <- function(block) { + list(idx = block$snpIdx, R = block$R) +} + +# ---- subsetting ------------------------------------------------------------- + +# Stored block matrices index their variants by position (`snpIdx`), so a +# subset or reorder would leave them describing other variants. Without them +# each block's variants are found afresh from the ranges, so the subset is +# safe. Every subset -- `[`, head(), tail(), window(), subset() -- reaches the +# rows through extractROWS(). +setMethod("extractROWS", "LdScore", function(x, i) { + if (length(x@ldMatrixList) > 0L) { + abort(glue( + "an LdScore storing per-block LD matrices cannot be subset: each ", + "block's matrix indexes its variants by position, so narrowing ", + "or reordering the variants would leave it describing other ", + "variants. Rebuild the scores over the subset instead." + )) + } + methods::callNextMethod() +}) + #' @rdname ldMatrixList #' @export -setMethod("ldMatrixList", "LdScore", function(x) x@ldMatrixList) +setMethod("ldMatrixList", "LdScore", function(x) { + if (is.null(x@ldReference)) { + return(x@ldMatrixList) + } + map(.ldScoreBlocks(x), .ldScorePanelMatrix, ldReference = x@ldReference) +}) + +# One block's LD matrix computed from the panel an LdScore reads it from. +# @noRd +.ldScorePanelMatrix <- function(block, ldReference) { + F <- .ldRefFactor(ldReference, block$rows) + list(R = crossprod(F) / (nrow(F) - 1), snpIdx = block$idx) +} diff --git a/R/LdStatistic.R b/R/LdStatistic.R index c324cf174..bcd87248b 100644 --- a/R/LdStatistic.R +++ b/R/LdStatistic.R @@ -7,7 +7,7 @@ # LD-score-based methods (S-LDSC/g-LDSC). # ============================================================================= -#' @include AllGenerics.R LdBlocks.R +#' @include AllGenerics.R AllClasses.R LdBlocks.R NULL #' @title LD Statistic (Virtual Base Class) @@ -21,10 +21,21 @@ NULL #' live in \code{mcols} too, so subsetting narrows the ranges and the #' statistics together. The genome build comes from \code{seqinfo()} rather #' than a slot of its own. +#' +#' A statistic built from a genotype panel (or a +#' \code{\link{GenotypeMixture}}) keeps that reference rather than its +#' blocks' LD: each block's LD is read from the panel when an estimator +#' reaches the block, so only one block's is ever in memory, and +#' \code{mcols()} records each variant's row in the panel as +#' \code{panelRow}. One built from \code{\link{LdMatrix}} blocks stores +#' what it needs of their LD instead. #' @slot ldBlocks A \code{GRanges} of LD block intervals. #' @slot nSamples Integer, sample size of the LD reference panel. #' @slot inSample Logical, whether the LD reference is from the same cohort as #' the GWAS (affects bias correction). +#' @slot ldReference The genotype panel or \code{GenotypeMixture} the +#' statistic reads its blocks' LD from, or \code{NULL} when it was built +#' from \code{LdMatrix} blocks. #' @export setClass( "LdStatistic", @@ -32,8 +43,10 @@ setClass( representation( ldBlocks = "GRanges", nSamples = "integer", - inSample = "logical" + inSample = "logical", + ldReference = "LdReference_OR_NULL" ), + prototype(ldReference = NULL), validity = function(object) .validateLdStatistic(object) ) @@ -53,9 +66,41 @@ setClass( if (length(object) == 0L) { coll$push("an LdStatistic must carry at least one variant") } + referenceCheck <- .ldStatReferenceCheck(object) + if (!isTRUE(referenceCheck)) { + coll$push(referenceCheck) + } coll$getMessages() } +# A statistic read from a genotype reference finds each variant there by its +# `panelRow`, so every variant needs one, and it must be a row of the panel. +# @noRd +.ldStatReferenceCheck <- function(object) { + ref <- object@ldReference + if (is.null(ref)) { + return(TRUE) + } + if (!.ldIsGenotypeRef(ref)) { + return(str_c( + "`ldReference` must be a genotype panel or a GenotypeMixture: ", + "LdMatrix blocks are stored, not read on demand" + )) + } + rows <- S4Vectors::mcols(object, use.names = FALSE)$panelRow + nRows <- length(.ldRefRanges(ref)) + valid <- is.integer(rows) && + !anyNA(rows) && + all(rows >= 1L & rows <= nRows) + if (valid) { + return(TRUE) + } + str_c( + "a statistic read from `ldReference` needs an integer `panelRow` ", + "column naming each variant's row in it" + ) +} + # The variants of an LD reference, as the GRanges every subclass is built on. # Shared by the LdScore() and LdEigen() constructors so the two agree on what # a reference panel's variant table looks like. @@ -98,146 +143,161 @@ setClass( } # ============================================================================= -# Building an LD statistic from already-loaded LD +# Building an LD statistic from an LD reference # ----------------------------------------------------------------------------- -# buildLdEigen() and buildLdScore() both start from LD that has already been -# read -- one or more LdData, as loadLdMatrix() returns them -- -# and differ only in what they compute per block. Everything before that -# (splitting into blocks, assembling the variant table, resolving nRef and the -# genome build) is shared, so it lives here next to .ldStatRanges(). +# buildLdEigen() and buildLdScore() both start from an LD reference -- one +# LdMatrix per LD block, or a genotype panel cut at the blocks' spans -- and +# differ only in what they compute per block. Everything before that +# (splitting into blocks, assembling the variant table, resolving nRef and +# the genome build) is shared, so it lives here next to .ldStatRanges(). # ============================================================================= # Shared front half of buildLdEigen()/buildLdScore(): validate the input, -# split it into blocks, and resolve what the LdStatistic base needs. +# split it into blocks, and resolve what the LdStatistic base needs. A +# genotype reference is kept, with each variant's row in it, for the +# statistic to read its blocks from. # @noRd -.ldRefPrepare <- function(ldBlockData, nSamples, genome) { - dataList <- .ldRefAsDataList(ldBlockData) - blocks <- list_flatten(map(dataList, .ldRefBlocksOf)) - resolvedGenome <- .ldRefResolveGenome(dataList, genome) +.h2RefPrepare <- function(ldReference, ldBlocks, nSamples, genome) { + blocks <- .h2RefBlocks(ldReference, ldBlocks) + resolvedGenome <- .h2RefResolveGenome(ldReference, genome) + lazy <- .ldIsGenotypeRef(ldReference) list( blocks = blocks, - snpInfo = .ldRefSnpInfo(blocks), - snpIdx = .ldRefSnpIdx(blocks), - ldBlocks = .ldRefBlockRanges(blocks, resolvedGenome), - nRef = .ldRefResolveNRef(dataList, nSamples), - genome = resolvedGenome + snpInfo = .h2RefSnpInfo(blocks), + snpIdx = .h2RefSnpIdx(blocks), + ldBlocks = .h2RefBlockRanges(blocks, resolvedGenome), + nRef = .h2RefResolveNRef(ldReference, nSamples), + genome = resolvedGenome, + ldReference = if (lazy) ldReference, + panelRow = if (lazy) list_c(map(blocks, "rows")) ) } -# Accept a single LdData as well as a list of them. +# The reference's LD blocks: list(R, gr) per LdMatrix, or list(rows, gr) per +# block a genotype panel is cut into at `ldBlocks` -- the panel rows whose LD +# is computed when it is needed, not here. # @noRd -.ldRefAsDataList <- function(ldBlockData) { - if (is(ldBlockData, "LdData")) { - return(list(ldBlockData)) +.h2RefBlocks <- function(ldReference, ldBlocks) { + if (.ldIsGenotypeRef(ldReference)) { + return(.h2RefPanelBlocks(ldReference, ldBlocks)) } - if (!is.list(ldBlockData) || length(ldBlockData) == 0L) { - abort(glue( - "`ldBlockData` must be an LdData or a non-empty list of them ", - "(got {class(ldBlockData)[[1L]]})." + if (!is.null(ldBlocks)) { + abort(c( + "`ldBlocks` cuts a genotype panel into LD blocks.", + i = "A list of LdMatrix objects is already one block per element." )) } - bad <- which(!map_lgl(ldBlockData, is, class2 = "LdData")) - if (length(bad) > 0L) { - abort(glue( - "`ldBlockData` element(s) {str_flatten(bad, ', ')} are not ", - "LdData objects." + map(.h2RefMatrixList(ldReference), .h2RefMatrixBlock) +} + +# A single LdMatrix is one block; a list must hold LdMatrix objects only. +# @noRd +.h2RefMatrixList <- function(ldReference) { + if (.ldIsMatrixRef(ldReference)) { + return(list(ldReference)) + } + isList <- is.list(ldReference) && + length(ldReference) > 0L && + all(map_lgl(ldReference, .ldIsMatrixRef)) + if (!isList) { + abort(c( + str_c( + "`ldReference` must be a genotype panel, an LdMatrix, or a ", + "non-empty list of LdMatrix objects (one per LD block)." + ), + i = glue("Got {class(ldReference)[[1L]]}.") )) } - ldBlockData + ldReference } -# The per-block LD matrices of one LdData, each paired with the variants it -# describes. An LdData holds either a single matrix (one block) or a list of -# them (block-diagonal LD across a multi-block region). # @noRd -.ldRefBlocksOf <- function(x) { - R <- ldMatrix(x) - gr <- variantInfo(x) - if (!is.list(R)) { - return(list(.ldRefOneBlock(R, gr))) - } - idx <- .ldRefBlockIndices(x, R, length(gr)) - map2(R, idx, .ldRefBlockFromIdx, gr = gr) +.h2RefMatrixBlock <- function(x) { + list(R = ldMatrix(x), gr = as(x, "GRanges")) } +# A genotype panel cut into the LD blocks `ldBlocks` spans. Each block holds +# the panel variants inside it, whose LD is computed from their dosages; +# variants outside every block are not part of the reference, and a variant +# inside two blocks would be scored twice, so overlapping blocks are refused. # @noRd -.ldRefBlockFromIdx <- function(R, idx, gr) { - .ldRefOneBlock(R, gr[idx]) +.h2RefPanelBlocks <- function(panel, ldBlocks) { + if (is.null(ldBlocks)) { + abort(c( + "`ldBlocks` is required with a genotype panel.", + i = "It says where each LD block starts and ends." + )) + } + # A panel's seqnames are chr-prefixed; a block table may not be. + blocks <- .asLdBlockRanges(ldBlocks) + spans <- GenomeInfoDb::renameSeqlevels( + blocks, + withChrPrefix(GenomeInfoDb::seqlevels(blocks)) + ) + gr <- .ldRefRanges(panel) + hits <- GenomicRanges::findOverlaps(gr, spans) + if (anyDuplicated(S4Vectors::queryHits(hits)) > 0L) { + abort("`ldBlocks` overlap: a variant may sit in only one LD block.") + } + idx <- compact(map( + seq_along(spans), + .h2RefSpanIdx, + hits = hits + )) + if (length(idx) == 0L) { + abort("None of the panel's variants falls inside `ldBlocks`.") + } + map(idx, .h2RefPanelBlockAt, panel = panel, gr = gr) } +# The panel rows inside block `k`, in panel order, or NULL for an empty block. # @noRd -#' @importFrom checkmate assertMatrix -.ldRefOneBlock <- function(R, gr) { - R <- as.matrix(R) - assertMatrix(R, nrows = length(gr), .var.name = "LD block correlation") - list(R = R, gr = gr) +.h2RefSpanIdx <- function(k, hits) { + idx <- sort(S4Vectors::queryHits(hits)[S4Vectors::subjectHits(hits) == k]) + if (length(idx) == 0L) NULL else idx } -# Which variants each block matrix covers. blockMetadata's startIdx/endIdx is -# authoritative when it lines up with the matrices; otherwise the blocks are -# taken in order, each covering as many variants as its matrix has rows. # @noRd -.ldRefBlockIndices <- function(x, R, nVariants) { - sizes <- map_int(R, nrow) - if (sum(sizes) != nVariants) { - abort(glue( - "the LD block matrices cover {sum(sizes)} variant(s) but the ", - "LdData carries {nVariants}." - )) - } - md <- blockMetadata(x) - # blockMetadata is a GRanges: the index payload is in mcols. - hasIdx <- all(is_in( - c("startIdx", "endIdx"), - names(S4Vectors::mcols(md)) - )) && - length(md) == length(R) - if (hasIdx) { - return(map2(as.integer(md$startIdx), as.integer(md$endIdx), seq.int)) - } - ends <- cumsum(sizes) - map2(as.integer(ends - sizes + 1L), as.integer(ends), seq.int) +.h2RefPanelBlockAt <- function(idx, panel, gr) { + list(rows = idx, gr = gr[idx]) } # Global (concatenated) variant positions of each block. # @noRd -.ldRefSnpIdx <- function(blocks) { - sizes <- map_int(blocks, .ldRefBlockSize) +.h2RefSnpIdx <- function(blocks) { + sizes <- map_int(blocks, .h2RefBlockSize) ends <- cumsum(sizes) map2(as.integer(ends - sizes + 1L), as.integer(ends), seq.int) } # @noRd -.ldRefBlockSize <- function(block) length(block$gr) +.h2RefBlockSize <- function(block) length(block$gr) # The variant table .ldStatRanges() expects, stacked over the blocks in -# order. An LdData names its variants `variant_id`, and reports frequency as -# allele_freq rather than MAF. +# order. Both kinds of reference name their variants `SNP`; frequency comes +# from `AF` (an LdMatrix's, or a stochastic sketch's recorded one) as MAF. # @noRd -.ldRefSnpInfo <- function(blocks) { - bind_rows(map(blocks, .ldRefSnpInfoOne)) +.h2RefSnpInfo <- function(blocks) { + bind_rows(map(blocks, .h2RefSnpInfoOne)) } # @noRd -.ldRefSnpInfoOne <- function(block) { +.h2RefSnpInfoOne <- function(block) { gr <- block$gr md <- S4Vectors::mcols(gr, use.names = FALSE) info <- tibble( - SNP = .ldRefVariantIds(gr, md), + SNP = .h2RefVariantIds(gr, md), CHR = as.character(seqnames(gr)), BP = as.integer(start(gr)), - A1 = .ldRefAllele(md, "A1"), - A2 = .ldRefAllele(md, "A2") + A1 = .h2RefAllele(md, "A1"), + A2 = .h2RefAllele(md, "A2") ) - maf <- .ldRefMaf(md) + maf <- .h2RefMaf(md) if (is.null(maf)) info else mutate(info, MAF = maf) } # @noRd -.ldRefVariantIds <- function(gr, md) { - if (is_in("variant_id", colnames(md))) { - return(as.character(md$variant_id)) - } +.h2RefVariantIds <- function(gr, md) { if (is_in("SNP", colnames(md))) { return(as.character(md$SNP)) } @@ -248,7 +308,7 @@ setClass( } # @noRd -.ldRefAllele <- function(md, which) { +.h2RefAllele <- function(md, which) { if (!is_in(which, colnames(md))) { abort(glue( "the LD reference's variants carry no `{which}` column; an ", @@ -259,37 +319,35 @@ setClass( } # @noRd -.ldRefMaf <- function(md) { - if (is_in("MAF", colnames(md))) { - return(as.numeric(md$MAF)) +.h2RefMaf <- function(md) { + if (!is_in("AF", colnames(md))) { + return(NULL) } - if (is_in("allele_freq", colnames(md))) { - af <- as.numeric(md$allele_freq) - return(pmin(af, 1 - af)) - } - NULL + af <- as.numeric(md$AF) + pmin(af, 1 - af) } # One range per block, spanning the variants it covers. These are the blocks # the jackknife leaves out one at a time, so they must line up with -# eigenList / ldMatrixList element for element. +# eigenList / ldMatrixList element for element; a statistic read from a panel +# finds each block's variants by placing them in these ranges. # @noRd -.ldRefBlockRanges <- function(blocks, genome) { +.h2RefBlockRanges <- function(blocks, genome) { gr <- GRanges( - seqnames = map_chr(blocks, .ldRefBlockChrom), + seqnames = map_chr(blocks, .h2RefBlockChrom), ranges = IRanges::IRanges( - start = map_int(blocks, .ldRefBlockStart), - end = map_int(blocks, .ldRefBlockEnd) + start = map_int(blocks, .h2RefBlockStart), + end = map_int(blocks, .h2RefBlockEnd) ) ) - .withGenomeBuild(gr, .ldRefNamedGenome(genome), genome) + .withGenomeBuild(gr, .h2RefNamedGenome(genome), genome) } -# An LD block is within-chromosome by construction, and .ldRefBlockRanges() +# An LD block is within-chromosome by construction, and .h2RefBlockRanges() # collapses each to a single range, so a block spanning chromosomes would # silently get a range covering everything between them. # @noRd -.ldRefBlockChrom <- function(block) { +.h2RefBlockChrom <- function(block) { chrom <- unique(withChrPrefix(as.character(seqnames(block$gr)))) if (length(chrom) != 1L) { abort(glue( @@ -301,60 +359,75 @@ setClass( } # @noRd -.ldRefBlockStart <- function(block) as.integer(min(start(block$gr))) +.h2RefBlockStart <- function(block) as.integer(min(start(block$gr))) # @noRd -.ldRefBlockEnd <- function(block) as.integer(max(start(block$gr))) +.h2RefBlockEnd <- function(block) as.integer(max(start(block$gr))) # @noRd -.ldRefNamedGenome <- function(genome) { +.h2RefNamedGenome <- function(genome) { !is.null(genome) && length(genome) == 1L && !is.na(genome) && nzchar(genome) } -# nRef comes from the LdData unless the caller names one. Differing panel -# sizes are an error rather than a silent pick: nRef drives HDL's -# finite-reference correction, so the wrong one shifts h2. +# nRef is the number of independent samples the reference's LD is worth -- +# a panel's sample count, a sketch's effective size, or what each LdMatrix +# records -- unless the caller names one with `nSamples`. Differing sizes +# across LdMatrix blocks are an error rather than a silent pick: nRef drives +# HDL's finite-reference correction and the n of ldsc's unbiased r^2 in the +# LD scores, so the wrong one shifts h2. # @noRd -.ldRefResolveNRef <- function(dataList, nSamples) { +.h2RefResolveNRef <- function(ldReference, nSamples) { if (!is.null(nSamples)) { return(as.integer(nSamples)) } - found <- discard(unique(map_int(dataList, .ldRefNRefOf)), is.na) + if (.ldIsGenotypeRef(ldReference)) { + return(as.integer(.ldRefNoiseN(ldReference))) + } + found <- discard( + unique(map_int(.h2RefMatrixList(ldReference), .h2RefNSamplesOf)), + is.na + ) if (length(found) == 0L) { - abort(glue( - "`nRef` is required: none of the supplied LdData records a ", - "reference panel sample size." + abort(c( + "`nSamples` is required: no LdMatrix records its panel's size.", + i = "Pass `nSamples`, or build them with LdMatrix(nSamples = )." )) } if (length(found) > 1L) { abort(glue( - "the supplied LdData record differing reference panel sizes ", - "({str_flatten(found, ', ')}); pass `nRef` to choose one." + "the LdMatrix blocks record differing reference panel sizes ", + "({str_flatten(found, ', ')}); pass `nSamples` to choose one." )) } found[[1L]] } +# A named helper rather than `nSamples` itself: inside .h2RefResolveNRef() +# that name is the formal, which would shadow the accessor in FUN position. # @noRd -.ldRefNRefOf <- function(x) { - n <- ldPanelNSamples(x) - if (length(n) == 0L) NA_integer_ else as.integer(n[[1L]]) +.h2RefNSamplesOf <- function(x) { + nSamples(x) } # @noRd -.ldRefResolveGenome <- function(dataList, genome) { - if (.ldRefNamedGenome(genome)) { +.h2RefResolveGenome <- function(ldReference, genome) { + if (.h2RefNamedGenome(genome)) { return(genome) } - found <- discard(unique(map_chr(dataList, .ldRefGenomeOf)), is.na) + refs <- if (.ldIsGenotypeRef(ldReference)) { + list(.ldRefRanges(ldReference)) + } else { + .h2RefMatrixList(ldReference) + } + found <- discard(unique(map_chr(refs, .h2RefGenomeOf)), is.na) if (length(found) == 1L) found[[1L]] else NA_character_ } # @noRd -.ldRefGenomeOf <- function(x) { +.h2RefGenomeOf <- function(x) { g <- discard(unique(GenomeInfoDb::genome(as(x, "GRanges"))), is.na) if (length(g) == 1L) g[[1L]] else NA_character_ } @@ -370,3 +443,103 @@ setMethod("inSample", "LdStatistic", function(x) x@inSample) #' @rdname ldBlocks #' @export setMethod("ldBlocks", "LdStatistic", function(x) x@ldBlocks) + +#' @rdname ldReference +#' @export +setMethod("ldReference", "LdStatistic", function(x) x@ldReference) + +# ============================================================================= +# Reading an LD statistic's blocks +# ----------------------------------------------------------------------------- +# The estimators walk a statistic block by block. Each block is a record: +# `idx`, the block's variant positions in the statistic, and where its LD +# comes from -- `R`, the matrix an LdScore stores; `eigen`, the decomposition +# an LdEigen stores; or `rows`, the block's rows in the genotype reference it +# is read from. A scores-only LdScore has `idx` alone. The readers below turn +# a record into the LD form a computation needs, reading a genotype +# reference's block only then. +# ============================================================================= + +# The records of a statistic that stores nothing per block: its variants +# placed in its LD blocks, with their rows in its genotype reference when it +# has one. +# @noRd +.ldStatLazyBlocks <- function(x) { + idx <- .ldStatPlacedIdx(x) + if (is.null(x@ldReference)) { + return(map(idx, .ldStatIdxBlock)) + } + rows <- S4Vectors::mcols(x, use.names = FALSE)$panelRow + map(idx, .ldStatPanelBlock, rows = rows) +} + +# Each LD block's variant positions, in block order, found by placing the +# variants in `ldBlocks(x)`. A block no variant falls in has no entry: the +# placement only groups the hits there are. +# @noRd +.ldStatPlacedIdx <- function(x) { + snpInfo <- tibble(CHR = as.character(seqnames(x)), BP = start(x)) + idx <- unname(snpsPerBlock(snpInfo, x@ldBlocks)) + if (length(idx) == 0L) { + abort("no LD block covers any reference variant.") + } + idx +} + +# @noRd +.ldStatIdxBlock <- function(idx) { + list(idx = idx) +} + +# @noRd +.ldStatPanelBlock <- function(idx, rows) { + list(idx = idx, rows = rows[idx]) +} + +# A block's LD source (see .ldSourceRows()): the stored matrix, the stored +# decomposition, or a factor of it read from the genotype reference. +# @noRd +.ldStatBlockLd <- function(block, ldReference) { + if (!is.null(block$R)) { + return(list(R = block$R)) + } + if (!is.null(block$eigen)) { + return(block$eigen[c("vectors", "values")]) + } + list(F = .ldRefFactor(ldReference, block$rows)) +} + +# A block's eigendecomposition, keeping the leading components that carry +# `truncation` of its eigenvalue mass: the stored one, or one computed from +# the factor of the block read from the genotype reference. A computed one +# lists only the min(m, p) directions the factor's SVD returns; `nNull` counts +# the block's other directions, whose eigenvalue is exactly zero, when +# nothing is truncated. +# @noRd +.ldStatBlockEigen <- function(block, ldReference, truncation) { + if (!is.null(block$eigen)) { + return(list_assign(block$eigen, nNull = 0L)) + } + dec <- .ldFactorEigen(.ldRefFactor(ldReference, block$rows)) + keep <- .ldEigenKeep(dec$values, truncation) + list( + values = dec$values[keep], + vectors = dec$vectors[, keep, drop = FALSE], + snpIdx = block$idx, + nNull = if (truncation >= 1) dec$nNull else 0L + ) +} + +# `obj` reading its blocks from the genotype reference it was built from: the +# reference in its slot and each variant's row in it as `panelRow`. A +# statistic built from LdMatrix blocks is returned as it is. +# @noRd +.ldStatAttachReference <- function(obj, prep) { + if (is.null(prep$ldReference)) { + return(obj) + } + S4Vectors::mcols(obj)$panelRow <- prep$panelRow + obj@ldReference <- prep$ldReference + validObject(obj) + obj +} diff --git a/R/MethodOptions.R b/R/MethodOptions.R index 98607cf65..95522d970 100644 --- a/R/MethodOptions.R +++ b/R/MethodOptions.R @@ -92,7 +92,7 @@ setClass("MethodOptions", contains = "SimpleList") if (!requireNamespace(parts[[1L]], quietly = TRUE)) { return(NULL) } - tryCatch( + try_fetch( get(parts[[2L]], envir = asNamespace(parts[[1L]])), error = function(cnd) NULL ) @@ -123,7 +123,7 @@ setClass("MethodOptions", contains = "SimpleList") if (is.null(fn)) { return(NULL) } - fm <- tryCatch(names(formals(fn)), error = function(cnd) NULL) + fm <- try_fetch(names(formals(fn)), error = function(cnd) NULL) if (is.null(fm)) { return(NULL) } @@ -150,7 +150,7 @@ setClass("MethodOptions", contains = "SimpleList") if (is.null(fn)) { return(FALSE) } - fm <- tryCatch(names(formals(fn)), error = function(cnd) NULL) + fm <- try_fetch(names(formals(fn)), error = function(cnd) NULL) !is.null(fm) && is_in("...", fm) } @@ -169,7 +169,7 @@ setClass("MethodOptions", contains = "SimpleList") # One callee's formals with `...` removed, NULL when they cannot be read. # @noRd .engineExplicitFormals <- function(fn) { - setdiff(tryCatch(names(formals(fn)), error = function(cnd) NULL), "...") + setdiff(try_fetch(names(formals(fn)), error = function(cnd) NULL), "...") } # --- which input classes a set of engine arguments belongs to ------------- @@ -300,7 +300,7 @@ setClass("MethodOptions", contains = "SimpleList") defaults <- discard(defaults, is.null) overridden <- intersect(names(defaults), names(extra)) defaults[overridden] <- extra[overridden] - c(defaults, extra[!names(extra) %in% overridden]) + c(defaults, extra[!is_in(names(extra), overridden)]) } # Build one engine's argument record: pecotmr's defaults, overlaid with the @@ -575,7 +575,7 @@ setClass("MethodOptions", contains = "SimpleList") # sibling key that shares the same engine. # @noRd .nestedExpectedEngine <- function(key, ctors) { - rec <- tryCatch(ctors[[key]](), error = function(cnd) NULL) + rec <- try_fetch(ctors[[key]](), error = function(cnd) NULL) if (is.null(rec)) { return(key) } @@ -631,7 +631,7 @@ setClass("MethodOptions", contains = "SimpleList") "'{str_split_1(callee, fixed('::'))[[1L]]}' is not installed" )) } - fm <- tryCatch(names(formals(fn)), error = function(cnd) NULL) + fm <- try_fetch(names(formals(fn)), error = function(cnd) NULL) if (is.null(fm)) { return("argument names NOT checked: the engine's formals are unknown") } diff --git a/R/MethodParam.R b/R/MethodParam.R index e9048d210..d6dddff6b 100644 --- a/R/MethodParam.R +++ b/R/MethodParam.R @@ -44,6 +44,10 @@ NULL setClassUnion("numeric_OR_NULL", c("numeric", "NULL")) setClassUnion("logical_OR_NULL", c("logical", "NULL")) setClassUnion("list_OR_NULL", c("list", "NULL")) +# A tibble is a data.frame here, so it fills a data.frame_OR_NULL slot; a bare +# list does not. +setClassUnion("data.frame_OR_NULL", c("data.frame", "NULL")) +setClassUnion("matrix_OR_NULL", c("matrix", "NULL")) # The one cross-family slot: SusieRssParam() carries a SusieRssControlOptions() # record, which is a MethodOptions rather than a Param -- `control` really is @@ -311,10 +315,13 @@ setValidity("MethodsSelectionParam", function(object) { # "is mrmash requested", "are these methods available at all". # @noRd .methodsParamTokens <- function(param) { - unique(unlist(map( - .methodsSlotNames, - function(s) names(slot(param, s)) %||% character(0) - ))) + unique(unlist(map(.methodsSlotNames, .methodsSlotTokens, param = param))) +} + +# The method names one slot of a Param holds; empty when the slot is. +# @noRd +.methodsSlotTokens <- function(slotName, param) { + names(slot(param, slotName)) %||% character(0) } # The same Param with some methods removed from every slot -- the joint @@ -844,13 +851,13 @@ setValidity("MethodsSelectionParam", function(object) { # @noRd .methodsCtorHasCallee <- function(ctor, callee) { - rec <- tryCatch(ctor(), error = function(cnd) NULL) + rec <- try_fetch(ctor(), error = function(cnd) NULL) !is.null(rec) && identical(metadata(rec)$callee, callee) } # @noRd .methodsRecordEngine <- function(ctor) { - rec <- tryCatch(ctor(), error = function(cnd) NULL) + rec <- try_fetch(ctor(), error = function(cnd) NULL) if (is.null(rec)) { return(NULL) } @@ -1053,8 +1060,14 @@ setValidity("MethodsSelectionParam", function(object) { # *Param() has its own class, so handing `ColocPriorParam()` where # `CredibleSetParam()` was wanted is caught here rather than surfacing as a # missing field somewhere downstream. +# +# The message names the argument from the caller's own expression +# (caller_arg()), so it cannot drift from the argument's name the way a +# hand-written label did: 19 of 30 labels still named arguments that had +# since been renamed. # @noRd -.assertMethodParam <- function(x, ctor, argName, class = NULL) { +#' @importFrom rlang caller_arg +.assertMethodParam <- function(x, ctor, argName = caller_arg(x), class = NULL) { class <- class %||% .paramClassOf(ctor) if (is(x, class)) { return(invisible(x)) diff --git a/R/MultiStudyQtlDataset.R b/R/MultiStudyQtlDataset.R index 8136251ea..e9d27060c 100644 --- a/R/MultiStudyQtlDataset.R +++ b/R/MultiStudyQtlDataset.R @@ -8,9 +8,13 @@ # orchestrate joint or per-study analyses across multiple cohorts. # ============================================================================= -#' @include QtlDataset.R +#' @include QtlDataset.R qtlSumStats.R NULL +# The summary-statistic-only studies: a QtlSumStats, or NULL when there are +# none. The slot type refuses anything else when the object is built. +setClassUnion("QtlSumStats_OR_NULL", c("QtlSumStats", "NULL")) + #' @title Multi-Study QTL Dataset #' @description S4 container for a multi-study QTL analysis: a collection of #' individual-level \code{QtlDataset} studies, optionally combined with a @@ -34,7 +38,7 @@ setClass( "MultiStudyQtlDataset", representation( qtlDatasets = "list", - sumStats = "ANY" + sumStats = "QtlSumStats_OR_NULL" ), validity = function(object) .validateMultiStudyQtlDataset(object) ) @@ -46,10 +50,7 @@ setClass( # Each stage only makes sense once the previous one holds: the study count # reads slots the type checks just validated, and trait consistency reads # across the studies the count check just confirmed. - slotErrors <- c( - .msqdCheckDatasets(object@qtlDatasets), - .msqdCheckSumStats(object@sumStats) - ) + slotErrors <- .msqdCheckDatasets(object@qtlDatasets) if (length(slotErrors) > 0L) { return(slotErrors) } @@ -85,14 +86,6 @@ setClass( methods::is(d, "QtlDataset") } -# @noRd -.msqdCheckSumStats <- function(sumStats) { - if (!is.null(sumStats) && !methods::is(sumStats, "QtlSumStats")) { - return("'sumStats' must be a QtlSumStats object or NULL") - } - NULL -} - # At least 2 studies in total (individual-level + summary-statistic). # @noRd .msqdCheckStudyCount <- function(object) { diff --git a/R/QtlDataset.R b/R/QtlDataset.R index f3c7c1e55..0f1a2a133 100644 --- a/R/QtlDataset.R +++ b/R/QtlDataset.R @@ -45,7 +45,7 @@ NULL #' view of that experiment reads its dosages. #' #' @slot studyName Character (length 1). Study identifier; used in collection -#' classes to tag downstream \code{FineMappingResult} / \code{TwasWeights} +#' classes to tag downstream \code{QtlFineMappingResult} / \code{TwasWeights} #' entries. #' The \code{genotype} experiment's assay reads through this handle; the #' extraction accessors read it directly, so that QC can be applied per @@ -159,11 +159,6 @@ setClass( ) } -# Shape checks on the phenotype list handed to the constructor. Run before -# the MultiAssayExperiment is assembled, because a malformed list makes -# ExperimentList fail first with a message about experiments rather than -# about contexts. Returns an error vector. -# @noRd # What is wrong with the context names, if anything. The checks are ordered: # a list with no usable names is not also reported as having duplicates. # @noRd @@ -207,6 +202,11 @@ setClass( character(0) } +# Shape checks on the phenotype list handed to the constructor. Run before +# the MultiAssayExperiment is assembled, because a malformed list makes +# ExperimentList fail first with a message about experiments rather than +# about contexts. Returns an error vector. +# @noRd .qtlCheckPhenotypeList <- function(phenotypes) { emptyError <- if (length(phenotypes) == 0L) { "'phenotypes' must not be empty" @@ -334,7 +334,7 @@ setClass( # QtlDataset constructor and accessors # ============================================================================= -# Check the constructor's inputs and return the handle behind `genotypes`. +# Check the constructor's inputs and return the genotype source as given. # Every phenotype must be a usable SummarizedExperiment, and the genotype # source must be an unsubset panel (or a bare handle) -- see # .qtlCheckWholePanel for why the "unsubset" part matters. @@ -344,28 +344,42 @@ setClass( if (length(errors) > 0L) { abort(str_flatten(errors, "\n")) } - handle <- .openGenotypeHandle(genotypes) - if (is.null(handle)) { + source <- .openGenotypeSource(genotypes) + if (is.null(source)) { abort(glue( "'genotypes' must be a genotype panel from readGenotypes() ", "(got {class(genotypes)[[1L]]})" )) } - .qtlCheckWholePanel(genotypes, handle) - handle + .qtlCheckWholePanel(genotypes) + .qtlCheckRealGenotypes(genotypes) + source } -# A QtlDataset indexes variants positionally into the handle's whole snpInfo -# (see .qtlVariantIndices), so a panel that has already been narrowed would -# leave extraction reading the wrong rows. Refuse it rather than silently -# widening back to the file: narrowing belongs to keepVariants / keepSamples, -# which the extraction path honours. +# A stochastic LD sketch is a panel whose columns are pseudo-samples +# projected from real ones: it carries LD, not anyone's genotypes, so it +# cannot be the genotype experiment a QTL study is fitted on. # @noRd -.qtlCheckWholePanel <- function(genotypes, handle) { +.qtlCheckRealGenotypes <- function(genotypes) { + if (!.ldRefIsProjection(genotypes)) { + return(invisible(NULL)) + } + abort(c( + "'genotypes' is a stochastic LD sketch, not a study's genotypes.", + i = "Its samples are projections; use it as an `ldReference` instead." + )) +} + +# A panel that has already been narrowed is refused rather than taken as the +# study's genotypes: narrowing belongs to keepVariants / keepSamples, which +# the extraction path honours, and it is recorded there rather than in which +# panel happened to be passed. +# @noRd +.qtlCheckWholePanel <- function(genotypes) { if (!methods::is(genotypes, "RangedSummarizedExperiment")) { return(invisible(NULL)) } - full <- c(nrow(snpInfo(handle)), nSamples(handle)) + full <- .panelFullDims(genotypes) got <- c(nrow(genotypes), ncol(genotypes)) if (identical(as.integer(got), as.integer(full))) { return(invisible(NULL)) @@ -450,8 +464,7 @@ QtlDataset <- function( ) { .assertMethodParam( genotypeFilterParam, - "GenotypeFilterParam", - "genotypeFilter" + "GenotypeFilterParam" ) filt <- .qtlResolveFilter(genotypeFilterParam) handle <- .qtlValidateInputs(phenotypes, genotypes) @@ -743,12 +756,19 @@ setMethod("genotypeHandle", "QtlDataset", function(x) { # Derived, not stored. The handle already lives inside the genotype # assay's seed -- that is what lets the dosages read lazily -- so a # parallel slot was a second copy that had to be kept in step by hand. - # Reading it back removes the invariant instead of policing it. - .ldSketchHandle( - MultiAssayExperiment::experiments(x)[[.QTL_GENO_EXPERIMENT]] - ) + # Reading it back removes the invariant instead of policing it. NULL for + # genotypes read through another backend (VCF, GDS) or held in memory. + .ldRefHandle(.qtlGenotypeSe(x)) }) +# The genotype experiment's variants, one row per panel row: SNP (the +# panel's own ids), CHR (canonical), BP, A1, A2. Read off its rowRanges, so +# genotypes from any backend answer alike. +# @noRd +.qtlGenotypeVariants <- function(x) { + .rangesSnpInfo(SummarizedExperiment::rowRanges(.qtlGenotypeSe(x))) +} + #' @rdname qtlDatasetFilters #' @export setMethod("mafCutoff", "QtlDataset", function(x) x@mafCutoff) @@ -799,9 +819,6 @@ setMethod("keepIndel", "QtlDataset", function(x) x@keepIndel) .qtlLiteralRegion(region, cisWindow) } -# The union span (+/- cisWindow) of a trait's rowRanges across all contexts. -# Requires cisWindow and a single shared chromosome. -# @noRd # The requested traits' ranges within one context, or NULL when it carries # none of them. # @noRd @@ -821,6 +838,9 @@ setMethod("keepIndel", "QtlDataset", function(x) x@keepIndel) hits[!is.na(hits)] } +# The union span (+/- cisWindow) of the requested traits' rowRanges across all +# contexts. Requires cisWindow and a single shared chromosome. +# @noRd .qtlTraitRegion <- function(x, traitId, cisWindow) { if (is.null(cisWindow) || length(cisWindow) != 1L || cisWindow < 0) { msg <- glue( @@ -953,16 +973,16 @@ setMethod("traitPosition", "QtlDataset", function(x, traitId = NULL) { }) # Internal: map a GRanges region (one or more ranges) into 1-based snpIdx into -# handle@snpInfo. Indices are unioned across ranges in range order (first -# occurrence wins), so overlapping ranges contribute each variant once. +# the genotype experiment's rows. Indices are unioned across ranges in range +# order (first occurrence wins), so overlapping ranges contribute each +# variant once. .qtlVariantIndices <- function(x, region = NULL) { - handle <- genotypeHandle(x) + variants <- .qtlGenotypeVariants(x) if (is.null(region)) { - return(seq_len(nrow(snpInfo(handle)))) + return(seq_len(nrow(variants))) } - snpInfo <- snpInfo(handle) - siChr <- canonChrom(snpInfo$CHR) - bp <- as.integer(snpInfo$BP) + siChr <- variants$CHR + bp <- variants$BP rChr <- canonChrom(GenomicRanges::seqnames(region)) rStart <- GenomicRanges::start(region) rEnd <- GenomicRanges::end(region) @@ -1036,8 +1056,7 @@ setMethod("traitPosition", "QtlDataset", function(x, traitId = NULL) { .qtlApplyFilterOverrides <- function(data, genotypeFilterParam) { .assertMethodParam( genotypeFilterParam, - "GenotypeFilterParam", - "genotypeFilter" + "GenotypeFilterParam" ) # discard(is.null), not compact(): a keepVariants of character(0) is a # real instruction ("restrict to nothing was not asked, keep all"), and @@ -1117,8 +1136,12 @@ setMethod("traitPosition", "QtlDataset", function(x, traitId = NULL) { if (length(snpIdx) == 0L) { return(.qtlEmptyBlock()) } - handle <- genotypeHandle(x) - dosage <- .dosageMatrix(handle, snpIdx, meanImpute = FALSE) + se <- .qtlGenotypeSe(x) + # Labelled with the panel's own variant ids, as the rows a block reports. + dosage <- `colnames<-`( + .ldRefDosage(se, snpIdx, meanImpute = FALSE), + .ldRefVariantIds(se)[snpIdx] + ) keep <- .qtlResolveSamples(dosage, x, samples) if (length(keep) == 0L) { return(.qtlEmptyBlockNoSamples(dosage)) @@ -1138,16 +1161,16 @@ setMethod("traitPosition", "QtlDataset", function(x, traitId = NULL) { # Empty block preserving the full panel sample set (no variants selected). # @noRd .qtlEmptyBlockAllSamples <- function(x) { - handle <- genotypeHandle(x) + samples <- as.character(colnames(.qtlGenotypeSe(x))) list( geno = matrix( numeric(0), - nrow = nSamples(handle), + nrow = length(samples), ncol = 0L, - dimnames = list(sampleIds(handle), character(0)) + dimnames = list(samples, character(0)) ), variantIds = character(0), - sampleIds = sampleIds(handle), + sampleIds = samples, maf = numeric(0), af = numeric(0) ) @@ -1186,18 +1209,17 @@ setMethod("traitPosition", "QtlDataset", function(x, traitId = NULL) { # allele) and, unless kept, by dropping indels. Done before materialization. # @noRd .qtlNarrowSnpIdx <- function(x, snpIdx) { - handle <- genotypeHandle(x) + si <- .qtlGenotypeVariants(x) kept <- if (length(keepVariants(x)) == 0L) { snpIdx } else { - snpAll <- as.character(snpInfo(handle)$SNP[snpIdx]) + snpAll <- si$SNP[snpIdx] km <- matchVariants(snpAll, as.character(keepVariants(x))) snpIdx[replace(logical(length(snpAll)), km$idxA, TRUE)] } if (length(kept) == 0L || .qtlKeepIndel(x)) { return(kept) } - si <- snpInfo(handle) # which() (not the mask) so an NA mask drops the variant rather than # injecting an NA index. snpMask <- str_length(as.character(si$A1[kept])) == 1L & @@ -1659,11 +1681,11 @@ setMethod("phenotypeCovariates", "QtlDataset", function(x, contexts) { # convention `residualizePhenotype` / `residualizeGenotype` use -- a flat # formal defaulting to TRUE could not tell "FALSE" from "not given". # @noRd -.qtlScaleResiduals <- function(x, residualizationArgs) { - if (is.null(residualizationArgs)) { +.qtlScaleResiduals <- function(x, residualizationParam) { + if (is.null(residualizationParam)) { return(scaleResiduals(x)) } - residualizationArgs$scaleResiduals %||% scaleResiduals(x) + residualizationParam$scaleResiduals %||% scaleResiduals(x) } # Internal: residualize a numeric matrix Y (n x k) against a covariate @@ -1719,7 +1741,7 @@ setMethod("phenotypeCovariates", "QtlDataset", function(x, contexts) { if (length(keep) != length(requested)) { missingNames <- setdiff(requested, avail) msg <- glue( - "residualizationArgs$phenotypeCovariates: context '{ctx}' has no ", + "residualizationParam$phenotypeCovariates: context '{ctx}' has no ", "covariate(s) named: {str_flatten(missingNames, ', ')}" ) abort(msg) @@ -1866,10 +1888,6 @@ setMethod("phenotypeCovariates", "QtlDataset", function(x, contexts) { # Per-context phenotype covariate matrices (colData columns from # phenoSelection), column names prefixed with the context. # @noRd -# Sample names survive the coercion because the constructor requires every -# context to name its samples, which SummarizedExperiment carries into -# rownames(colData(se)). -# @noRd .qtlPhenoCovBlocks <- function(x, contexts, phenoSelection) { compact(set_names( map( @@ -1884,6 +1902,10 @@ setMethod("phenotypeCovariates", "QtlDataset", function(x, contexts) { # One context's selected covariate columns, context-qualified, or NULL when # nothing is selected there. +# +# Sample names survive the coercion because the constructor requires every +# context to name its samples, which SummarizedExperiment carries into +# rownames(colData(se)). # @noRd .qtlPhenoCovBlock <- function(ctx, x, phenoSelection) { keep <- phenoSelection[[ctx]] @@ -1998,7 +2020,7 @@ setMethod("phenotypeCovariates", "QtlDataset", function(x, contexts) { region = NULL, cisWindow = NULL, samples = NULL, - residualizationArgs = ResidualizationParam() + residualizationParam = ResidualizationParam() ) { if (missing(contexts) || is.null(contexts) || length(contexts) == 0L) { msg <- glue( @@ -2011,9 +2033,8 @@ setMethod("phenotypeCovariates", "QtlDataset", function(x, contexts) { abort(msg) } .assertMethodParam( - residualizationArgs, - "ResidualizationParam", - "residualizationArgs" + residualizationParam, + "ResidualizationParam" ) bad <- setdiff(contexts, contexts(x)) if (length(bad) > 0L) { @@ -2030,12 +2051,12 @@ setMethod("phenotypeCovariates", "QtlDataset", function(x, contexts) { if (ncol(G) == 0L) { return(G) } - C <- .qtlResidCovariateMatrix(x, contexts, residualizationArgs) + C <- .qtlResidCovariateMatrix(x, contexts, residualizationParam) aligned <- .qtlAlignGC(G, C, contexts) .qtlResidualizeQr( aligned$G, aligned$C, - scaleResiduals = .qtlScaleResiduals(x, residualizationArgs) + scaleResiduals = .qtlScaleResiduals(x, residualizationParam) ) } @@ -2130,7 +2151,7 @@ setMethod( outlierAction = c("keep", "drop"), outlierPvalThreshold = 1e-3, outlierArgs = CovMcdOptions(), - residualizationArgs = ResidualizationParam() + residualizationParam = ResidualizationParam() ) { if (missing(contexts) || is.null(contexts) || length(contexts) == 0L) { abort("`contexts` is required for residualizedPhenotypes().") @@ -2139,16 +2160,15 @@ setMethod( outlierAction <- arg_match(outlierAction) .assertMethodOptions(outlierArgs, "CovMcdOptions", "outlierArgs") .assertMethodParam( - residualizationArgs, - "ResidualizationParam", - "residualizationArgs" + residualizationParam, + "ResidualizationParam" ) bad <- setdiff(contexts, contexts(x)) if (length(bad) > 0L) { msg <- glue("Unknown context(s): {str_flatten(bad, \', \')}") abort(msg) } - C <- .qtlResidCovariateMatrix(x, contexts, residualizationArgs) + C <- .qtlResidCovariateMatrix(x, contexts, residualizationParam) Yraw <- .qtlResidPhenoY( x, contexts, @@ -2164,7 +2184,7 @@ setMethod( outlierAction = outlierAction, outlierPvalThreshold = outlierPvalThreshold, outlierArgs = outlierArgs, - scaleResiduals = .qtlScaleResiduals(x, residualizationArgs) + scaleResiduals = .qtlScaleResiduals(x, residualizationParam) ) } @@ -2181,15 +2201,15 @@ setMethod( # they select, build the design, and apply the NA policy to it. One step, # because none of the four halves means anything without the others. # @noRd -.qtlResidCovariateMatrix <- function(x, contexts, residualizationArgs) { +.qtlResidCovariateMatrix <- function(x, contexts, residualizationParam) { # arg_match() reports on its argument by name, so it must be handed a # symbol: resolve the bundle field first, then validate it. - covariateNaAction <- residualizationArgs$covariateNaAction %||% "impute" + covariateNaAction <- residualizationParam$covariateNaAction %||% "impute" covariateNaAction <- arg_match(covariateNaAction, c("impute", "drop")) phenoSel <- .qtlResolvePhenoSelection( x, contexts, - residualizationArgs$phenotypeCovariates + residualizationParam$phenotypeCovariates ) design <- .qtlBuildResidualizationDesign( x, @@ -2197,13 +2217,13 @@ setMethod( phenoSelection = phenoSel, genoSelection = .qtlResolveGenoSelection( x, - residualizationArgs$genotypeCovariates + residualizationParam$genotypeCovariates ), # Unset means "residualize on this side": the accessors' own # historical default, now expressed as the absence of a value # rather than as a formal defaulting to TRUE. - includePheno = residualizationArgs$residualizePhenotype %||% TRUE, - includeGeno = residualizationArgs$residualizeGenotype %||% TRUE + includePheno = residualizationParam$residualizePhenotype %||% TRUE, + includeGeno = residualizationParam$residualizeGenotype %||% TRUE ) .qtlHandleCovariateNa(design, covariateNaAction) } @@ -2254,9 +2274,8 @@ setMethod("show", "QtlDataset", function(object) { .trim = FALSE )) cat(glue(" {totalTraits} unique traits across contexts\n", .trim = FALSE)) - gh <- genotypeHandle(object) cat(glue( - " Genotypes: {genotypeFormat(gh)} @ {path(gh)}\n", + " Genotypes: {.ldRefLabel(.qtlGenotypeSe(object))}\n", .trim = FALSE )) cat(glue( diff --git a/R/QtlFineMappingResult.R b/R/QtlFineMappingResult.R index f7dfe39ac..d90de27ed 100644 --- a/R/QtlFineMappingResult.R +++ b/R/QtlFineMappingResult.R @@ -4,7 +4,7 @@ # DFrame-subclass collection keyed by the identity tuple (study, context, # trait, method). Each row holds a FineMappingRow payload. Class-level # slots: -# * ldSketch GenotypeHandle (NULL for individual-level fits, the +# * ldReference GenotypeHandle (NULL for individual-level fits, the # LD-sketch handle for RSS-derived fits). # Optional columns jointStudies / jointContexts / jointTraits carry # semicolon-joined member identities for cross-axis joint fits emitted by @@ -39,7 +39,7 @@ setClass( # ---- QtlFineMappingResult validity helpers --------------------------------- # Collect all contract violations (empty vector = valid). The per-entry checks -# run only once the required columns are present; the ldSketch check always +# run only once the required columns are present; the ldReference check always # runs. # @noRd #' @importFrom checkmate makeAssertCollection assertNames checkNames @@ -92,10 +92,6 @@ setClass( str_c("missing entry payload columns: ", res) } -# @noRd - -# @noRd - # Each present joint* column must be character. # @noRd .qfmrCheckJointCols <- function(object, jointCols) { @@ -138,7 +134,7 @@ setClass( .tupleColumn(object, cn) } -# ldSketch must be a GenotypeHandle or NULL. +# ldReference must be a GenotypeHandle or NULL. # The identity-tuple vectors and the payload list are parallel: one entry per # (study, context, trait, method). A length mismatch would otherwise surface as @@ -163,8 +159,8 @@ setClass( #' @title Create a QtlFineMappingResult Collection #' @description Construct a \code{QtlFineMappingResult} DFrame-subclass #' collection from per-tuple vectors and a list of \code{FineMappingRow} -#' payloads (one per tuple). The optional \code{ldSketch} slot records the LD -#' reference used for RSS-derived fits; pass \code{NULL} (the default) for +#' payloads (one per tuple). The optional \code{ldReference} slot records the +#' LD reference used for RSS-derived fits; pass \code{NULL} (the default) for #' individual-level fits. #' @param studyName Character vector of study identifiers (per tuple). Use the #' sentinel \code{"joint"} for rows produced by a cross-study joint fit. @@ -188,7 +184,7 @@ setClass( #' @param dataSource Optional per-row label for the kind of input the fit saw, #' \code{"individual"} or \code{"sumstat"}. Recorded rather than derived: a #' combined collection can hold rows of both kinds, and its single -#' \code{ldSketch} cannot say which row came from which. +#' \code{ldReference} cannot say which row came from which. #' @param weightStandardized Optional per-row flag: whether the posterior #' effect is on the STANDARDIZED genotype scale. The posterior effect IS the #' prediction weight, so it carries the same scale question as a TWAS @@ -196,9 +192,9 @@ setClass( #' it standardizes genotypes before fitting, and the posterior mean is #' reported on the scale of the \code{X} the fit was given. The argument #' exists for a collection assembled by hand, where only the caller knows. -#' @param ldSketch An optional genotype panel (see \code{\link{readGenotypes}}) -#' (the LD reference for RSS-derived fits), or \code{NULL} for -#' individual-level fits. +#' @param ldReference An optional genotype panel (see +#' \code{\link{readGenotypes}}) (the LD reference for RSS-derived fits), or +#' \code{NULL} for individual-level fits. #' @param traitPos Optional per-row trait genomic anchor (a \code{GRanges} or #' \code{NULL}), carried forward as provenance; not part of the identity key. #' \code{NULL} (default) omits the column. @@ -232,7 +228,7 @@ QtlFineMappingResult <- function( dataType = NULL, dataSource = NULL, weightStandardized = NULL, - ldSketch = NULL + ldReference = NULL ) { .qfmrAssertArgs( studyName, @@ -264,13 +260,7 @@ QtlFineMappingResult <- function( weightStandardized, n ) - obj <- new( - "QtlFineMappingResult", - .rtlGrlWithMetadata(payloads, cols), - ldSketch = .asLdSketch(ldSketch) - ) - validObject(obj) - obj + .rtlNewValidated("QtlFineMappingResult", payloads, cols, ldReference) } # Every argument's type contract, checked before anything is built. @@ -509,10 +499,10 @@ setMethod("show", "QtlFineMappingResult", function(object) { .trim = FALSE )) } - ldSrc <- if (is.null(object@ldSketch)) { + ldSrc <- if (is.null(object@ldReference)) { "NULL (individual-level fit)" } else { - .ldSketchLabel(object@ldSketch) + .ldRefLabel(object@ldReference) } - cat(glue(" LD sketch: {ldSrc}\n", .trim = FALSE)) + cat(glue(" LD reference: {ldSrc}\n", .trim = FALSE)) }) diff --git a/R/RangedTupleList.R b/R/RangedTupleList.R index 26cece45a..83b46fc13 100644 --- a/R/RangedTupleList.R +++ b/R/RangedTupleList.R @@ -15,10 +15,10 @@ # `[[` is left alone on purpose: on a GRangesList it addresses elements, so # overloading it for columns would collide. Use `mcols(x)[i, j]` for a cell. # -# Slots hold COLLECTION-LEVEL scalars only (ldSketch, genome, qcInfo). Anything -# with one value per element belongs in mcols -- a per-element slot silently -# desyncs on subsetting, because `x[i]` narrows the elements and the mcols but -# has no idea the slot is parallel to them. +# Slots hold COLLECTION-LEVEL scalars only (ldReference, genome, qcInfo). +# Anything with one value per element belongs in mcols -- a per-element slot +# silently desyncs on subsetting, because `x[i]` narrows the elements and the +# mcols but has no idea the slot is parallel to them. # ============================================================================= #' @include AllGenerics.R @@ -128,14 +128,6 @@ methods::setValidity("RangedTupleList", function(object) { ) } -# Rebuild `x` from a new list of GRanges elements, carrying every -# collection-level slot over and narrowing mcols by `keep`. -# -# This is the ONLY supported way to produce a modified collection. `endoapply` -# via a `setAs("list", )` coercion looks like it works but silently -# drops the subclass's slots -- verified -- which is exactly the desync this -# class exists to prevent. -# @noRd # Put `x`'s seqinfo back onto a rebuilt GRangesList. Only the seqlevels the # rebuild actually spans can be kept, so this restores the genome (and any # lengths) for those, and supplies the whole seqinfo when the rebuild spans @@ -160,6 +152,14 @@ methods::setValidity("RangedTupleList", function(object) { ) } +# Rebuild `x` from a new list of GRanges elements, carrying every +# collection-level slot over and narrowing mcols by `keep`. +# +# This is the ONLY supported way to produce a modified collection. `endoapply` +# via a `setAs("list", )` coercion looks like it works but silently +# drops the subclass's slots -- verified -- which is exactly the desync this +# class exists to prevent. +# @noRd .rtlRebuild <- function(x, elements, keep) { # mcols and slots are attached BEFORE new(), not after: new() validates # during initialize(), and a subclass's validity method reads its identity @@ -352,7 +352,7 @@ setMethod("endoapply", "RangedTupleList", function(X, FUN, ...) { # Defined explicitly rather than left to the inherited machinery. Stock # `endoapply` on a subclass errors unless a `setAs("list", )` # coercion exists, and adding that coercion makes it run while silently - # DROPPING the subclass's slots (verified: genome / ldSketch come back + # DROPPING the subclass's slots (verified: genome / ldReference come back # unset). Rebuilding here keeps every collection-level slot. # # Call this as `endoapply(x, ...)`, NOT `S4Vectors::endoapply(x, ...)`: @@ -431,6 +431,20 @@ setMethod("subsetRegion", "RangedTupleList", function(x, region) { ) } +# Build and validate a RangedTupleList subclass from its payloads and metadata +# columns -- the shared tail of the TwasWeights and QtlFineMappingResult +# constructors. +# @noRd +.rtlNewValidated <- function(Class, payloads, cols, ldReference) { + obj <- new( + Class, + .rtlGrlWithMetadata(payloads, cols), + ldReference = .asLdRef(ldReference) + ) + validObject(obj) + obj +} + # Split any multi-seqname element into one element per seqname, and report the # index each piece came from so the caller can replicate that element's # metadata row alongside it. @@ -621,7 +635,10 @@ setMethod("subsetRegion", "RangedTupleList", function(x, region) { # on every call -- pure noise for a collection spanning several chromosomes. # @noRd .rtlRestrictOne <- function(g, win) { - onWindowChrom <- as.character(seqnames(g)) %in% as.character(seqnames(win)) + onWindowChrom <- is_in( + as.character(seqnames(g)), + as.character(seqnames(win)) + ) if (!any(onWindowChrom)) { return(g[0L]) } diff --git a/R/causalInferencePipeline.R b/R/causalInferencePipeline.R index 35512d902..80ea97dba 100644 --- a/R/causalInferencePipeline.R +++ b/R/causalInferencePipeline.R @@ -13,22 +13,25 @@ NULL #' \item \code{twasWeights} alone (no \code{fineMappingResult}): #' TWAS Z only, no MR. #' \item \code{fineMappingResult} alone (no \code{twasWeights}): -#' TWAS Z derived from SuSiE-style coefficients carried on the -#' \code{topLoci} slot of each FineMappingRow; plus MR. +#' TWAS Z derived from the SuSiE-style coefficients in each +#' row's top loci (\code{\link{topLoci}}); plus MR. #' \item both: TWAS Z computed from \code{twasWeights}; #' MR computed from \code{fineMappingResult}. #' } #' -#' @section LD-sketch compatibility check: If a QTL input (TwasWeights or -#' QtlFineMappingResult) carries a non-\code{NULL} \code{ldSketch}, it must -#' come from the same reference panel as the \code{ldSketch} on -#' \code{gwasSumStats} --- same samples, same allele orientation on the -#' shared variants. The two need NOT carry the same variants: QC-ing the two -#' sides separately trims each sketch to its own surviving variants, and a -#' partial overlap only warns. A different sample set, a swapped A1/A2 on a -#' shared variant, or no shared variant at all is a hard error. A QTL input -#' with \code{ldSketch = NULL} (the fit was learned from individual-level -#' data) skips the validation for that input. +#' @section LD reference compatibility check: If a QTL input +#' (TwasWeights or QtlFineMappingResult) carries a non-\code{NULL} +#' \code{ldReference}, it must be the same LD reference as the +#' \code{ldReference} on \code{gwasSumStats}. +#' The two must be the same kind of reference. Two genotype panels must +#' share their samples and allele orientation; two \code{LdMatrix} objects +#' must agree on the LD they share; two \code{GenotypeMixture} objects must +#' average the same panels with the same weights. The two need NOT carry +#' the same variants: QC-ing the two sides separately trims each reference +#' to its own surviving variants, and a partial overlap only warns. A +#' mismatch of any of those, or no shared variant at all, is a hard error. A +#' QTL input with \code{ldReference = NULL} (the fit was learned from +#' individual-level data) skips the validation for that input. #' #' @section Output shape: #' A long-format \code{GRanges} with one row per @@ -55,7 +58,7 @@ NULL #' supplied, drives the MR computation and (when \code{twasWeights = NULL}) #' the TWAS-Z weights via the SuSiE-style coefficients on each entry's #' \code{topLoci}. -#' @param weightSelectionArgs How one TWAS method's weights are picked per +#' @param weightSelectionParam How one TWAS method's weights are picked per #' \code{(study, context, trait, gwasStudy)} tuple, built with #' \code{\link{WeightSelectionParam}}: \code{cutoff} (minimum #' cross-validated r-squared, default \code{0}), \code{pvalCutoff} (the @@ -66,7 +69,7 @@ NULL #' gates that also produced a finite TWAS Z. Otherwise every method is #' carried through. Ports the legacy \code{twas_pipeline} #' \code{pick_best_model} / \code{update_twas_method}. -#' @param mrArgs How Mendelian randomization is run, built with +#' @param mrParam How Mendelian randomization is run, built with #' \code{\link{MrParam}}: \code{method} (\code{"ivwPerVariant"}, the #' default, or \code{"csAware"}), the PIP threshold that method reads #' (\code{pipCutoff} or \code{cpipCutoff}), and \code{pvalCutoff}, the @@ -93,25 +96,24 @@ causalInferencePipeline <- function( gwasSumStats, twasWeights = NULL, fineMappingResult = NULL, - weightSelectionArgs = WeightSelectionParam(), - mrArgs = MrParam(), + weightSelectionParam = WeightSelectionParam(), + mrParam = MrParam(), combineMethods = NULL, alleleFlip = TRUE ) { .assertMethodParam( - weightSelectionArgs, - "WeightSelectionParam", - "weightSelection" + weightSelectionParam, + "WeightSelectionParam" ) - .assertMethodParam(mrArgs, "MrParam", "mr") + .assertMethodParam(mrParam, "MrParam") .cipRun( gwasSumStats = gwasSumStats, twasWeights = twasWeights, fineMappingResult = fineMappingResult, combineMethods = combineMethods, - weightSelectionArgs = weightSelectionArgs, + weightSelectionParam = weightSelectionParam, alleleFlip = alleleFlip, - mrArgs = mrArgs + mrParam = mrParam ) } @@ -123,12 +125,12 @@ causalInferencePipeline <- function( twasWeights, fineMappingResult, combineMethods, - weightSelectionArgs, + weightSelectionParam, alleleFlip, - mrArgs + mrParam ) { .cipValidateInputs(gwasSumStats, twasWeights, fineMappingResult) - gwasLd <- .cipCheckLdSketches( + gwasLd <- .cipCheckLdRefs( gwasSumStats, twasWeights, fineMappingResult @@ -137,7 +139,7 @@ causalInferencePipeline <- function( sel <- .cipCvSelection( qtlRows = allRows, twasWeights = twasWeights, - weightSelectionArgs = weightSelectionArgs + weightSelectionParam = weightSelectionParam ) qtlRows <- sel$qtlRows outRows <- list_flatten(map( @@ -149,7 +151,7 @@ causalInferencePipeline <- function( gwasSumStats = gwasSumStats, gwasLd = gwasLd, alleleFlip = alleleFlip, - mrArgs = mrArgs + mrParam = mrParam )) if (length(outRows) == 0L) { abort( @@ -199,18 +201,18 @@ causalInferencePipeline <- function( # Require the twasWeights / fineMappingResult LD sketches to match the GWAS one; # returns the GWAS LD sketch. # @noRd -.cipCheckLdSketches <- function(gwasSumStats, twasWeights, fineMappingResult) { - gwasLd <- ldSketch(gwasSumStats) +.cipCheckLdRefs <- function(gwasSumStats, twasWeights, fineMappingResult) { + gwasLd <- ldReference(gwasSumStats) if (!is.null(twasWeights)) { - .cipRequireMatchingLdSketches( - ldSketch(twasWeights), + .cipRequireMatchingLdRefs( + ldReference(twasWeights), gwasLd, label = "twasWeights" ) } if (!is.null(fineMappingResult)) { - .cipRequireMatchingLdSketches( - ldSketch(fineMappingResult), + .cipRequireMatchingLdRefs( + ldReference(fineMappingResult), gwasLd, label = "fineMappingResult" ) @@ -256,14 +258,14 @@ causalInferencePipeline <- function( .cipCvSelection <- function( qtlRows, twasWeights, - weightSelectionArgs + weightSelectionParam ) { # The bundle's terminal. .cipMethodMetrics / .cipEligibleRowsIn below # take plain scalars, so it is unrolled once, here. - rsqCutoff <- weightSelectionArgs$cutoff - rsqPvalCutoff <- weightSelectionArgs$pvalCutoff - rsqOption <- weightSelectionArgs$metric - rsqPvalOption <- weightSelectionArgs$pvalMetric + rsqCutoff <- weightSelectionParam$cutoff + rsqPvalCutoff <- weightSelectionParam$pvalCutoff + rsqOption <- weightSelectionParam$metric + rsqPvalOption <- weightSelectionParam$pvalMetric selectionActive <- !is.null(twasWeights) && (rsqCutoff > 0 || is.finite(rsqPvalCutoff)) if (!selectionActive) { @@ -309,7 +311,7 @@ causalInferencePipeline <- function( gwasSumStats, gwasLd, alleleFlip, - mrArgs + mrParam ) { tuple <- list( qStudy = qtlRows$qtlStudy[[qi]], @@ -345,11 +347,12 @@ causalInferencePipeline <- function( gwasSumStats = gwasSumStats, gwasLd = gwasLd, alleleFlip = alleleFlip, - mrArgs = mrArgs + mrParam = mrParam )) } -# Resolve the FineMappingRow for a tuple (NULL when the fmr lacks it). +# The tuple's single-row collection from `fineMappingResult` (NULL when it +# lacks the tuple). # @noRd .cipResolveFmrEntry <- function( fineMappingResult, @@ -383,7 +386,7 @@ causalInferencePipeline <- function( gwasSumStats, gwasLd, alleleFlip, - mrArgs + mrParam ) { gStudy <- as.character(gwasSumStats$study)[[gi]] gdf <- as.data.frame( @@ -407,7 +410,7 @@ causalInferencePipeline <- function( gdf, twasOut, alleleFlip = alleleFlip, - mrArgs = mrArgs + mrParam = mrParam ) .cipResultRow(tuple, gStudy, twasOut, mrOut) } @@ -430,13 +433,13 @@ causalInferencePipeline <- function( gdf, twasOut, alleleFlip, - mrArgs + mrParam ) { # The bundle's terminal; the MR engines below take plain scalars. - mrMethod <- mrArgs$method - mrPipCutoff <- mrArgs$pipCutoff - mrCpipCutoff <- mrArgs$cpipCutoff - mrPvalCutoff <- mrArgs$pvalCutoff + mrMethod <- mrParam$method + mrPipCutoff <- mrParam$pipCutoff + mrCpipCutoff <- mrParam$cpipCutoff + mrPvalCutoff <- mrParam$pvalCutoff mrGateOpen <- mrPvalCutoff >= 1 || (!is.na(twasOut$pval) && twasOut$pval < mrPvalCutoff) if (is.null(fmrEntry) || !mrGateOpen) { @@ -525,9 +528,9 @@ causalInferencePipeline <- function( # ============================================================================= # Check two LD sketches for reference-panel compatibility. Thin wrapper over -# the shared `.requireMatchingLdSketches` helper (R/ld.R). -.cipRequireMatchingLdSketches <- function(qtlLd, gwasLd, label) { - .requireMatchingLdSketches( +# the shared `.requireMatchingLdRefs` helper (R/ld.R). +.cipRequireMatchingLdRefs <- function(qtlLd, gwasLd, label) { + .requireMatchingLdRefs( qtlLd, gwasLd, pipelineName = "causalInferencePipeline", @@ -744,7 +747,7 @@ causalInferencePipeline <- function( } # Fetch the per-tuple weight-source entry (a TwasWeightsRow from -# `twasWeights`, or a FineMappingRow from `fineMappingResult`) and resolve it +# `twasWeights`, or the tuple's row of `fineMappingResult`) and resolve it # to an aligned (variantIds, weights) pair via the shared `resolveWeights` -- # the SuSiE-style posterior effect for the FMR path. Returns NULL when the tuple # is absent or has no usable weights. @@ -805,7 +808,7 @@ causalInferencePipeline <- function( ) } -# FineMappingResult entry for a tuple (NULL when absent). +# The fine-mapping entry for a tuple (NULL when absent). # @noRd .cipWeightsFromFmr <- function( fineMappingResult, @@ -856,9 +859,7 @@ causalInferencePipeline <- function( wSub <- weights[m$idxA] * m$sign zSub <- gwasDf$z[m$idxB] gwasIds <- gwasDf$variant_id[m$idxB] - ldMat <- .ldFromSketch(gwasLd, gwasIds, label = "causalInferencePipeline") - - res <- twasZ(weights = wSub, z = zSub, R = ldMat) + res <- .cipTwasZ(wSub, zSub, gwasLd, gwasIds) zMat <- res$Z zVal <- as.numeric(zMat[1L, "Z"]) pVal <- as.numeric(zMat[1L, "pval"]) @@ -875,6 +876,22 @@ causalInferencePipeline <- function( ) } +# twasZ() of weights `w` against GWAS z-scores `z` over the GWAS variants +# `gwasIds`, needing w'Rw only: a genotype reference gives its LD factor, so +# the p x p LD is never formed; an LdMatrix gives the matrix. +# @noRd +.cipTwasZ <- function(w, z, gwasLd, gwasIds) { + label <- "causalInferencePipeline" + if (.ldIsGenotypeRef(gwasLd)) { + return(twasZ( + weights = w, + z = z, + X = .ldFactorFromRef(gwasLd, gwasIds, label) + )) + } + twasZ(weights = w, z = z, R = .ldFromRef(gwasLd, gwasIds, label)) +} + # Report how many QTL weight variants failed to match the GWAS sumstats. # Dropping below two matches yields no TWAS Z at all, which used to be a silent # NULL, so that case is called out separately. @@ -957,9 +974,6 @@ causalInferencePipeline <- function( ) } -# Instrumental variables from a FineMappingRow's topLoci: variants with PIP > -# pipCutoff. Returns list(ivVars, betaX, seX) or NULL when none qualify. -# @noRd # The per-variant top-loci table of one entry. topLoci() returns ranges and # mcols() is exactly the pre-conversion table -- as.data.frame() on the # ranges would add the standard seqnames/start/end/width/strand columns the @@ -971,6 +985,9 @@ causalInferencePipeline <- function( as.data.frame(S4Vectors::mcols(topLoci(fmrEntry))) } +# Instrumental variables from a FineMappingRow's topLoci: variants with PIP > +# pipCutoff. Returns list(ivVars, betaX, seX) or NULL when none qualify. +# @noRd .cipMrInstruments <- function(fmrEntry, pipCutoff) { tl <- .cipTopLociTable(fmrEntry) if (is.null(tl) || nrow(tl) == 0L) { @@ -1014,7 +1031,8 @@ causalInferencePipeline <- function( # # Logic: # 1. Group topLoci variants by credible set (column `cs` in topLoci, or -# first column matching ^cs). +# first column matching ^cs -- cs_95 on a stored result, whose +# "_" labels are parsed to the set index). # 2. Per CS, compute cumulative PIP; drop CSs with cpip < cpipCutoff. # 3. Per surviving CS, compute a PIP-weighted composite Wald ratio # `composite_bhat = sum((bhatY/bhatX * pip) / cpip)` with the @@ -1083,7 +1101,10 @@ causalInferencePipeline <- function( # with NA cs / non-positive cs / NA pip / NA beta. Returns NULL when none valid. # @noRd .cipCsAwareRaw <- function(tl, cols) { - cs <- as.integer(tl[[cols$cs[[1L]]]]) + csCol <- tl[[cols$cs[[1L]]]] + # A stored topLoci table labels each variant's set "_" + # (cs_95); a caller-built table may carry the bare integer instead. + cs <- if (is.character(csCol)) .csLabelIndex(csCol) else as.integer(csCol) pip <- as.numeric(tl[[cols$pip[[1L]]]]) bhatX <- as.numeric(tl[[cols$beta[[1L]]]]) sbhatX <- as.numeric(tl[[cols$se[[1L]]]]) @@ -1367,9 +1388,9 @@ causalInferencePipeline <- function( list_c(pieces) } -# One combined row: the group's first row re-stamped with the combined -# p-value for method `m`. The per-method statistics do not carry over, so -# they are blanked rather than inherited. +# One combined row: the group's first row, carrying the combined p-value for +# method `m` in place of its own. The per-method statistics do not carry +# over, so they are blanked rather than inherited. # @noRd .cipCombinedRow <- function(m, mc, rows, cp) { mutate( @@ -1473,17 +1494,39 @@ causalInferencePipeline <- function( V } -# R / X path: covY = W^T R W (R computed from X when R is absent). +# R / X path: covY = W^T R W. From a genotype matrix X the LD is never formed: +# W^T R W = crossprod(F W) / (n - 1) for F, X's LD factor +# (.ldFactorFromDosage()), which is computeLd()'s LD exactly. # @noRd .twasZCovYR <- function(weights, R, X, rn) { - if (is.null(R)) { - if (is.null(X)) { - abort("twasZ: provide R, X, or the (V, D, nSketch) SVD triplet.") + if (!is.null(R)) { + rSub <- .twasZAlignR(R, rn, nrow(weights)) + return(list(covY = crossprod(weights, rSub) %*% weights)) + } + if (is.null(X)) { + abort("twasZ: provide R, X, or the (V, D, nSketch) SVD triplet.") + } + factor <- .ldFactorFromDosage(.twasZAlignX(X, rn, nrow(weights))) + list(covY = crossprod(factor %*% weights) / (nrow(factor) - 1)) +} + +# Column-align X to the weights (by name when available, else positionally). +# @noRd +#' @importFrom checkmate assertMatrix +.twasZAlignX <- function(X, rn, nW) { + if (!is.null(colnames(X)) && !is.null(rn)) { + idx <- match(rn, colnames(X)) + if (anyNA(idx)) { + msg <- glue( + "twasZ: X is missing columns for {sum(is.na(idx))} ", + "variant(s) named in weights." + ) + abort(msg) } - R <- computeLd(X) + return(X[, idx, drop = FALSE]) } - rSub <- .twasZAlignR(R, rn, nrow(weights)) - list(covY = crossprod(weights, rSub) %*% weights) + assertMatrix(X, ncols = nW, .var.name = "X (positional alignment)") + X } # Symmetric-align R to the weights (by name when available, else positionally). @@ -1528,8 +1571,10 @@ causalInferencePipeline <- function( #' The SVD path (\code{V}, \code{D}, \code{nSketch}) lets the caller avoid #' materializing the full LD matrix: \code{covY = (V^TW * sqrt(Lambda))^T (V^TW #' * sqrt(Lambda))} with \code{Lambda_i = D_i^2 / (nSketch - 1)}. Use the -#' \code{R} path when an LD correlation matrix is already available; use the -#' \code{X} path to compute \code{R} from a genotype matrix. +#' \code{R} path when an LD correlation matrix is already available; the +#' \code{X} path works from a genotype matrix directly, as +#' \code{covY = (F W)^T (F W) / (n - 1)} for its standardized dosages +#' \code{F}, never forming \code{R}. #' #' @param weights Numeric vector of weights (single tuple) or a numeric matrix #' with one column per tuple (method / context). When a vector, the column @@ -1537,8 +1582,9 @@ causalInferencePipeline <- function( #' @param z Numeric vector of GWAS Z-scores aligned to the rows of #' \code{weights}. #' @param R Optional LD correlation matrix. -#' @param X Optional genotype matrix used to compute \code{R} when \code{R} is -#' missing. +#' @param X Optional genotype matrix (samples x variants) standing in for +#' \code{R} when \code{R} is missing; its LD is taken as +#' \code{\link{computeLd}}'s, without forming it. #' @param V,D,nSketch SVD components of the LD sketch (right-singular vectors, #' singular values, panel sample size). Supplying all three selects the SVD #' path. diff --git a/R/colocPipeline.R b/R/colocPipeline.R index f3e301843..d297e2267 100644 --- a/R/colocPipeline.R +++ b/R/colocPipeline.R @@ -25,9 +25,9 @@ NULL # The three argument groups, checked in one place so the pipeline body does # not open with three near-identical lines. # @noRd -.colocAssertGroups <- function(priors, lbfFilterArgs, methodArgs) { - .assertMethodParam(priors, "ColocPriorParam", "priors") - .assertMethodParam(lbfFilterArgs, "ColocLbfFilterParam", "lbfFilter") +.colocAssertGroups <- function(priors, colocLbfFilterParam, methodArgs) { + .assertMethodParam(priors, "ColocPriorParam") + .assertMethodParam(colocLbfFilterParam, "ColocLbfFilterParam") .assertMethodOptions(methodArgs, "ColocOptions", "methodArgs") } @@ -175,8 +175,8 @@ setClass( #' \code{\link{fineMappingPipeline}}; the settings that pipeline exposes #' but that cannot apply here are deliberately absent: #' \code{fineMappingResult} (the pipeline is being asked to produce one), -#' \code{crossValidation} (refused on summary statistics) and -#' \code{residualization} (nothing to regress out). +#' \code{crossValidationParam} (refused on summary statistics) and +#' \code{residualizationParam} (nothing to regress out). #' #' The whole bundle is inert when \code{gwasInput} is already a #' fine-mapping result, since no fit is run. @@ -260,7 +260,7 @@ GwasFineMappingParam <- function( #' \item \strong{fSuSiE support}: the LBF matrix lives at a different #' slot for fSuSiE fits (\code{fsusie_result$lBF}) and gets stacked #' into a single matrix. -#' \item \strong{Effect filtering}: \code{lbfFilter}'s +#' \item \strong{Effect filtering}: \code{colocLbfFilterParam}'s #' \code{filterLbfCs} keeps only effects that produced a credible #' set, its \code{secondary} keeps effects at a secondary coverage; #' otherwise the default filter drops effects whose prior variance is @@ -282,16 +282,18 @@ GwasFineMappingParam <- function( #' inline fine-mapping. #' } #' -#' @section LD-sketch compatibility check: If -#' \code{ldSketch(qtlFineMappingResult)} is non-\code{NULL}, it must come -#' from the same reference panel as the LD sketch on \code{gwasInput}: the -#' same samples, and the same allele orientation on the variants the two -#' carry in common. The two need NOT carry the same variants --- running -#' \code{\link{summaryStatsQc}} on the two sides separately normally leaves -#' each sketch trimmed to its own surviving variants, and LD is looked up per -#' variant, so a partial overlap only warns (once per session). No shared -#' variant at all, a different sample set, or a swapped A1/A2 on a shared -#' variant is a hard error. When the first side's \code{ldSketch} is +#' @section LD reference compatibility check: If +#' \code{ldReference(qtlFineMappingResult)} is non-\code{NULL}, it must be +#' the same LD reference as the one on \code{gwasInput}. +#' The two must be the same kind of reference. Two genotype panels must +#' share their samples and allele orientation; two \code{LdMatrix} objects +#' must agree on the LD they share; two \code{GenotypeMixture} objects must +#' average the same panels with the same weights. The two need NOT +#' carry the same variants --- running \code{\link{summaryStatsQc}} on the +#' two sides separately normally leaves each reference trimmed to its own +#' surviving variants, and LD is looked up per variant, so a partial overlap +#' only warns (once per session). No shared variant at all, or a mismatch of +#' any of those, is a hard error. When the first side's \code{ldReference} is #' \code{NULL} (individual-level fit), the validation is skipped on that side #' and the second side's panel is what the result carries forward. #' @@ -302,7 +304,7 @@ GwasFineMappingParam <- function( #' \code{\link{GwasFineMappingResult}}, or the summary statistics to #' fine-map inline (\code{\link{QtlSumStats}} or #' \code{\link{GwasSumStats}}). -#' @param lbfFilterArgs Credible-set filtering applied before scoring, +#' @param colocLbfFilterParam Credible-set filtering applied before scoring, #' built with #' \code{\link{ColocLbfFilterParam}}. \code{filterLbfCs} keeps only effects #' that produced a credible set; supplying \code{secondary} instead runs a @@ -317,7 +319,7 @@ GwasFineMappingParam <- function( #' \code{\link{ColocPriorParam}}: \code{p1} (QTL signal), \code{p2} (GWAS #' signal) and \code{p12} (shared). Mutually exclusive with #' \code{enrichment}, which derives the same three. -#' @param gwasFineMappingArgs How \code{gwasInput} is fine-mapped when it is +#' @param gwasFineMappingParam How \code{gwasInput} is fine-mapped when it is #' summary statistics rather than a fine-mapping result, built with #' \code{\link{GwasFineMappingParam}}: \code{methods}, #' \code{credibleSetParam}, \code{susieRssParam}, @@ -405,8 +407,8 @@ colocPipeline <- function( qtlFineMappingResult, gwasInput, priors = ColocPriorParam(), - lbfFilterArgs = ColocLbfFilterParam(), - gwasFineMappingArgs = GwasFineMappingParam(), + colocLbfFilterParam = ColocLbfFilterParam(), + gwasFineMappingParam = GwasFineMappingParam(), returnGwasFineMapping = FALSE, enrichment = NULL, adjustPips = TRUE, @@ -417,17 +419,17 @@ colocPipeline <- function( qtlFineMappingResult = qtlFineMappingResult, gwasInput = gwasInput, priors = priors, - gwasFineMappingArgs = gwasFineMappingArgs, + gwasFineMappingParam = gwasFineMappingParam, enrichment = enrichment, adjustPips = adjustPips, methodArgs = methodArgs, - lbfFilterArgs = lbfFilterArgs, + colocLbfFilterParam = colocLbfFilterParam, priorsMissing = missing(priors) ) - # Pre-extract per-GWAS-tuple LBF matrices: group the GWAS FMR by study, - # stack each study's LBF rows, and store per-(study, method) batched - # matrices (reproduces the legacy row-wise combine per xQTL). - gwasLbfByPair <- .colocPreextractGwasLbf(prepared$gwasFmr, lbfFilterArgs) + gwasLbfByPair <- .colocPreextractGwasLbf( + prepared$gwasFmr, + colocLbfFilterParam + ) # Both exits -- no scorable GWAS effect, and the scored result -- shape # their output from the same five values, so they are named once. shape <- list( @@ -443,7 +445,7 @@ colocPipeline <- function( results <- .colocScoreAll( prepared, gwasLbfByPair, - lbfFilterArgs = lbfFilterArgs, + colocLbfFilterParam = colocLbfFilterParam, enrichment = enrichment, priors = priors, alleleFlip = alleleFlip @@ -456,7 +458,7 @@ colocPipeline <- function( .colocScoreAll <- function( prepared, gwasLbfByPair, - lbfFilterArgs, + colocLbfFilterParam, enrichment, priors, alleleFlip @@ -466,7 +468,7 @@ colocPipeline <- function( .colocScoreQtlTuple, qtlFineMappingResult = prepared$qtlFineMappingResult, gwasLbfByPair = gwasLbfByPair, - lbfFilterArgs = lbfFilterArgs, + colocLbfFilterParam = colocLbfFilterParam, useEnrichment = prepared$useEnrichment, enrichment = enrichment, priors = priors, @@ -484,14 +486,14 @@ colocPipeline <- function( qtlFineMappingResult, gwasInput, priors, - gwasFineMappingArgs, + gwasFineMappingParam, enrichment, adjustPips, methodArgs, - lbfFilterArgs, + colocLbfFilterParam, priorsMissing ) { - .colocAssertGroups(priors, lbfFilterArgs, methodArgs) + .colocAssertGroups(priors, colocLbfFilterParam, methodArgs) .colocRefusePriorsWithEnrichment(priors, enrichment, priorsMissing) useEnrichment <- !is.null(enrichment) .colocValidateInputs( @@ -500,10 +502,10 @@ colocPipeline <- function( enrichment = enrichment, useEnrichment = useEnrichment ) - rawGwasFmr <- .colocResolveGwasFmr(gwasInput, gwasFineMappingArgs) - .colocRequireMatchingLdSketches( - ldSketch(qtlFineMappingResult), - ldSketch(rawGwasFmr) + rawGwasFmr <- .colocResolveGwasFmr(gwasInput, gwasFineMappingParam) + .colocRequireMatchingLdRefs( + ldReference(qtlFineMappingResult), + ldReference(rawGwasFmr) ) adjusted <- .colocMaybeAdjustPips( adjustPips = adjustPips, @@ -598,17 +600,16 @@ colocPipeline <- function( # Resolve the second side to a fine-mapping collection (fine-map QC'd sumstats # when summary statistics are passed, whichever flavour they are). # @noRd -.colocResolveGwasFmr <- function(gwasInput, gwasFineMappingArgs) { +.colocResolveGwasFmr <- function(gwasInput, gwasFineMappingParam) { if (methods::is(gwasInput, "FineMappingResultBase")) { # Already fine-mapped: nothing in the bundle applies, so a setting # given here would be dropped rather than honoured. - .colocAssertGwasFmrUnset(gwasFineMappingArgs, class(gwasInput)[[1L]]) + .colocAssertGwasFmrUnset(gwasFineMappingParam, class(gwasInput)[[1L]]) return(gwasInput) } .assertMethodParam( - gwasFineMappingArgs, - "GwasFineMappingParam", - "gwasFineMapping" + gwasFineMappingParam, + "GwasFineMappingParam" ) if (length(qcInfo(gwasInput)) == 0L) { msg <- glue( @@ -619,44 +620,44 @@ colocPipeline <- function( } # The bundle's terminal: fineMappingPipeline takes these as its own # arguments, so it is unrolled once, here. + withSusieInf <- gwasFineMappingParam$initializeWithSusieInf %||% TRUE fineMappingPipeline( gwasInput, - methods = gwasFineMappingArgs$methods %||% "susie", - credibleSetParam = gwasFineMappingArgs$credibleSetParam %||% + methods = gwasFineMappingParam$methods %||% "susie", + credibleSetParam = gwasFineMappingParam$credibleSetParam %||% CredibleSetParam(), - susieRssParam = gwasFineMappingArgs$susieRssParam %||% SusieRssParam(), - panelFilterParam = gwasFineMappingArgs$panelFilterParam %||% + susieRssParam = gwasFineMappingParam$susieRssParam %||% SusieRssParam(), + panelFilterParam = gwasFineMappingParam$panelFilterParam %||% PanelFilterParam(), - initializeWithSusieInf = gwasFineMappingArgs$initializeWithSusieInf %||% - TRUE, - fitRetention = gwasFineMappingArgs$fitRetention %||% "slim" + initializeWithSusieInf = withSusieInf, + fitRetention = gwasFineMappingParam$fitRetention %||% "slim" ) } -# `gwasFineMapping` configures the fine-mapping run colocPipeline does on a -# raw GWAS input. A caller who has already fine-mapped the GWAS has no such +# `gwasFineMappingParam` configures the fine-mapping run colocPipeline does on +# a raw GWAS input. A caller who has already fine-mapped the GWAS has no such # run, so a non-default bundle is an instruction that cannot be carried out. # @noRd -.colocAssertGwasFmrUnset <- function(gwasFineMappingArgs, inputClass) { - if (!.isMethodParam(gwasFineMappingArgs)) { +.colocAssertGwasFmrUnset <- function(gwasFineMappingParam, inputClass) { + if (!.isMethodParam(gwasFineMappingParam)) { return(invisible(NULL)) } defaults <- GwasFineMappingParam() set <- keep( names(defaults), .colocGwasFmrFieldSet, - given = gwasFineMappingArgs, + given = gwasFineMappingParam, defaults = defaults ) if (length(set) == 0L) { return(invisible(NULL)) } abort(glue( - "colocPipeline: `gwasFineMapping` ", + "colocPipeline: `gwasFineMappingParam` ", "({str_flatten(str_c(set, ' ='), ', ')}) configures the GWAS ", "fine-mapping run, but `gwasInput` is already a ", "{inputClass} -- there is no run to configure. ", - "Drop `gwasFineMapping`, or pass the raw GwasSumStats instead." + "Drop `gwasFineMappingParam`, or pass the raw GwasSumStats instead." )) } @@ -695,12 +696,12 @@ colocPipeline <- function( # The LD reference the result carries forward, so colocCredibleSets() can # recompute purity (section 3.7) without being handed a sketch separately. The # two sides' sketches are already required to match by -# .colocRequireMatchingLdSketches, so either one identifies the panel -- but a +# .colocRequireMatchingLdRefs, so either one identifies the panel -- but a # first side fit on individual-level data carries none, and then the second # side's panel is the only one there is. # @noRd -.colocLdSketch <- function(qtlFineMappingResult, gwasFmr) { - ldSketch(qtlFineMappingResult) %||% ldSketch(gwasFmr) +.colocLdRef <- function(qtlFineMappingResult, gwasFmr) { + ldReference(qtlFineMappingResult) %||% ldReference(gwasFmr) } # Empty-result early return (attaching the GWAS fine-mapping when requested). @@ -714,7 +715,7 @@ colocPipeline <- function( ) { out <- .colocEmptyResult( enriched = useEnrichment, - ldSketch = .colocLdSketch(qtlFineMappingResult, gwasFmr) + ldReference = .colocLdRef(qtlFineMappingResult, gwasFmr) ) .colocAttachGwasFm(out, returnGwasFineMapping, gwasInput, gwasFmr) } @@ -725,7 +726,7 @@ colocPipeline <- function( qi, qtlFineMappingResult, gwasLbfByPair, - lbfFilterArgs, + colocLbfFilterParam, useEnrichment, enrichment, priors, @@ -735,7 +736,7 @@ colocPipeline <- function( q <- .colocQtlTupleInfo(qi, qtlFineMappingResult) qLbfInfo <- .colocExtractLbfFromEntry( q$parts, - lbfFilterArgs, + colocLbfFilterParam, label = q$label ) if (is.null(qLbfInfo)) { @@ -1050,7 +1051,7 @@ colocPipeline <- function( out <- .colocAssemble( results, useEnrichment, - .colocLdSketch(qtlFineMappingResult, gwasFmr) + .colocLdRef(qtlFineMappingResult, gwasFmr) ) .colocAttachGwasFm(out, returnGwasFineMapping, gwasInput, gwasFmr) } @@ -1067,13 +1068,16 @@ colocPipeline <- function( # Row-bind + column-order the per-pair summary rows (empty result when none). # @noRd -.colocAssemble <- function(results, useEnrichment, ldSketch = NULL) { +.colocAssemble <- function(results, useEnrichment, ldReference = NULL) { if (length(results) == 0L) { - return(.colocEmptyResult(enriched = useEnrichment, ldSketch = ldSketch)) + return(.colocEmptyResult( + enriched = useEnrichment, + ldReference = ldReference + )) } rows <- bind_rows(map(map(results, "rows"), .colocStandardiseRow)) variants <- list_c(map(results, "variants")) - ColocResult(.colocOrderColumns(rows, useEnrichment), variants, ldSketch) + ColocResult(.colocOrderColumns(rows, useEnrichment), variants, ldReference) } # Identity columns first, then everything coloc produced. @@ -1101,11 +1105,11 @@ colocPipeline <- function( # ============================================================================= # LD-sketch compatibility check. Thin wrapper over the shared -# `.requireMatchingLdSketches` helper (R/ld.R). Shared with +# `.requireMatchingLdRefs` helper (R/ld.R). Shared with # qtlEnrichmentPipeline. # @noRd -.colocRequireMatchingLdSketches <- function(qtlLd, gwasLd) { - .requireMatchingLdSketches(qtlLd, gwasLd, pipelineName = "colocPipeline") +.colocRequireMatchingLdRefs <- function(qtlLd, gwasLd) { + .requireMatchingLdRefs(qtlLd, gwasLd, pipelineName = "colocPipeline") } # SuSiE credible-set concentration filter. Given a trimmed SuSiE fit @@ -1136,7 +1140,8 @@ colocPipeline <- function( # Extract an LBF matrix (effects x variants) from a FineMappingRow, # applying the same filtering knobs as the legacy .extractLbfMatrix, now -# carried as one `lbfFilter` bundle rather than three loose arguments: +# carried as one `colocLbfFilterParam` bundle rather than three loose +# arguments: # - filterLbfCs (CS-only) # - secondary (secondary coverage CS, with a concentration cutoff) # - priorTol drop on V (default) @@ -1146,7 +1151,7 @@ colocPipeline <- function( # @noRd .colocExtractLbfFromEntry <- function( parts, - lbfFilterArgs, + colocLbfFilterParam, label = "entry" ) { fit <- susieFit(parts) @@ -1161,7 +1166,7 @@ colocPipeline <- function( # stays a skip; it is only an ABSENT LBF that is an error. allRows <- .colocLbfMatrix(fit, label) allMass <- .colocEffectRetainedMass(fit, nrow(allRows)) - keep <- .colocSelectLbfRows(allRows, fit, lbfFilterArgs) + keep <- .colocSelectLbfRows(allRows, fit, colocLbfFilterParam) kept <- allRows[keep, , drop = FALSE] mass <- allMass[keep] if (nrow(kept) == 0L) { @@ -1223,25 +1228,28 @@ colocPipeline <- function( # matching when the exact name is absent, so a fit carrying `sets_secondary` # but no `sets` would silently filter on the wrong element. # @noRd -.colocSelectLbfRows <- function(lbfMatrix, fit, lbfFilterArgs) { +.colocSelectLbfRows <- function(lbfMatrix, fit, colocLbfFilterParam) { # Read off the bundle like the other three filter fields, rather than # taken as a second copy of a value the caller already has in hand. - priorTol <- lbfFilterArgs$priorTol %||% 1e-9 + priorTol <- colocLbfFilterParam$priorTol %||% 1e-9 # The only place the filter's fields are read. Every function between # here and colocPipeline carries the bundle whole, so the names cannot # drift apart on the way down. allRows <- seq_len(nrow(lbfMatrix)) - if (isTRUE(lbfFilterArgs$filterLbfCs) && is.null(lbfFilterArgs$secondary)) { + if ( + isTRUE(colocLbfFilterParam$filterLbfCs) && + is.null(colocLbfFilterParam$secondary) + ) { csIdx <- fit[["sets"]][["cs_index"]] if (!is.null(csIdx) && length(csIdx) > 0L) { return(csIdx) } - } else if (!is.null(lbfFilterArgs$secondary)) { + } else if (!is.null(colocLbfFilterParam$secondary)) { secIdx <- try_fetch( .colocFilterCsByConcentration( fit, - coverage = lbfFilterArgs$secondary, - concentration = lbfFilterArgs$concentration + coverage = colocLbfFilterParam$secondary, + concentration = colocLbfFilterParam$concentration ), error = function(cnd) NULL ) @@ -1299,7 +1307,7 @@ colocPipeline <- function( # another's. Nothing downstream indexes this list by name, so positional # records make the collision impossible instead of merely unlikely. # @noRd -.colocPreextractGwasLbf <- function(gwasFmr, lbfFilterArgs) { +.colocPreextractGwasLbf <- function(gwasFmr, colocLbfFilterParam) { if (nrow(gwasFmr) == 0L) { return(list()) } @@ -1309,7 +1317,7 @@ colocPipeline <- function( gwasFmr = gwasFmr, blockIds = .colocGwasBlockIds(gwasFmr), side = .fmrSideName(gwasFmr), - lbfFilterArgs = lbfFilterArgs + colocLbfFilterParam = colocLbfFilterParam )) } @@ -1321,14 +1329,14 @@ colocPipeline <- function( gwasFmr, blockIds, side, - lbfFilterArgs + colocLbfFilterParam ) { ident <- .colocTupleIdentity(gwasFmr, ri) blockId <- blockIds[[ri]] label <- .fmrTupleLabel(side, ident, block = blockId) info <- .colocExtractLbfFromEntry( .fmrRowParts(gwasFmr, ri), - lbfFilterArgs, + colocLbfFilterParam, label = label ) if (is.null(info)) { @@ -1387,7 +1395,7 @@ colocPipeline <- function( # do not have to special-case a NULL return. When `enriched = TRUE` the # enrichment + p12Used columns are appended (the enloc-mode schema). # @noRd -.colocEmptyResult <- function(enriched = FALSE, ldSketch = NULL) { +.colocEmptyResult <- function(enriched = FALSE, ldReference = NULL) { base <- tibble( study = character(0), context = character(0), @@ -1414,7 +1422,7 @@ colocPipeline <- function( ColocResult( mutate(base, !!!.colocEnrichmentCols(enriched, NULL)), list(), - ldSketch = ldSketch + ldReference = ldReference ) } @@ -1542,9 +1550,6 @@ colocPipeline <- function( # ---- map/apply helpers (lambda-free callbacks) --------------------------- -# The variant ids of one fine-mapping entry (S4 slot; not pluckable by name). -# @noRd - # Score the first side's LBF against one second-side record -> a summary row # (or NULL). # @noRd diff --git a/R/colocboostPipeline.R b/R/colocboostPipeline.R index 592ad5c77..633b86dfc 100644 --- a/R/colocboostPipeline.R +++ b/R/colocboostPipeline.R @@ -8,7 +8,7 @@ #' extracted from the dataset (filtering knobs on the #' constructor apply lazily inside the accessors). #' \item \code{QtlSumStats} -- summary-statistic-only QTL data with a -#' shared LD reference (\code{ldSketch}). Must already have +#' shared LD reference (\code{ldReference}). Must already have #' been passed through \code{\link{summaryStatsQc}} (the #' pipeline rejects inputs whose \code{qcInfo()} is empty). #' Every analysis variant is available on this input, including @@ -21,7 +21,7 @@ #' GWAS is optional and always passed separately as a #' \code{GwasSumStats} object (must also be QC'd). #' -#' \code{colocboostPipeline} does \strong{not} accept a \code{FineMappingResult} +#' \code{colocboostPipeline} does \strong{not} accept a fine-mapping result #' for either side; colocboost has its own variable-selection algorithm. #' #' @section QC contract: @@ -32,17 +32,17 @@ #' \code{genotypes()} / \code{residualizedGenotypes()}. #' The pipeline does \emph{not} run a separate #' individual-level QC pass. -#' \item \code{panelFilter} acts at ANALYSIS time on the sumstat +#' \item \code{panelFilterParam} acts at ANALYSIS time on the sumstat #' sides, measured against the \strong{LD reference panel}: a #' variant whose panel genotypes fall below its cutoffs is dropped #' before the LD matrix is built. It applies to a #' \code{QtlSumStats} QTL side and to a \code{GwasSumStats} GWAS #' side (so it still bites when the QTL side is #' individual-level). Its defaults filter nothing. Unlike -#' \code{summaryStatsQc(imputeArgs = ...)}, which only bounds what +#' \code{summaryStatsQc(raissParam = ...)}, which only bounds what #' RAISS will impute, these discard \emph{observed} variants. #' \item All summary-statistic QC (variant filters, harmonization -#' against the \code{ldSketch}, LD-mismatch detection, RAISS +#' against the \code{ldReference}, LD-mismatch detection, RAISS #' imputation, etc.) lives in #' \code{\link{summaryStatsQc}}. The pipeline rejects any #' \code{QtlSumStats} or \code{GwasSumStats} where @@ -96,7 +96,7 @@ #' GWAS study focal. #' @param xqtlColoc,jointGwas,separateGwas Logical flags selecting which #' colocboost variants to run. -#' @param signalScreenArgs Individual-level pre-filter (ports the legacy +#' @param signalScreenParam Individual-level pre-filter (ports the legacy #' \code{pip_cutoff_to_skip_ind}), built with #' \code{\link{SignalScreenParam}}. For each context every outcome is fit #' with a single-effect SuSiE (\code{L = 1}) and dropped unless the chosen @@ -121,7 +121,7 @@ #' below its cutoffs is dropped before the LD matrix is built. The defaults #' filter nothing. See the Details section for how these relate to the #' individual-level cutoffs recorded on a \code{QtlDataset}. -#' @param residualizationArgs Covariate residualization settings for +#' @param residualizationParam Covariate residualization settings for #' individual-level input, built with \code{\link{ResidualizationParam}}. #' Reaches both \code{\link{residualizedGenotypes}} and #' \code{\link{residualizedPhenotypes}}, so it governs what is regressed @@ -243,16 +243,16 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # Helpers (private) # ============================================================================= -# Build the LD / X_ref slot of the colocboost call from a list of LD -# matrices. When any matrix is non-square it is treated as a samples x -# variants genotype reference and routed to X_ref; otherwise routed to LD. -.cbBuildLdArgs <- function(ldList) { +# Build the LD / X_ref slot of the colocboost call from a bundle's LD list. +# `ldArg` says which the list holds -- LD matrices ("LD") or factors of them +# ("X_ref"), which colocboost correlates itself -- as .cbMergeSumstatBundles() +# records it; a factor can be square by coincidence, so its shape cannot say. +.cbBuildLdArgs <- function(ldList, ldArg = "LD") { ldList <- compact(ldList) if (length(ldList) == 0L) { return(list()) } - isGeno <- any(map_lgl(ldList, .cbIsNonSquare)) - if (isGeno) list(X_ref = ldList) else list(LD = ldList) + set_names(list(ldList), ldArg) } # Reject SumStats objects that have not been passed through @@ -308,7 +308,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { cisWindow = NULL, samples = NULL, pipCutoffToSkip = 0, - residualizationArgs = ResidualizationParam() + residualizationParam = ResidualizationParam() ) { contexts <- .cbResolveBundleContexts(qd, contexts) p <- list( @@ -318,7 +318,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { cisWindow = cisWindow, samples = samples, pipCutoffToSkip = pipCutoffToSkip, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) built <- compact(map(contexts, .cbBuildContextXY, p = p)) if (length(built) == 0L) { @@ -373,7 +373,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { ctx, p$traitId, p$region, - p$residualizationArgs + p$residualizationParam ) if (is.null(rawY) || ncol(rawY) == 0L) { return(NULL) @@ -385,7 +385,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { p$region, p$cisWindow, p$samples, - p$residualizationArgs + p$residualizationParam ) if (is.null(rawX) || ncol(rawX) == 0L) { return(NULL) @@ -425,7 +425,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { ctx, traitId, region, - residualizationArgs = ResidualizationParam() + residualizationParam = ResidualizationParam() ) { try_fetch( residualizedPhenotypes( @@ -433,7 +433,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { contexts = ctx, traitId = traitId, region = region, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ), error = function(cnd) { msg <- glue( @@ -455,7 +455,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { region, cisWindow, samples, - residualizationArgs = ResidualizationParam() + residualizationParam = ResidualizationParam() ) { try_fetch( residualizedGenotypes( @@ -465,7 +465,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { region = region, cisWindow = cisWindow, samples = samples, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ), error = function(cnd) { msg <- glue( @@ -621,10 +621,10 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # Build a single (sumstat data.frame, LD correlation matrix) pair from a # QtlSumStats / GwasSumStats entry. Returns NULL when the entry has no -# variants overlapping the ldSketch panel. +# variants overlapping the ldReference panel. .cbSumstatPair <- function( df, - ldSketch, + ldReference, varY = NULL, nCase = NULL, nControl = NULL, @@ -644,35 +644,42 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # the two. wanted <- variantIds[.panelKeepMask( variantIds, - ldSketch, + ldReference, cutoffs, ".cbSumstatPair" )] if (length(wanted) == 0L) { return(NULL) } - # Use the shared `.ldFromSketch` helper in "drop" mode so missing-from-panel - # variants are silently filtered (the colocboost path expects to operate - # only on the overlap). - R <- .ldFromSketch( - ldSketch, - wanted, - label = ".cbSumstatPair", - onMissing = "drop" - ) - if (is.null(R)) { + ld <- .cbPairLd(ldReference, wanted) + if (is.null(ld)) { return(NULL) } - keptIds <- attr(R, "keptVariantIds") + keptIds <- attr(ld, "keptVariantIds") kept <- filter(df, is_in(variantIds, keptIds)) ss <- .cbSumstatFrame(kept, keptIds, nCase, nControl, varY) list( sumstat = ss, - LD = `attr<-`(R, "keptVariantIds", NULL), - variantIds = keptIds + LD = `attr<-`(ld, "keptVariantIds", NULL), + variantIds = keptIds, + isFactor = .ldIsGenotypeRef(ldReference) ) } +# The LD of the `wanted` variants the panel holds, the rest dropped (the +# colocboost path expects to operate only on the overlap): a genotype +# reference's factor, which colocboost takes as X_ref and correlates itself, +# or an LdMatrix's matrix. +# @noRd +.cbPairLd <- function(ldReference, wanted) { + ldFrom <- if (.ldIsGenotypeRef(ldReference)) { + .ldFactorFromRef + } else { + .ldFromRef + } + ldFrom(ldReference, wanted, label = ".cbSumstatPair", onMissing = "drop") +} + # The colocboost sumstat frame for one entry, over the variants the LD panel # kept. `var_y` is added only when supplied: colocboost treats its absence as # "z-scale", so writing NA would change the model rather than say nothing. @@ -734,7 +741,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { if (is.null(ss) || nrow(ss) == 0L) { return(list()) } - ldSketch <- ldSketch(ss) + ldReference <- ldReference(ss) byContext <- if (!is.null(contexts) && length(contexts) > 0L) { is_in(as.character(ss$context), contexts) } else { @@ -756,7 +763,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { rows, .cbQtlEntryPair, ss = ss, - ldSketch = ldSketch, + ldReference = ldReference, cutoffs = cutoffs ), map_chr(rows, .cbQtlEntryLabel, ss = ss) @@ -775,7 +782,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # One QTL entry's (sumstat, LD) pair, or NULL when it has no panel overlap. # @noRd -.cbQtlEntryPair <- function(i, ss, ldSketch, cutoffs) { +.cbQtlEntryPair <- function(i, ss, ldReference, cutoffs) { .cbSumstatPair( df = as.data.frame( ss, @@ -784,7 +791,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { trait = as.character(ss$trait)[[i]], require = "Z" ), - ldSketch = ldSketch, + ldReference = ldReference, varY = .cbTupleValue(ss, "varY", i), cutoffs = cutoffs ) @@ -807,13 +814,13 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { if (is.null(gws) || nrow(gws) == 0L) { return(list()) } - ldSketch <- ldSketch(gws) + ldReference <- ldReference(gws) compact(set_names( map( seq_len(nrow(gws)), .cbGwasEntryPair, gws = gws, - ldSketch = ldSketch, + ldReference = ldReference, cutoffs = cutoffs ), as.character(gws$study) @@ -822,14 +829,14 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # One GWAS entry's (sumstat, LD) pair, or NULL when it has no panel overlap. # @noRd -.cbGwasEntryPair <- function(i, gws, ldSketch, cutoffs) { +.cbGwasEntryPair <- function(i, gws, ldReference, cutoffs) { .cbSumstatPair( df = as.data.frame( gws, studyName = as.character(gws$study)[[i]], require = "Z" ), - ldSketch = ldSketch, + ldReference = ldReference, varY = .cbTupleValue(gws, "varY", i), nCase = .cbTupleValue(gws, "nCase", i), nControl = .cbTupleValue(gws, "nControl", i), @@ -838,12 +845,12 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { } # Check two LD sketches for the reference-panel compatibility contract. Thin -# wrapper over the shared `.requireMatchingLdSketches` helper (R/ld.R) +# wrapper over the shared `.requireMatchingLdRefs` helper (R/ld.R) # using the "lenient" null policy: a NULL on either side skips the check # (only colocboostPipeline allows that, since some bundles only have a # QTL side or only a GWAS side). -.cbRequireMatchingLdSketches <- function(qtlLd, gwasLd) { - .requireMatchingLdSketches( +.cbRequireMatchingLdRefs <- function(qtlLd, gwasLd) { + .requireMatchingLdRefs( qtlLd, gwasLd, pipelineName = "colocboostPipeline", @@ -853,15 +860,26 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # Combine sumstat bundles into the (sumstat-list, LD-list, dict) shape # colocboost expects. Deduplicates identical LD matrices so dict_sumstatLD -# can point multiple sumstats at one LD. +# can point multiple sumstats at one LD, and records in `ldArg` whether the +# list holds LD matrices ("LD") or factors of them ("X_ref"). Every side of +# a run reads one LD reference (.cbRequireMatchingLdRefs()), so the bundles +# never mix the two. .cbMergeSumstatBundles <- function(bundles) { if (length(bundles) == 0L) { return(list( sumstat = list(), LD = list(), - dict_sumstatLD = matrix(integer(0), ncol = 2L) + dict_sumstatLD = matrix(integer(0), ncol = 2L), + ldArg = "LD" )) } + kinds <- unique(map_lgl(bundles, .cbPairIsFactor)) + if (length(kinds) > 1L) { + abort( + "colocboost inputs mix LD matrices and genotype factors.", + .internal = TRUE + ) + } # Same shape as .cbDedupX: every bundle resolves to the first bundle # carrying an identical LD matrix, so the unique set and the back-mapping # both fall out without carrying state. @@ -874,7 +892,8 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { dict_sumstatLD = cbind( sumstat = seq_along(bundles), LD = match(firstIdx, keepIdx) - ) + ), + ldArg = if (kinds) "X_ref" else "LD" ) } @@ -903,10 +922,6 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { ) } -# A requested analysis with nothing to run on would otherwise be a silent -# no-op: the caller gets an empty ColocBoostResult whose computingTime() -# entries are all NULL, with no indication of why. Say so. -# @noRd # TRUE when an analysis was requested and has the data to run. When it was # requested but cannot run, warn and answer FALSE, so the caller's compact() # drops it instead of skipping it silently. @@ -922,6 +937,10 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { FALSE } +# A requested analysis with nothing to run on would otherwise be a silent +# no-op: the caller gets an empty ColocBoostResult whose computingTime() +# entries are all NULL, with no indication of why. Say so. +# @noRd .cbWarnNoData <- function(flag, needed) { msg <- glue( "colocboostPipeline: {flag} = TRUE was requested, but there is no ", @@ -933,48 +952,25 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { } # Shared dispatch: accepts a fully-prepared individual bundle (possibly -# NULL) plus a sumstat bundle (possibly empty) and runs the three -# colocboost variants the user requested. `qtlSumstatBundle` is the QTL-side -# subset of `sumstatBundle` (see .cbDriver); only the xQTL-only run uses it, -# so that a GWAS study never becomes an xQTL-only outcome. -# The analyses that were requested AND have data to run on, keyed by the -# result slot each fills. .cbCanRun warns in the "requested but cannot" case -# so the skip is never silent, and compact() drops what did not run. +# NULL) plus a sumstat bundle (possibly empty) and runs the colocboost +# variants `run` (from .cbResolveRuns) switched on. `qtlSumstatBundle` is the +# QTL-side subset of `sumstatBundle` (see .cbDriver); only the xQTL-only run +# uses it, so that a GWAS study never becomes an xQTL-only outcome. The +# result is keyed by the result slot each run fills; compact() drops what did +# not run. # @noRd .cbDispatchRuns <- function( individualBundle, sumstatBundle, qtlSumstatBundle, - xqtlColoc, - jointGwas, - separateGwas, + run, hasInd, - hasSs, - hasQtlSs, focalTrait, methodArgs, - gwasRunBundles = list(), - hasGwasSs = FALSE + gwasRunBundles = list() ) { - # Resolved first, in the order the runs are listed: .cbCanRun() warns - # when a requested analysis has no data for it, and hoisting keeps those - # warnings in the same order they were emitted inline. - ssNeeded <- "summary-statistic data" - runXqtl <- .cbCanRun( - xqtlColoc, - hasInd || hasQtlSs, - "xqtlColoc", - "QTL data" - ) - runJoint <- .cbCanRun(jointGwas, hasSs, "jointGwas", ssNeeded) - runSeparate <- .cbCanRun( - separateGwas, - hasGwasSs, - "separateGwas", - "GWAS summary statistics" - ) compact(list( - xqtl_coloc = if (runXqtl) { + xqtl_coloc = if (run$xqtl) { .cbRunXqtlOnly( individualBundle, qtlSumstatBundle, @@ -983,7 +979,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { methodArgs ) }, - joint_gwas = if (runJoint) { + joint_gwas = if (run$joint) { .cbRunJointGwas( individualBundle, sumstatBundle, @@ -992,7 +988,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { gwasNames = names(gwasRunBundles) ) }, - separate_gwas = if (runSeparate) { + separate_gwas = if (run$separate) { .cbRunSeparateGwas( individualBundle, gwasRunBundles, @@ -1003,6 +999,45 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { )) } +# Which analyses were requested AND have data to run on. .cbCanRun warns in +# the "requested but cannot" case so the skip is never silent; the flags are +# resolved in the order the runs are listed, so those warnings keep that +# order. separateGwas needs a GWAS, not merely "some summary statistics": a +# QTL-only sumstat input used to satisfy a `hasSs` guard and then run one +# focal pass per QTL study. +# @noRd +.cbResolveRuns <- function( + xqtlColoc, + jointGwas, + separateGwas, + hasInd, + hasSs, + qtlSumstatBundle, + gwasRunBundles +) { + hasQtlSs <- length(qtlSumstatBundle$sumstat) > 0L + list( + xqtl = .cbCanRun( + xqtlColoc, + hasInd || hasQtlSs, + "xqtlColoc", + "QTL data" + ), + joint = .cbCanRun( + jointGwas, + hasSs, + "jointGwas", + "summary-statistic data" + ), + separate = .cbCanRun( + separateGwas, + length(gwasRunBundles) > 0L, + "separateGwas", + "GWAS summary statistics" + ) + ) +} + # Each run's result and timing folded into the empty result skeleton, so a # slot no analysis filled keeps the skeleton's own (empty) value. # @noRd @@ -1044,7 +1079,9 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { Y[, keep, drop = FALSE] } - +# Run the requested colocboost analyses over the prepared bundles and fold +# them into a ColocBoostResult (an empty one when no QTL input remains). +# @noRd .cbRunVariants <- function( individualBundle, sumstatBundle, @@ -1053,19 +1090,13 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { separateGwas, focalTrait, methodArgs, - qtlLdSketch = NULL, + qtlLdReference = NULL, qtlSumstatBundle = NULL, gwasRunBundles = list() ) { - empty <- .cbEmptyResult() hasInd <- !is.null(individualBundle) hasSs <- length(sumstatBundle$sumstat) > 0L qtlSumstatBundle <- qtlSumstatBundle %||% .cbMergeSumstatBundles(list()) - hasQtlSs <- length(qtlSumstatBundle$sumstat) > 0L - # separateGwas needs a GWAS, not merely "some summary statistics": a - # QTL-only sumstat input used to satisfy the old `hasSs` guard and then - # run one focal pass per QTL study. - hasGwasSs <- length(gwasRunBundles) > 0L if (!hasInd && !hasSs) { msg <- glue( "colocboostPipeline: no QTL inputs remain after selection. ", @@ -1074,26 +1105,30 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { inform(msg) return(.cbEmptyResultObject()) } - runs <- .cbDispatchRuns( - individualBundle, - sumstatBundle, - qtlSumstatBundle, + run <- .cbResolveRuns( xqtlColoc, jointGwas, separateGwas, hasInd, hasSs, - hasQtlSs, + qtlSumstatBundle, + gwasRunBundles + ) + runs <- .cbDispatchRuns( + individualBundle, + sumstatBundle, + qtlSumstatBundle, + run, + hasInd, focalTrait, methodArgs, - gwasRunBundles = gwasRunBundles, - hasGwasSs = hasGwasSs + gwasRunBundles = gwasRunBundles ) - results <- .cbMergeRuns(empty, runs) + results <- .cbMergeRuns(.cbEmptyResult(), runs) .cbToResultObject( results, .cbOutcomeInfo(individualBundle, sumstatBundle, hasInd), - qtlLdSketch + qtlLdReference ) } @@ -1110,14 +1145,6 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { ) } -# Flatten the three raw colocboost runs into one ColocBoostResult. -# -# The three analyses are NOT parallel structures: xqtl_coloc and joint_gwas are -# each a single colocboost object, while separate_gwas is a NAMED LIST of them, -# one per GWAS study -- one level deeper. That is why `gwasStudy` exists as a -# key: without it the separate-GWAS sets would be indistinguishable from each -# other once flattened. -# @noRd # @noRd .cbJointRun <- function(nm, results) { if (is.null(results[[nm]])) { @@ -1143,7 +1170,15 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { map_chr(entries, field) } -.cbToResultObject <- function(results, outcomeInfo, ldSketch = NULL) { +# Flatten the three raw colocboost runs into one ColocBoostResult. +# +# The three analyses are NOT parallel structures: xqtl_coloc and joint_gwas are +# each a single colocboost object, while separate_gwas is a NAMED LIST of them, +# one per GWAS study -- one level deeper. That is why `gwasStudy` exists as a +# key: without it the separate-GWAS sets would be indistinguishable from each +# other once flattened. +# @noRd +.cbToResultObject <- function(results, outcomeInfo, ldReference = NULL) { sep <- results$separate_gwas sepRuns <- if (is.null(sep) || length(sep) == 0L) { list() @@ -1170,7 +1205,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { analysis = .cbEntryField(entries, "analysis"), gwasStudy = .cbEntryField(entries, "gwasStudy"), outcomeInfo = outcomeInfo, - ldSketch = ldSketch, + ldReference = ldReference, computingTime = results$computing_time %||% list() ) } @@ -1219,7 +1254,11 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { nCtx <- if (hasInd) length(individualBundle$Y) else 0L nSs <- length(sumstatBundle$sumstat) .cbAnnounceXqtl(nCtx, nSs) - ldArgs <- if (nSs > 0L) .cbBuildLdArgs(sumstatBundle$LD) else list() + ldArgs <- if (nSs > 0L) { + .cbBuildLdArgs(sumstatBundle$LD, sumstatBundle$ldArg) + } else { + list() + } args <- c( list( X = if (hasInd) individualBundle$X else NULL, @@ -1298,7 +1337,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { if (hasInd) individualBundle$outcomeNames else character(), ssNames ) - ldArgs <- .cbBuildLdArgs(sumstatBundle$LD) + ldArgs <- .cbBuildLdArgs(sumstatBundle$LD, sumstatBundle$ldArg) # The merged bundle holds QTL sumstats as well as GWAS, so split the # labels rather than calling every sumstat a GWAS: a context is any # outcome that is not one of the GWAS studies, whether it arrived as @@ -1381,7 +1420,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # `runBundle` carries every QTL sumstat followed by this one GWAS, so the # whole LD list and its dict come along -- the same shape as the joint # run, but focal on the last outcome. - ldArgs <- .cbBuildLdArgs(runBundle$LD) + ldArgs <- .cbBuildLdArgs(runBundle$LD, runBundle$ldArg) traits <- c( if (hasInd) individualBundle$outcomeNames else character(), names(runBundle$sumstat) @@ -1443,9 +1482,10 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # Relabel a (sumstat, LD) pair to the canonical coding: flip the z-score and the # LD sign-submatrix for variants swapped relative to canonical -# (LD_ij -> sign_i * sign_j * LD_ij), drop unmatched variants, and relabel to -# canonical. The sumstat and its LD share one sign vector, so they stay -# consistent. Returns NULL when no variant matches. +# (LD_ij -> sign_i * sign_j * LD_ij; on a factor, the swapped columns are +# negated), drop unmatched variants, and relabel to canonical. The sumstat +# and its LD share one sign vector, so they stay consistent. Returns NULL +# when no variant matches. # @noRd .cbFlipPairToCanonical <- function(p, canonical) { mm <- matchVariants(p$sumstat$variant, canonical) @@ -1461,11 +1501,31 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { z = .data$z * sgn, variant = canonical[ib] ) - ld <- `dimnames<-`( - p$LD[ia, ia, drop = FALSE] * outer(sgn, sgn), - list(canonical[ib], canonical[ib]) + isFactor <- .cbPairIsFactor(p) + ld <- if (isFactor) { + `colnames<-`( + sweep(p$LD[, ia, drop = FALSE], 2L, sgn, FUN = "*"), + canonical[ib] + ) + } else { + `dimnames<-`( + p$LD[ia, ia, drop = FALSE] * outer(sgn, sgn), + list(canonical[ib], canonical[ib]) + ) + } + list( + sumstat = ss, + LD = ld, + variantIds = canonical[ib], + isFactor = isFactor ) - list(sumstat = ss, LD = ld, variantIds = canonical[ib]) +} + +# TRUE when a (sumstat, LD) pair carries a factor of its LD rather than the +# matrix. +# @noRd +.cbPairIsFactor <- function(p) { + isTRUE(p$isFactor) } # Harmonize the allele coding of every colocboost input (individual X columns, @@ -1528,6 +1588,12 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { # are per-tuple lists of `list(sumstat, LD)` produced by the per-class # bundle helpers; they are merged here so dict_sumstatLD can dedupe # identical LD matrices across QTL and GWAS sides. +# +# The xQTL-only run gets its own bundle over just the QTL-side pairs, so a +# GWAS study is never treated as an xQTL outcome. Rebuilding it through +# .cbMergeSumstatBundles (rather than subsetting the merged one) keeps the +# deduplicated LD list and dict_sumstatLD consistent for the subset. +# @noRd .cbDriver <- function( individualBundle, qtlPairs, @@ -1537,7 +1603,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { separateGwas, focalTrait, methodArgs, - qtlLdSketch = NULL, + qtlLdReference = NULL, alleleFlip = TRUE, cutoffs = NULL ) { @@ -1548,7 +1614,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { combinedPairs <- .cbAppendGwasPairs( qtlPairs, gwasSumStats, - qtlLdSketch, + qtlLdReference, cutoffs = cutoffs ) harmonized <- .cbMaybeHarmonize( @@ -1557,29 +1623,9 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { alleleFlip ) sumstatBundle <- .cbMergeSumstatBundles(harmonized$pairs) - # The xQTL-only run gets its own bundle over just the QTL-side pairs, so - # a GWAS study is never treated as an xQTL outcome. Rebuilding it through - # .cbMergeSumstatBundles (rather than subsetting the merged one) keeps the - # deduplicated LD list and dict_sumstatLD consistent for the subset. qtlKeys <- intersect(names(harmonized$pairs), names(qtlPairs)) qtlSumstatBundle <- .cbMergeSumstatBundles(harmonized$pairs[qtlKeys]) - # The separate-GWAS runs are FOCAL: each is one GWAS tested against the - # whole QTL side, so each needs its own bundle of (every QTL pair + that - # one GWAS). Built the same way and for the same reason as the xQTL - # bundle above -- the deduplicated LD list and dict_sumstatLD have to - # match the subset, which subsetting the merged bundle would not give. - # Without this the separate loop ran over EVERY merged label and treated - # each QTL study as a focal GWAS in its own right. - gwasKeys <- setdiff(names(harmonized$pairs), qtlKeys) - gwasRunBundles <- set_names( - map( - gwasKeys, - .cbGwasRunBundle, - pairs = harmonized$pairs, - qtlKeys = qtlKeys - ), - gwasKeys - ) + gwasRunBundles <- .cbGwasRunBundles(harmonized$pairs, qtlKeys) .cbRunVariants( harmonized$individualBundle, sumstatBundle, @@ -1588,12 +1634,28 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { separateGwas, focalTrait, methodArgs, - qtlLdSketch = qtlLdSketch, + qtlLdReference = qtlLdReference, qtlSumstatBundle = qtlSumstatBundle, gwasRunBundles = gwasRunBundles ) } +# The separate-GWAS run bundles, keyed by GWAS label. The runs are FOCAL: each +# is one GWAS tested against the whole QTL side, so each needs its own bundle +# of (every QTL pair + that one GWAS). Built through .cbMergeSumstatBundles +# for the same reason as the xQTL bundle in .cbDriver -- the deduplicated LD +# list and dict_sumstatLD have to match the subset, which subsetting the +# merged bundle would not give. Without this the separate loop ran over EVERY +# merged label and treated each QTL study as a focal GWAS in its own right. +# @noRd +.cbGwasRunBundles <- function(pairs, qtlKeys) { + gwasKeys <- setdiff(names(pairs), qtlKeys) + set_names( + map(gwasKeys, .cbGwasRunBundle, pairs = pairs, qtlKeys = qtlKeys), + gwasKeys + ) +} + # Append the GWAS sumstat pairs to the QTL pairs, QC-gating gwasSumStats and # make.unique-ing colliding labels. (Sequential key resolution -- kept as a # loop.) @@ -1601,15 +1663,15 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { .cbAppendGwasPairs <- function( qtlPairs, gwasSumStats, - qtlLdSketch, + qtlLdReference, cutoffs = NULL ) { if (is.null(gwasSumStats)) { return(qtlPairs) } .cbRequireSumStatsQc(gwasSumStats, "gwasSumStats") - if (!is.null(qtlLdSketch)) { - .cbRequireMatchingLdSketches(qtlLdSketch, ldSketch(gwasSumStats)) + if (!is.null(qtlLdReference)) { + .cbRequireMatchingLdRefs(qtlLdReference, ldReference(gwasSumStats)) } gwasPairs <- .cbGwasSumStatsBundle(gwasSumStats, cutoffs = cutoffs) if (length(gwasPairs) == 0L) { @@ -1647,12 +1709,12 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { separateGwas, samples, panelFilterParam, - signalScreenArgs, + signalScreenParam, alleleFlip, methodArgs, - residualizationArgs + residualizationParam ) { - screenSpec <- .screenResolve(signalScreenArgs) + screenSpec <- .screenResolve(signalScreenParam) indBundle <- .cbIndividualBundle( qtlData, contexts = contexts, @@ -1661,7 +1723,7 @@ setGeneric("colocboostPipeline", function(qtlData, gwasSumStats = NULL, ...) { cisWindow = cisWindow, samples = samples, pipCutoffToSkip = screenSpec, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) .cbDriver( indBundle, @@ -1698,9 +1760,9 @@ setMethod( separateGwas = FALSE, samples = NULL, panelFilterParam = PanelFilterParam(), - signalScreenArgs = SignalScreenParam(), + signalScreenParam = SignalScreenParam(), alleleFlip = TRUE, - residualizationArgs = ResidualizationParam(), + residualizationParam = ResidualizationParam(), # `list()`, not ColocboostOptions(): the formal shadows the # constructor, so a constructor-call default is a recursive # reference. An empty list is the "no options" spelling @@ -1722,10 +1784,10 @@ setMethod( separateGwas = separateGwas, samples = samples, panelFilterParam = panelFilterParam, - signalScreenArgs = signalScreenArgs, + signalScreenParam = signalScreenParam, alleleFlip = alleleFlip, methodArgs = methodArgs, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) } ) @@ -1767,7 +1829,7 @@ setMethod( separateGwas = separateGwas, focalTrait = focalTrait, methodArgs = methodArgs, - qtlLdSketch = ldSketch(qtlData), + qtlLdReference = ldReference(qtlData), alleleFlip = alleleFlip, cutoffs = cutoffs ) @@ -1792,9 +1854,9 @@ setMethod( separateGwas = FALSE, samples = NULL, panelFilterParam = PanelFilterParam(), - signalScreenArgs = SignalScreenParam(), + signalScreenParam = SignalScreenParam(), alleleFlip = TRUE, - residualizationArgs = ResidualizationParam(), + residualizationParam = ResidualizationParam(), # `list()`, not ColocboostOptions(): the formal shadows the # constructor, so a constructor-call default is a recursive # reference. An empty list is the "no options" spelling @@ -1816,10 +1878,10 @@ setMethod( separateGwas = separateGwas, samples = samples, panelFilterParam = panelFilterParam, - signalScreenArgs = signalScreenArgs, + signalScreenParam = signalScreenParam, alleleFlip = alleleFlip, methodArgs = methodArgs, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) } ) @@ -1840,12 +1902,12 @@ setMethod( separateGwas, samples, panelFilterParam, - signalScreenArgs, + signalScreenParam, alleleFlip, methodArgs, - residualizationArgs + residualizationParam ) { - screenSpec <- .screenResolve(signalScreenArgs) + screenSpec <- .screenResolve(signalScreenParam) indBundle <- .cbMultiStudyIndBundle( qtlData = qtlData, contexts = contexts, @@ -1854,7 +1916,7 @@ setMethod( cisWindow = cisWindow, samples = samples, screenSpec = screenSpec, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) cutoffs <- .panelCutoffs(panelFilterParam) ss <- .cbMultiStudySumstats( @@ -1872,17 +1934,12 @@ setMethod( separateGwas, focalTrait, methodArgs, - qtlLdSketch = ss$qtlLdSketch, + qtlLdReference = ss$qtlLdReference, alleleFlip = alleleFlip, cutoffs = cutoffs ) } -# Aggregate the individual-level bundles across all QtlDataset members. Per- -# study trait names are prefixed with "{study}:" so colocboost sees distinct -# outcomes when two studies share a trait. Returns the combined bundle or NULL. -# (Sequential offset-shifted merge -- kept as a loop.) -# @noRd # One study's bundle with its names study-prefixed, or NULL when it has # nothing to contribute. # @noRd @@ -1895,7 +1952,7 @@ setMethod( cisWindow, samples, screenSpec, - residualizationArgs = ResidualizationParam() + residualizationParam = ResidualizationParam() ) { sub <- .cbIndividualBundle( qtlDatasets[[studyName]], @@ -1905,7 +1962,7 @@ setMethod( cisWindow = cisWindow, samples = samples, pipCutoffToSkip = screenSpec, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) if (is.null(sub)) { return(NULL) @@ -1931,6 +1988,12 @@ setMethod( cbind(Y = d[, "Y"] + yOffsets[[i]], X = d[, "X"] + xOffsets[[i]]) } +# The individual-level bundle across every study of a MultiStudyQtlDataset. +# Each study's dict indexes its own X and Y, so it shifts by however many +# entries the studies before it contributed -- a cumulative count, known once +# all the bundles are in hand rather than tracked while looping. +# NULL when no study has anything to contribute. +# @noRd .cbMultiStudyIndBundle <- function( qtlData, contexts, @@ -1939,7 +2002,7 @@ setMethod( cisWindow, samples, screenSpec, - residualizationArgs = ResidualizationParam() + residualizationParam = ResidualizationParam() ) { qtlDatasets <- qtlDatasets(qtlData) subs <- compact(map( @@ -1952,14 +2015,11 @@ setMethod( cisWindow = cisWindow, samples = samples, screenSpec = screenSpec, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam )) if (length(subs) == 0L) { return(NULL) } - # Each study's dict indexes its own X and Y, so it shifts by however many - # entries the studies before it contributed -- a cumulative count, known - # once all the bundles are in hand rather than tracked while looping. xOffsets <- .cbOffsets(map_int(subs, .cbFieldLength, field = "X")) yOffsets <- .cbOffsets(map_int(subs, .cbFieldLength, field = "Y")) combinedX <- .cbConcat(map(subs, "X")) @@ -1985,7 +2045,8 @@ setMethod( } # Prefix a study's X names + outcome names with "{study}:" (Y names track the -# outcome names). +# outcome names), so colocboost sees distinct outcomes when two studies share +# a trait. # @noRd .cbPrefixStudyNames <- function(sub, studyName) { outcomeNames <- str_c(studyName, sub$outcomeNames, sep = ":") @@ -2014,7 +2075,7 @@ setMethod( } # Sumstat side of a MultiStudyQtlDataset: bundle any embedded QtlSumStats. -# Returns list(qtlPairs, qtlLdSketch). +# Returns list(qtlPairs, qtlLdReference). # @noRd .cbMultiStudySumstats <- function( qtlData, @@ -2024,7 +2085,7 @@ setMethod( ) { embeddedSs <- sumStats(qtlData) if (is.null(embeddedSs)) { - return(list(qtlPairs = list(), qtlLdSketch = NULL)) + return(list(qtlPairs = list(), qtlLdReference = NULL)) } .cbRequireSumStatsQc(embeddedSs, "MultiStudyQtlDataset@sumStats") list( @@ -2034,18 +2095,12 @@ setMethod( traitId = traitId, cutoffs = cutoffs ), - qtlLdSketch = ldSketch(embeddedSs) + qtlLdReference = ldReference(embeddedSs) ) } # ---- map/apply helpers (lambda-free callbacks) --------------------------- -# TRUE when a matrix is non-square (a samples x variants genotype reference). -# @noRd -.cbIsNonSquare <- function(m) { - nrow(m) != ncol(m) -} - # Single-effect screen decision for outcome column `j` (drop on failure). # @noRd .cbScreenOutcome <- function(j, X, Y, spec) { diff --git a/R/cpp11.R b/R/cpp11.R index 725ad5d98..141781dea 100644 --- a/R/cpp11.R +++ b/R/cpp11.R @@ -1,25 +1,61 @@ # Generated by cpp11: do not edit by hand +bgenDosageCpp <- function(data, nSamples, alleles) { + .Call(`_pecotmr_bgenDosageCpp`, data, nSamples, alleles) +} + +bgenSampleIdsCpp <- function(ids, nSamples) { + .Call(`_pecotmr_bgenSampleIdsCpp`, ids, nSamples) +} + dentistIterativeImpute <- function(ldMatR, nSample, zScoreR, pValueThreshold, propSVD, gcControl, nIter, gPvalueThreshold, numThreads, correctChenEtAlBug, verbose, seed) { .Call(`_pecotmr_dentistIterativeImpute`, ldMatR, nSample, zScoreR, pValueThreshold, propSVD, gcControl, nIter, gPvalueThreshold, numThreads, correctChenEtAlBug, verbose, seed) } -lassosumRssRcpp <- function(zR, LD, lambdaR, thr, maxiter) { - .Call(`_pecotmr_lassosumRssRcpp`, zR, LD, lambdaR, thr, maxiter) +dentistIterativeImputeFactor <- function(factorR, nSample, zScoreR, pValueThreshold, propSVD, gcControl, nIter, gPvalueThreshold, numThreads, correctChenEtAlBug, verbose, seed) { + .Call(`_pecotmr_dentistIterativeImputeFactor`, factorR, nSample, zScoreR, pValueThreshold, propSVD, gcControl, nIter, gPvalueThreshold, numThreads, correctChenEtAlBug, verbose, seed) +} + +lassosumRssCpp <- function(zR, LD, shrink, lambdaR, thr, maxiter) { + .Call(`_pecotmr_lassosumRssCpp`, zR, LD, shrink, lambdaR, thr, maxiter) +} + +lassosumRssFactorCpp <- function(zR, factorR, shrink, lambdaR, thr, maxiter) { + .Call(`_pecotmr_lassosumRssFactorCpp`, zR, factorR, shrink, lambdaR, thr, maxiter) +} + +ldThresholdComponents <- function(standardized, threshold, stripe) { + .Call(`_pecotmr_ldThresholdComponents`, standardized, threshold, stripe) +} + +penalizedRssCpp <- function(zR, LD, shrink, lambdaR, penaltyStr, gamma, alpha, lambda0, lambda2, thr, maxiter, maxSwaps) { + .Call(`_pecotmr_penalizedRssCpp`, zR, LD, shrink, lambdaR, penaltyStr, gamma, alpha, lambda0, lambda2, thr, maxiter, maxSwaps) +} + +penalizedRssFactorCpp <- function(zR, factorR, shrink, lambdaR, penaltyStr, gamma, alpha, lambda0, lambda2, thr, maxiter, maxSwaps) { + .Call(`_pecotmr_penalizedRssFactorCpp`, zR, factorR, shrink, lambdaR, penaltyStr, gamma, alpha, lambda0, lambda2, thr, maxiter, maxSwaps) +} + +prsCsCpp <- function(a, b, phi, bhat, maf, n, ldBlk, nIter, nBurnin, thin, verbose, seed) { + .Call(`_pecotmr_prsCsCpp`, a, b, phi, bhat, maf, n, ldBlk, nIter, nBurnin, thin, verbose, seed) +} + +prsCsFactorCpp <- function(a, b, phi, bhat, maf, n, factorR, nIter, nBurnin, thin, verbose, seed) { + .Call(`_pecotmr_prsCsFactorCpp`, a, b, phi, bhat, maf, n, factorR, nIter, nBurnin, thin, verbose, seed) } -penalizedRssRcpp <- function(zR, LD, lambdaR, penaltyStr, gamma, alpha, lambda0, lambda2, thr, maxiter, maxSwaps) { - .Call(`_pecotmr_penalizedRssRcpp`, zR, LD, lambdaR, penaltyStr, gamma, alpha, lambda0, lambda2, thr, maxiter, maxSwaps) +qtlEnrichmentCpp <- function(rGwasPip, rQtlSusieFit, piGwas, piQtl, ImpN, shrinkageLambda, doubleShrinkage, besselCorrection, numThreads, seed) { + .Call(`_pecotmr_qtlEnrichmentCpp`, rGwasPip, rQtlSusieFit, piGwas, piQtl, ImpN, shrinkageLambda, doubleShrinkage, besselCorrection, numThreads, seed) } -prsCsRcpp <- function(a, b, phi, bhat, maf, n, ldBlk, nIter, nBurnin, thin, verbose, seed) { - .Call(`_pecotmr_prsCsRcpp`, a, b, phi, bhat, maf, n, ldBlk, nIter, nBurnin, thin, verbose, seed) +sdprCpp <- function(bhatR, LD, n, perVariantSampleSize, array, a, c, M, a0k, b0k, iter, burn, thin, numThreads, optLlk, verbose, seed) { + .Call(`_pecotmr_sdprCpp`, bhatR, LD, n, perVariantSampleSize, array, a, c, M, a0k, b0k, iter, burn, thin, numThreads, optLlk, verbose, seed) } -qtlEnrichmentRcpp <- function(rGwasPip, rQtlSusieFit, piGwas, piQtl, ImpN, shrinkageLambda, doubleShrinkage, besselCorrection, numThreads, seed) { - .Call(`_pecotmr_qtlEnrichmentRcpp`, rGwasPip, rQtlSusieFit, piGwas, piQtl, ImpN, shrinkageLambda, doubleShrinkage, besselCorrection, numThreads, seed) +sdprFactorCpp <- function(bhatR, factorR, n, perVariantSampleSize, array, a, c, M, a0k, b0k, iter, burn, thin, numThreads, optLlk, verbose, seed) { + .Call(`_pecotmr_sdprFactorCpp`, bhatR, factorR, n, perVariantSampleSize, array, a, c, M, a0k, b0k, iter, burn, thin, numThreads, optLlk, verbose, seed) } -sdprRcpp <- function(bhatR, LD, n, perVariantSampleSize, array, a, c, M, a0k, b0k, iter, burn, thin, numThreads, optLlk, verbose, seed) { - .Call(`_pecotmr_sdprRcpp`, bhatR, LD, n, perVariantSampleSize, array, a, c, M, a0k, b0k, iter, burn, thin, numThreads, optLlk, verbose, seed) +vcfGtDosageCpp <- function(gt, alleles) { + .Call(`_pecotmr_vcfGtDosageCpp`, gt, alleles) } diff --git a/R/crossValidation.R b/R/crossValidation.R index 90b0384a6..b3d5f356f 100644 --- a/R/crossValidation.R +++ b/R/crossValidation.R @@ -347,17 +347,15 @@ NULL samplePartitions } -# @noRd # multicoreWorkers() rather than bpworkers(MulticoreParam()): the two answer # the same number, but constructing a MulticoreParam costs ~0.6s (almost all # of it garbage collection) and this runs on every fit. +# @noRd .cvNumCores <- function(numThreads) { avail <- multicoreWorkers() min(if (numThreads == -1) avail else numThreads, avail) } -# Run each fold (parallel via BiocParallel when >= 2 cores). -# @noRd # MulticoreParam for CV / weight-fitting parallelism. A NULL seed keeps the # historical default RNGseed = 1L (reproducible); a caller-supplied seed # overrides it so the parallel L'Ecuyer streams are reproducible under it. This @@ -383,6 +381,8 @@ NULL } } +# Run each fold (parallel via BiocParallel when >= 2 cores). +# @noRd .cvRunFolds <- function(foldIds, cvState, numCores) { if (numCores >= 2) { return(bplapply( @@ -528,19 +528,43 @@ setClass( numThreads = "numeric", samplePartition = "list_OR_NULL", maxVariants = "numeric_OR_NULL", - weightMethods = "list_OR_NULL" + weightMethods = "list_OR_NULL", + fitFullData = "logical" ) ) +setValidity("CrossValidationParam", function(object) { + problems <- .cvFitFullDataProblem(object@fitFullData, object@folds) + if (length(problems) == 0L) TRUE else problems +}) + +# `fitFullData = FALSE` asks for cross-validation results alone, which is only +# a request for something when cross-validation runs: without folds there +# would be no fit of any kind. Checked here, at construction, rather than +# separately by each pipeline that reads it. +# @noRd +.cvFitFullDataProblem <- function(fitFullData, folds) { + if (length(fitFullData) != 1L || is.na(fitFullData)) { + return("`fitFullData` must be a single TRUE or FALSE.") + } + if (!fitFullData && !.cvEnabled(list(folds = folds))) { + return(str_c( + "fitFullData = FALSE requires cross-validation (folds >= 2): ", + "without folds there would be no fit at all." + )) + } + NULL +} + #' @title Cross-Validation Settings #' @description How cross-validation runs, shared by #' \code{\link{fineMappingPipeline}} and \code{\link{twasWeightsPipeline}}. #' -#' \code{folds}, \code{numThreads} and \code{samplePartition} mean the same -#' thing in both. \code{maxVariants} and \code{weightMethods} are honoured -#' by \code{\link{twasWeightsPipeline}} only and are \strong{ignored} by -#' \code{\link{fineMappingPipeline}}, so one bundle can be handed to -#' either pipeline unchanged. +#' \code{folds}, \code{numThreads}, \code{samplePartition} and +#' \code{fitFullData} mean the same thing in both. \code{maxVariants} and +#' \code{weightMethods} are honoured by \code{\link{twasWeightsPipeline}} +#' only and are \strong{ignored} by \code{\link{fineMappingPipeline}}, so +#' one bundle can be handed to either pipeline unchanged. #' @param folds Integer. Number of folds; \code{0} (the default) or #' \code{1} skips cross-validation. The same value in both pipelines --- #' \code{twasWeightsPipeline} used to default to \code{5}, which made an @@ -561,16 +585,24 @@ setClass( #' \strong{\code{twasWeightsPipeline} only} --- #' \code{fineMappingPipeline} ignores it, because its \code{methods=} are #' fine-mapping methods and its CV refits all of them. +#' @param fitFullData Logical. \code{TRUE} (default) fits the final model on +#' the full data as well as cross-validating it; \code{FALSE} keeps only +#' the cross-validation results, and requires \code{folds >= 2}. A row +#' from such a run carries its \code{cvResult} and nothing else, so it +#' cannot be exported and cannot supply full-data weights or fits +#' downstream. #' @return A \code{CrossValidationParam} object, a \code{\link{MethodParam}}. #' @examples #' CrossValidationParam(folds = 10, numThreads = 4) +#' CrossValidationParam(folds = 5, fitFullData = FALSE) #' @export CrossValidationParam <- function( folds = 0, numThreads = 1, samplePartition = NULL, maxVariants = NULL, - weightMethods = NULL + weightMethods = NULL, + fitFullData = TRUE ) { new( "CrossValidationParam", @@ -578,22 +610,24 @@ CrossValidationParam <- function( numThreads = numThreads, samplePartition = samplePartition, maxVariants = maxVariants, - weightMethods = weightMethods + weightMethods = weightMethods, + fitFullData = fitFullData ) } # The fields a pipeline reads, as a plain list so call sites index it without # caring that the caller passed a constructor result. # @noRd -.cvResolve <- function(crossValidationArgs) { +.cvResolve <- function(crossValidationParam) { list( - folds = crossValidationArgs$folds %||% 0, - numThreads = crossValidationArgs$numThreads %||% 1, - samplePartition = crossValidationArgs$samplePartition, + folds = crossValidationParam$folds %||% 0, + numThreads = crossValidationParam$numThreads %||% 1, + samplePartition = crossValidationParam$samplePartition, # -1 is twasWeightsCv()'s "no cap" sentinel. fineMappingPipeline # resolves these two as well but never reads them. - maxVariants = crossValidationArgs$maxVariants %||% -1, - weightMethods = crossValidationArgs$weightMethods + maxVariants = crossValidationParam$maxVariants %||% -1, + weightMethods = crossValidationParam$weightMethods, + fitFullData = crossValidationParam$fitFullData %||% TRUE ) } @@ -601,8 +635,8 @@ CrossValidationParam <- function( # and anything that needs out-of-fold predictions has to ask this rather than # testing `folds` itself. # @noRd -.cvEnabled <- function(crossValidationArgs) { - folds <- crossValidationArgs$folds %||% 0 +.cvEnabled <- function(crossValidationParam) { + folds <- crossValidationParam$folds %||% 0 !is.null(folds) && length(folds) == 1L && !is.na(folds) && folds >= 2L } @@ -611,9 +645,9 @@ CrossValidationParam <- function( # unimplemented but meaningless -- saying so beats running to completion and # returning results that were never cross-validated. # @noRd -.cvRefuseOnSumstats <- function(crossValidationArgs, pipeline, cls) { - set <- names(crossValidationArgs)[ - map_lgl(as.list(crossValidationArgs), .cvFieldIsSet) +.cvRefuseOnSumstats <- function(crossValidationParam, pipeline, cls) { + set <- names(crossValidationParam)[ + map_lgl(as.list(crossValidationParam), .cvFieldIsSet) ] if (length(set) == 0L) { return(invisible(NULL)) @@ -627,9 +661,10 @@ CrossValidationParam <- function( # A field counts as "set" when it differs from the constructor's own default, # so passing CrossValidationParam() unchanged is not mistaken for a request. +# fitFullData = TRUE is that field's default. # @noRd .cvFieldIsSet <- function(x) { - if (is.null(x)) { + if (is.null(x) || isTRUE(x)) { return(FALSE) } # numThreads = 1 and folds = 0 are the no-op defaults. @@ -648,10 +683,14 @@ CrossValidationParam <- function( # two pipelines disagreed on what an unspecified `cvFolds` meant; converging # on "off unless asked" makes one bundle mean one thing. The knock-on is that # the SR-TWAS ensemble, which needs out-of-fold predictions, no longer runs by -# default -- so `ensemble` defaults to FALSE too, and asking for an ensemble +# default -- so `EnsembleParam(enabled =)` defaults to FALSE too, and asking +# for an ensemble # without folds is an error rather than a silently missing row. # -# Two of the five fields are honoured by twasWeightsPipeline() only, and are +# `fitFullData` means the same thing in both pipelines: keep only the +# cross-validation results, skipping the full-data fit. +# +# Two of the six fields are honoured by twasWeightsPipeline() only, and are # IGNORED rather than rejected by fineMappingPipeline() -- one bundle the # caller can hand to either pipeline without rewriting it: # diff --git a/R/ctwasPipeline.R b/R/ctwasPipeline.R index f4b0599e6..e095f8bc2 100644 --- a/R/ctwasPipeline.R +++ b/R/ctwasPipeline.R @@ -158,21 +158,19 @@ BoundaryMergeParam <- function( # in the messages are the pipeline's own, which drop the `Args` suffix. # @noRd .ctwasAssertBundles <- function( - ctwasPriorArgs, - variantPruningArgs, - boundaryMergeArgs, + ctwasPriorParam, + variantPruningParam, + boundaryMergeParam, methodArgs ) { - .assertMethodParam(ctwasPriorArgs, "CtwasPriorParam", "ctwasPrior") + .assertMethodParam(ctwasPriorParam, "CtwasPriorParam") .assertMethodParam( - variantPruningArgs, - "VariantPruningParam", - "variantPruning" + variantPruningParam, + "VariantPruningParam" ) .assertMethodParam( - boundaryMergeArgs, - "BoundaryMergeParam", - "boundaryMerge" + boundaryMergeParam, + "BoundaryMergeParam" ) .assertMethodOptions(methodArgs, "CtwasOptions", "methodArgs") } @@ -180,7 +178,7 @@ BoundaryMergeParam <- function( #' @title Causal TWAS Pipeline (cTWAS, multi LD block) #' @description Pipeline that hands a per-block set of #' \code{\link{GwasSumStats}} of GWAS Z-scores together with the matching -#' per-block per-gene TWAS weights and LD sketches to +#' per-block per-gene TWAS weights and LD references to #' \code{ctwas::ctwas_sumstats}, producing per-gene posterior inclusion #' probabilities for causal genes. Optionally accepts a precomputed TWAS-Z #' \code{GRanges} from \code{\link{causalInferencePipeline}} as the @@ -191,18 +189,28 @@ BoundaryMergeParam <- function( #' \code{blockId} column -- build it with #' \code{loadGwasSumStatsFromManifest(..., ldBlocks = )}. Per-block #' \code{region_info}, \code{LD_map}, and \code{snp_map} entries are built -#' automatically from the LD sketch and concatenated before the call to +#' automatically from the LD reference and concatenated before the call to #' \code{ctwas::ctwas_sumstats}. A single-block input is rejected: cTWAS's EM #' cannot converge on a single region, so callers must supply at least two #' blocks. #' -#' @section LD-sketch compatibility check: Per block: -#' \code{ldSketch(twasWeights)} (when non-NULL) must come from the same -#' reference panel as \code{ldSketch(gwasSumStats)} --- same samples, same -#' allele orientation on the shared variants. The two need NOT carry the same -#' variants; a partial overlap, which is what QC-ing the two sides separately -#' produces, only warns. A different sample set, a swapped A1/A2 on a shared -#' variant, or no shared variant at all is a hard error. +#' @section cTWAS's log: cTWAS narrates each of its steps through the +#' \code{logging} package, which writes to the console with \code{cat()}, +#' out of reach of \code{suppressMessages()}. The pipeline keeps that +#' narration off the console, cTWAS's warnings excepted. To keep the full +#' log, give a step a log file through its options, e.g. +#' \code{CtwasEstParamOptions(logfile = "est_param.log")}. +#' +#' @section LD reference compatibility check: Per block: +#' \code{ldReference(twasWeights)} (when non-NULL) must be the same LD +#' reference as \code{ldReference(gwasSumStats)}. +#' The two must be the same kind of reference. Two genotype panels must +#' share their samples and allele orientation; two \code{LdMatrix} objects +#' must agree on the LD they share; two \code{GenotypeMixture} objects must +#' average the same panels with the same weights. The two need NOT carry the +#' same variants; a partial overlap, which is what QC-ing the two sides +#' separately produces, only warns. A mismatch of any of those, or no shared +#' variant at all, is a hard error. #' #' @param gwasSumStats A \code{\link{GwasSumStats}} whose elements are LD #' blocks (at least two), keyed by its \code{blockId} column, with @@ -242,17 +250,18 @@ BoundaryMergeParam <- function( #' fine-mapping. Default \code{5}. Pass-through to #' \code{ctwas::ctwas_sumstats}. #' @param numThreads Number of cores. Default \code{1}. -#' @param ctwasPriorArgs The EM stage that estimates cTWAS's group priors, built -#' with \code{\link{CtwasPriorParam}}: \code{thin}, \code{niterPrefit}, -#' \code{niter}, \code{varStructure} and \code{fallbackToPrefit}. Handed -#' whole to \code{\link{estCtwasGroupPriors}}. -#' @param variantPruningArgs Which variants survive into each gene's weight +#' @param ctwasPriorParam The EM stage that estimates cTWAS's group priors, +#' built with \code{\link{CtwasPriorParam}}: \code{thin}, +#' \code{niterPrefit}, \code{niter}, \code{varStructure} and +#' \code{fallbackToPrefit}. Handed whole to +#' \code{\link{estCtwasGroupPriors}}. +#' @param variantPruningParam Which variants survive into each gene's weight #' matrix, built with \code{\link{VariantPruningParam}}: #' \code{weightCutoff}, \code{csMinCor}, \code{pipRescue} and #' \code{maxVariants}. Handed whole to #' \code{\link{assembleCtwasInputs}}. The \code{csMinCor} / #' \code{pipRescue} rescues need a \code{fineMappingResult}. -#' @param boundaryMergeArgs Whether a high-PIP gene straddling an LD-block +#' @param boundaryMergeParam Whether a high-PIP gene straddling an LD-block #' boundary has its regions merged and re-fine-mapped, built with #' \code{\link{BoundaryMergeParam}}: \code{enabled} (default #' \code{FALSE}), \code{pipThreshold}, \code{filterCs} and @@ -291,12 +300,13 @@ BoundaryMergeParam <- function( #' IRanges::IRanges(c(10000000, 15000001), c(15000000, 19000000)), #' blockId = c("chr22_1", "chr22_2")) #' gss <- loadGwasSumStatsFromManifest(manifest = mani, genome = "hg38", -#' ldSketch = ldStem, region = "chr22:10000000-19000000", ldBlocks = blocks) +#' ldReference = ldStem, region = "chr22:10000000-19000000", +#' ldBlocks = blocks) #' gwasByRegion <- summaryStatsQc(gss, #' panelFilterParam = PanelFilterParam(mafCutoff = 0.0025)) #' ctwasPipeline(gwasSumStats = gwasByRegion, #' twasWeights = list(ctwasWeightsExample), -#' ctwasPrior = CtwasPriorParam(thin = 1, niterPrefit = 3, niter = 10, +#' ctwasPriorParam = CtwasPriorParam(thin = 1, niterPrefit = 3, niter = 10, #' fallbackToPrefit = TRUE), #' methodArgs = CtwasOptions( #' regionData = CtwasRegionDataOptions(min_group_size = 1), @@ -313,16 +323,16 @@ ctwasPipeline <- function( method = NULL, maxNumSingleEffects = 5L, numThreads = 1L, - ctwasPriorArgs = CtwasPriorParam(), - variantPruningArgs = VariantPruningParam(), - boundaryMergeArgs = BoundaryMergeParam(), + ctwasPriorParam = CtwasPriorParam(), + variantPruningParam = VariantPruningParam(), + boundaryMergeParam = BoundaryMergeParam(), keepSnps = FALSE, methodArgs = CtwasOptions() ) { .ctwasAssertBundles( - ctwasPriorArgs, - variantPruningArgs, - boundaryMergeArgs, + ctwasPriorParam, + variantPruningParam, + boundaryMergeParam, methodArgs ) .ctwasRequireNamedLists(gwasSumStats, twasWeights) @@ -335,11 +345,11 @@ ctwasPipeline <- function( twasWeights = twasWeights, twasZ = twasZ, fineMappingResult = fineMappingResult, - variantPruningArgs = variantPruningArgs, - ctwasPriorArgs = ctwasPriorArgs, + variantPruningParam = variantPruningParam, + ctwasPriorParam = ctwasPriorParam, numThreads = numThreads, maxNumSingleEffects = maxNumSingleEffects, - boundaryMergeArgs = boundaryMergeArgs, + boundaryMergeParam = boundaryMergeParam, gwasStudy = gwasStudy, keepSnps = keepSnps, methodArgs = methodArgs @@ -362,15 +372,15 @@ ctwasPipeline <- function( # @noRd .ctwasRunStages <- function( inputs, - ctwasPriorArgs, - boundaryMergeArgs, + ctwasPriorParam, + boundaryMergeParam, maxNumSingleEffects, numThreads, methodArgs ) { est <- estCtwasGroupPriors( inputs, - ctwasPriorArgs = ctwasPriorArgs, + ctwasPriorParam = ctwasPriorParam, numThreads = numThreads, methodArgs = methodArgs ) @@ -385,12 +395,12 @@ ctwasPipeline <- function( numThreads = numThreads, methodArgs = methodArgs ) - if (!isTRUE(boundaryMergeArgs$enabled)) { + if (!isTRUE(boundaryMergeParam$enabled)) { return(finemap) } .ctwasMaybeMerge( finemap, - boundaryMergeArgs = boundaryMergeArgs, + boundaryMergeParam = boundaryMergeParam, maxNumSingleEffects = maxNumSingleEffects, numThreads = numThreads, methodArgs = methodArgs @@ -407,11 +417,11 @@ ctwasPipeline <- function( twasWeights, twasZ, fineMappingResult, - variantPruningArgs, - ctwasPriorArgs, + variantPruningParam, + ctwasPriorParam, numThreads, maxNumSingleEffects, - boundaryMergeArgs, + boundaryMergeParam, gwasStudy, keepSnps, methodArgs @@ -422,12 +432,12 @@ ctwasPipeline <- function( twasZ = twasZ, fineMappingResult = fineMappingResult, method = m, - variantPruningArgs = variantPruningArgs + variantPruningParam = variantPruningParam ) merged <- .ctwasRunStages( inputs, - ctwasPriorArgs = ctwasPriorArgs, - boundaryMergeArgs = boundaryMergeArgs, + ctwasPriorParam = ctwasPriorParam, + boundaryMergeParam = boundaryMergeParam, maxNumSingleEffects = maxNumSingleEffects, numThreads = numThreads, methodArgs = methodArgs @@ -445,15 +455,15 @@ ctwasPipeline <- function( # @noRd .ctwasMaybeMerge <- function( posteriors, - boundaryMergeArgs, + boundaryMergeParam, maxNumSingleEffects, numThreads, methodArgs ) { # The bundle's terminal; mergeCtwasBoundaryRegions() takes scalars. - mergePipThresh <- boundaryMergeArgs$pipThreshold - mergeFilterCs <- boundaryMergeArgs$filterCs - mergeMaxSNP <- boundaryMergeArgs$maxSnp + mergePipThresh <- boundaryMergeParam$pipThreshold + mergeFilterCs <- boundaryMergeParam$filterCs + mergeMaxSNP <- boundaryMergeParam$maxSnp # methodArgs stays a record all the way down: mergeCtwasBoundaryRegions # takes one, and c()-ing it into an argument list would append the S4 # object as a single element rather than splice its entries. @@ -499,7 +509,8 @@ ctwasPipeline <- function( #' IRanges::IRanges(c(10000000, 15000001), c(15000000, 19000000)), #' blockId = c("chr22_1", "chr22_2")) #' gss <- loadGwasSumStatsFromManifest(manifest = mani, genome = "hg38", -#' ldSketch = ldStem, region = "chr22:10000000-19000000", ldBlocks = blocks) +#' ldReference = ldStem, region = "chr22:10000000-19000000", +#' ldBlocks = blocks) #' gwasByRegion <- summaryStatsQc(gss, #' panelFilterParam = PanelFilterParam(mafCutoff = 0.0025)) #' assembleCtwasInputs(gwasSumStats = gwasByRegion, @@ -511,18 +522,17 @@ assembleCtwasInputs <- function( twasZ = NULL, fineMappingResult = NULL, method = NULL, - variantPruningArgs = VariantPruningParam() + variantPruningParam = VariantPruningParam() ) { .assertMethodParam( - variantPruningArgs, - "VariantPruningParam", - "variantPruning" + variantPruningParam, + "VariantPruningParam" ) # The bundle's terminal: .ctwasBuildWeights and below take scalars. - twasWeightCutoff <- variantPruningArgs$weightCutoff - csMinCor <- variantPruningArgs$csMinCor - minPipCutoff <- variantPruningArgs$pipRescue - maxNumVariants <- variantPruningArgs$maxVariants + twasWeightCutoff <- variantPruningParam$weightCutoff + csMinCor <- variantPruningParam$csMinCor + minPipCutoff <- variantPruningParam$pipRescue + maxNumVariants <- variantPruningParam$maxVariants .ctwasValidateGwasList(gwasSumStats) globalPanelInfo <- .ctwasGlobalPanelInfo(gwasSumStats) gwasSumStats <- .ctwasGwasByBlock(gwasSumStats) @@ -618,8 +628,8 @@ assembleCtwasInputs <- function( } # One single-element GwasSumStats per LD block, keyed by blockId -- the shape -# the two-pass assembly consumes. The collection's own ldSketch rides along on -# each subset, so per-region LD lookups are unchanged. +# the two-pass assembly consumes. The collection's own ldReference rides along +# on each subset, so per-region LD lookups are unchanged. # @noRd .ctwasGwasByBlock <- function(gwasSumStats) { set_names( @@ -629,7 +639,7 @@ assembleCtwasInputs <- function( } # One block's slice of the collection. `[` carries the collection-level -# ldSketch through verbatim, and combineGwasSumStats() deliberately UNIONS +# ldReference through verbatim, and combineGwasSumStats() deliberately UNIONS # the per-piece panels, so without narrowing here every block references the # whole chromosome and its LD panel would cover all of it (a # chr19-wide panel runs to ~155 GB). Narrowed to the block's own variants @@ -637,13 +647,13 @@ assembleCtwasInputs <- function( # @noRd .ctwasPickBlock <- function(i, x) { block <- x[i] - sketch <- ldSketch(block) + sketch <- ldReference(block) if (is.null(sketch)) { return(block) } methods::initialize( block, - ldSketch = .subsetSketchToIds(sketch, as.list(block)) + ldReference = .subsetLdRefToIds(sketch, as.list(block)) ) } @@ -722,13 +732,6 @@ assembleCtwasInputs <- function( } } -# First pass: cache LD panels + build z_snp / region_info / snp_map per region. -# We need the union of GWAS variant ids ACROSS all blocks before filtering each -# per-block TwasWeights (a gene's weight variants can straddle adjacent blocks). -# Returns list(ldPanelsByRegion, zSnpPieces, regionInfoPieces, snpMap, -# ldFileByRegion). -# @noRd -# One region's GWAS LD sketch, checked against the region's weights. # The ctwas-shaped snpInfo for the whole collection's panel, captured BEFORE # .ctwasGwasByBlock() narrows each block's sketch to its own variants. A # gene's cis SPAN has to cover every block it reaches or @@ -739,26 +742,27 @@ assembleCtwasInputs <- function( # that case with its own message, so this must not pre-empt it. # @noRd .ctwasGlobalPanelInfo <- function(gwasSumStats) { - sketch <- ldSketch(gwasSumStats) + sketch <- ldReference(gwasSumStats) if (is.null(sketch)) { return(NULL) } .ctwasSnpInfoForBlock(sketch) } +# One region's GWAS LD sketch, checked against the region's weights. # @noRd .ctwasRegionGwasLd <- function(rid, gwasSumStats, twasWeights) { - gwasLd <- ldSketch(gwasSumStats[[rid]]) + gwasLd <- ldReference(gwasSumStats[[rid]]) if (is.null(gwasLd)) { msg <- glue( "ctwasPipeline: GwasSumStats for region '{rid}' carries no ", - "ldSketch (ldSketch = NULL); cTWAS requires an LD reference." + "ldReference (ldReference = NULL); cTWAS requires an LD reference." ) abort(msg) } tw <- twasWeights[[rid]] if (!is.null(tw)) { - .ctwasRequireMatchingLdSketches(ldSketch(tw), gwasLd) + .ctwasRequireMatchingLdRefs(ldReference(tw), gwasLd) } gwasLd } @@ -935,7 +939,11 @@ assembleCtwasInputs <- function( ) list( z_snp = zSnp, - z_gene = if (!is.null(twasZ)) .ctwasBuildZGene(twasZ) else NULL, + z_gene = if (!is.null(twasZ)) { + .ctwasBuildZGene(twasZ) + } else { + .ctwasGeneZ(weightsList, zSnp) + }, weights = weightsList, region_info = regionInfo, snp_map = fp$snpMap, @@ -946,6 +954,11 @@ assembleCtwasInputs <- function( ) } +# estCtwasGroupPriors(): CtwasPriorParam is the bundle's terminal; the EM +# helpers take its fields as plain scalars. assemble_region_data does not echo +# z_gene back, so the precomputed z_gene passed in is propagated onto the +# result (inputs$z_gene is NULL when twasZ was not supplied) so $z_gene +# resolves to the right entry. #' Estimate cTWAS group prior + prior variance #' #' @description Step 2 of the three-step \code{\link{ctwasPipeline}}: assembles @@ -955,7 +968,7 @@ assembleCtwasInputs <- function( #' \code{boundary_genes}, \code{z_gene}, and \code{param}. #' #' @param inputs A list returned by \code{\link{assembleCtwasInputs}}. -#' @param ctwasPriorArgs The EM settings, built with +#' @param ctwasPriorParam The EM settings, built with #' \code{\link{CtwasPriorParam}}: \code{thin} (SNP thinning when #' assembling region data), \code{niterPrefit} and \code{niter} (prefit / #' accurate EM iterations), \code{varStructure}, and @@ -971,7 +984,7 @@ assembleCtwasInputs <- function( #' @examples #' data(ctwasInputsExample) #' estCtwasGroupPriors(ctwasInputsExample, -#' ctwasPrior = CtwasPriorParam(thin = 1, niterPrefit = 3, niter = 10, +#' ctwasPriorParam = CtwasPriorParam(thin = 1, niterPrefit = 3, niter = 10, #' fallbackToPrefit = TRUE), #' methodArgs = CtwasOptions( #' regionData = CtwasRegionDataOptions(min_group_size = 1), @@ -982,17 +995,11 @@ assembleCtwasInputs <- function( #' @export estCtwasGroupPriors <- function( inputs, - ctwasPriorArgs = CtwasPriorParam(), + ctwasPriorParam = CtwasPriorParam(), numThreads = 1L, methodArgs = CtwasOptions() ) { - .assertMethodParam(ctwasPriorArgs, "CtwasPriorParam", "ctwasPrior") - # The bundle's terminal: the EM helpers below take plain scalars. - thin <- ctwasPriorArgs$thin - niterPrefit <- ctwasPriorArgs$niterPrefit - niter <- ctwasPriorArgs$niter - groupPriorVarStructure <- ctwasPriorArgs$varStructure - fallbackToPrefit <- ctwasPriorArgs$fallbackToPrefit + .assertMethodParam(ctwasPriorParam, "CtwasPriorParam") .assertMethodOptions(methodArgs, "CtwasOptions", "methodArgs") if (!requireNamespace("ctwas", quietly = TRUE)) { abort("Package 'ctwas' is required for estCtwasGroupPriors.") @@ -1003,7 +1010,7 @@ estCtwasGroupPriors <- function( regionData <- .ctwasAssembleRegionData( inputs, zGene, - thin, + ctwasPriorParam$thin, numThreads, methodArgs$regionData ) @@ -1014,17 +1021,14 @@ estCtwasGroupPriors <- function( ) paramRes <- .ctwasEstParamOrFallback( regionData, - niterPrefit, - niter, - groupPriorVarStructure, + ctwasPriorParam$niterPrefit, + ctwasPriorParam$niter, + ctwasPriorParam$varStructure, numThreads, - thin, - fallbackToPrefit, + ctwasPriorParam$thin, + ctwasPriorParam$fallbackToPrefit, methodArgs$estParam ) - # assemble_region_data does not echo z_gene back, so propagate the - # precomputed z_gene we passed in (inputs$z_gene is NULL when twasZ was not - # supplied) so $z_gene resolves to the right entry. c( list_assign(inputs, z_gene = zGene), list( @@ -1121,13 +1125,14 @@ estCtwasGroupPriors <- function( #' IRanges::IRanges(c(10000000, 15000001), c(15000000, 19000000)), #' blockId = c("chr22_1", "chr22_2")) #' gss <- loadGwasSumStatsFromManifest(manifest = mani, genome = "hg38", -#' ldSketch = ldStem, region = "chr22:10000000-19000000", ldBlocks = blocks) +#' ldReference = ldStem, region = "chr22:10000000-19000000", +#' ldBlocks = blocks) #' gwasByRegion <- summaryStatsQc(gss, #' panelFilterParam = PanelFilterParam(mafCutoff = 0.0025)) #' inp <- assembleCtwasInputs(gwasSumStats = gwasByRegion, #' twasWeights = list(ctwasWeightsExample)) #' est <- estCtwasGroupPriors(inp, -#' ctwasPrior = CtwasPriorParam(thin = 1, niterPrefit = 3, niter = 10, +#' ctwasPriorParam = CtwasPriorParam(thin = 1, niterPrefit = 3, niter = 10, #' fallbackToPrefit = TRUE), #' methodArgs = CtwasOptions( #' regionData = CtwasRegionDataOptions(min_group_size = 1), @@ -1257,7 +1262,7 @@ mergeCtwasBoundaryRegions <- function( # as a positional argument. user <- as.list(methodArgs$merge) callArgs <- c(fa$args, user[setdiff(names(user), names(fa$args))]) - res <- exec(fa$fn, !!!callArgs) + res <- .ctwasQuietly(fa$fn, callArgs) .ctwasApplyMergeResult(finemapResult, res) } @@ -1331,7 +1336,7 @@ mergeCtwasBoundaryRegions <- function( } args <- c(args, usable) } - exec(fn, !!!args) + .ctwasQuietly(fn, args) } # Run ONLY ctwas's prefit EM step against `region_data` and return a @@ -1436,9 +1441,9 @@ mergeCtwasBoundaryRegions <- function( # ============================================================================= # LD-sketch compatibility check. Thin wrapper over the shared -# `.requireMatchingLdSketches` helper (R/ld.R). -.ctwasRequireMatchingLdSketches <- function(twLd, gwasLd) { - .requireMatchingLdSketches(twLd, gwasLd, pipelineName = "ctwasPipeline") +# `.requireMatchingLdRefs` helper (R/ld.R). +.ctwasRequireMatchingLdRefs <- function(twLd, gwasLd) { + .requireMatchingLdRefs(twLd, gwasLd, pipelineName = "ctwasPipeline") } # Resolve which TWAS method's weights to feed into ctwas given a @@ -1564,16 +1569,6 @@ mergeCtwasBoundaryRegions <- function( abort(msg) } -# Parse cTWAS block-manifest keys ("chr1_1000_2000" / "chr1:1000-2000") into a -# per-key GRanges. -# -# This is cTWAS's OWN `region_id` concept, not pecotmr's: cTWAS keys its -# region_info, snp_map and per-region data by these strings, so the package has -# to be able to read them even though pecotmr's own collections now key on the -# element range instead (section 4.4). A key that carries no coordinates -# becomes a 0-width chrUn sentinel, which matches no anchor and so yields the -# documented "NA when the anchor falls in no block". -# @noRd # One block id parsed into chrom/start/end, or the unplaced sentinel when it # does not parse as a range. # @noRd @@ -1596,6 +1591,16 @@ mergeCtwasBoundaryRegions <- function( ) } +# Parse cTWAS block-manifest keys ("chr1_1000_2000" / "chr1:1000-2000") into a +# per-key GRanges. +# +# This is cTWAS's OWN `region_id` concept, not pecotmr's: cTWAS keys its +# region_info, snp_map and per-region data by these strings, so the package has +# to be able to read them even though pecotmr's own collections now key on the +# element range instead (section 4.4). A key that carries no coordinates +# becomes a 0-width chrUn sentinel, which matches no anchor and so yields the +# documented "NA when the anchor falls in no block". +# @noRd .ctwasBlockGrFromIds <- function(ids) { coords <- map(ids, .ctwasBlockCoords) GenomicRanges::GRanges( @@ -1675,16 +1680,12 @@ mergeCtwasBoundaryRegions <- function( length(g) == 0L } -# Bucket a flat weight source into a per-region named list keyed to the block -# grid, homing each gene by start(region). Each per-block sub-collection carries -# that block's GWAS LD sketch (the panel its weights are harmonized against, and -# what the downstream match-check expects). -# @noRd # The genes homed into region `rid`, carrying that region's LD sketch, or # NULL when nothing landed there. `slot<-` applied as a function returns a # copy rather than writing into the subset in place. # @noRd #' @importFrom methods slot<- +# @noRd .ctwasBucketForRegion <- function(rid, combined, home, gwasSumStats) { idx <- which(home == rid) if (length(idx) == 0L) { @@ -1692,11 +1693,16 @@ mergeCtwasBoundaryRegions <- function( } `slot<-`( combined[idx, ], - "ldSketch", - value = ldSketch(gwasSumStats[[rid]]) + "ldReference", + value = ldReference(gwasSumStats[[rid]]) ) } +# Bucket a flat weight source into a per-region named list keyed to the block +# grid, homing each gene by start(region). Each per-block sub-collection carries +# that block's GWAS LD sketch (the panel its weights are harmonized against, and +# what the downstream match-check expects). +# @noRd .ctwasBucketWeights <- function(weights, gwasSumStats) { combined <- .ctwasCombineWeightSources(weights) # Placement anchors on the GENE's own position, not on a stored analysis @@ -2074,7 +2080,7 @@ mergeCtwasBoundaryRegions <- function( return(ri) } cols <- names(ri) - if (!all(c("chrom", "start", "stop") %in% cols)) { + if (!all(is_in(c("chrom", "start", "stop"), cols))) { return(ri) } gr <- GenomicRanges::GRanges( @@ -2315,14 +2321,9 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { } # Row subset carries every column forward (joint* / region / ...); the old # hand-listed rebuild silently dropped them. - methods::initialize(tw[keep, ], ldSketch = ldSketch(tw)) + methods::initialize(tw[keep, ], ldReference = ldReference(tw)) } -# Build the per-variant Z data.frame ctwas expects from a GwasSumStats. -# Stacks each study row's GRanges via the shared `.entryToSumstatDf` -# helper (R/sumstatsQc.R), then projects to ctwas's column shape and -# bolts on the `study` column ctwas uses to disambiguate stacked rows. -# @noRd # Match variant ids against the LD panel's ids the way every other LD consumer # in the package does -- by (chrom, pos, allele) with ref/alt swaps tolerated -- # rather than by exact string identity. @@ -2330,7 +2331,7 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # ctwas already harmonizes TWAS weights this way (see # `.ctwasHarmonizeWeights()` below). The GWAS side used plain string joins, so # one variant could be kept for the weights and dropped for the z-scores. -# `removeStrandAmbiguous = FALSE` matches `.ldFromSketchMatch()`, the shared +# `removeStrandAmbiguous = FALSE` matches `.ldFromRefMatch()`, the shared # entry point: the panel defines the frame, so an A/T variant is not discarded # merely for being palindromic. # @noRd @@ -2390,6 +2391,11 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { ) } +# Build the per-variant Z data.frame ctwas expects from a GwasSumStats. +# Stacks each study row's GRanges via the shared `.entryToSumstatDf` +# helper (R/sumstatsQc.R), then projects to ctwas's column shape and +# bolts on the `study` column ctwas uses to disambiguate stacked rows. +# @noRd .ctwasBuildZSnp <- function(gwasSumStats, panelIds) { pieces <- map( seq_len(nrow(gwasSumStats)), @@ -2399,10 +2405,6 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { .ctwasHarmonizeZToPanel(bind_rows(pieces), panelIds) } -# Derive the single-row region_info from the LD sketch's snpInfo -# (min/max BP per chromosome). The sketch is assumed to cover exactly -# one block. -# @noRd # @noRd .ctwasEntryPositions <- function(i, gss) { gr <- gss[[i]] @@ -2428,11 +2430,15 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { list_c(pieces) } +# The single-row region_info for block `regionId`: its chromosome and the +# [start, stop] span of the GWAS variants it holds. An empty block, or one +# spanning several chromosomes, is an error. +# @noRd .ctwasBuildSingleRegionInfo <- function(regionId, gss) { # Derive the block's [start, stop] from the GWAS variants actually in this # block (the GwasSumStats entry GRanges) -- NOT the LD sketch. When many # blocks share one whole-chromosome LD payload (the common one-file-per-chr - # layout), snpInfo(ldSketch) spans the entire chromosome, so every region + # layout), snpInfo(ldReference) spans the entire chromosome, so every region # would collapse to the same whole-chromosome [start, stop] and every SNP # would be assigned to every region (inflating SNP group_size N-fold and # diluting the gene prior to ~0). @@ -2476,9 +2482,9 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # supported by the downstream cTWAS model. # @noRd .ctwasSnpInfoForBlock <- function(gwasLd) { - gr <- .ldSketchRanges(gwasLd) + gr <- .ldRefRanges(gwasLd) mc <- S4Vectors::mcols(gr) - # `.ldSketchMatchIds()`, not the raw SNP label: this id becomes the R + # `.ldRefMatchIds()`, not the raw SNP label: this id becomes the R # dimnames, the `variance` names, `panelSnps` and the weight-harmonization # reference id all at once, and every one of those is compared against a # harmonized (reference-allele) id from the GWAS or the weights. A panel @@ -2489,8 +2495,8 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # (df[, "pos"] -> vector for region-bound min/max); a tibble column is a # 1-col list and errors min(). data.frame( - chrom = as.integer(.ldSketchChrom(gwasLd)), - id = .ldSketchMatchIds(gwasLd), + chrom = as.integer(.ldRefChrom(gwasLd)), + id = .ldRefMatchIds(gwasLd), pos = as.integer(GenomicRanges::start(gr)), alt = as.character(mc$A1), ref = as.character(mc$A2), @@ -2502,9 +2508,10 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # from so the expensive part can be built later. Returns a list with: # snpInfo : ctwas-shaped per-block table (chrom, id, pos, alt, ref) # -- both the snp_map element and the snpinfo loader return. -# variance : named numeric vector of per-variant dosage variance from the -# LD reference, used to scale non-standardized TWAS weights to -# the correlation scale ctwas expects. +# variance : named numeric vector of per-variant genotype variance from +# the LD reference (see .ldRefVariance()), used to scale +# non-standardized TWAS weights to the correlation scale ctwas +# expects. # sketch : the block's narrowed LD sketch, retained because the n x n # correlation is deliberately NOT built here. # @@ -2513,21 +2520,16 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # O(n^2) apiece on panels that are frequently never read at all. # @noRd .ctwasPanelFor <- function(gwasLd) { - # Share the validator with `.ldFromSketch()`, the entry point every other + # Share the validator with `.ldFromRef()`, the entry point every other # pipeline uses: skipping the guard meant a NULL or non-panel sketch # surfaced as "unable to find an inherited method for 'snpInfo'" # instead of saying the LD reference was missing. - .ldFromSketchValidate(gwasLd, "ctwasPipeline") + .ldFromRefValidate(gwasLd, "ctwasPipeline") snpInfo <- .ctwasSnpInfoForBlock(gwasLd) - geno <- .ldSketchDosage( - gwasLd, - seq_len(nrow(snpInfo)), - meanImpute = TRUE - ) list( snpInfo = snpInfo, variance = set_names( - apply(geno, 2, stats::var, na.rm = TRUE), + .ldRefVariance(gwasLd, seq_len(nrow(snpInfo))), snpInfo$id ), sketch = gwasLd @@ -2549,51 +2551,9 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { ) } -# The correlation over `vids` alone. Identical to slicing the full matrix -- -# mean imputation is per column, so correlating a subset of the dosages gives -# the same pairwise values -- at O(|vids|^2) instead of O(n^2). -# @noRd -.ctwasPanelLdSubset <- function(panel, vids) { - idx <- match(vids, panel$snpInfo$id) - if (anyNA(idx)) { - msg <- glue( - "ctwasPipeline: {sum(is.na(idx))} weight variant(s) absent from ", - "the block's LD panel." - ) - abort(msg) - } - if (!is.null(panel$R)) { - return(panel$R[vids, vids, drop = FALSE]) - } - .ctwasPanelLdFor(panel, idx, vids) -} - # @noRd .ctwasPanelLdFor <- function(panel, idx, ids) { - geno <- .ldSketchDosage(panel$sketch, idx, meanImpute = TRUE) - `dimnames<-`( - computeLd(geno, method = "sample"), - list(ids, ids) - ) -} - -# Whether to build the block's full correlation once and slice it per gene, -# or to compute one small correlation per gene. Per-gene submatrices are what -# keep a block ctwas never fine-maps from ever forming its n x n matrix, but -# g genes on |vids| variants each cost sum(|vids|^2) against n^2 for the -# whole block, so the full matrix wins once the weights are dense enough. -# Returns the full matrix in that case and NULL in the other. -# @noRd -.ctwasRWgtSource <- function(ldPanel, nVids) { - # Already built: slicing it is free, so there is nothing to weigh up. - if (!is.null(ldPanel$R)) { - return(ldPanel$R) - } - n <- as.numeric(nrow(ldPanel$snpInfo)) - if (length(nVids) == 0L || sum(as.numeric(nVids)^2) < n^2) { - return(NULL) - } - .ctwasPanelLd(ldPanel) + `dimnames<-`(.ldRefLdAt(panel$sketch, idx), list(ids, ids)) } # Harmonize TWAS weight variants against the LD reference panel. Same @@ -2729,10 +2689,12 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # Build the weights list ctwas expects: keyed by per-tuple gene id, # each element a list with wgt (variants x 1 matrix; rownames = SNP id), -# R_wgt (per-gene LD submatrix), and gene metadata. ctwas's compute_gene_z -# pulls rownames(wgt) for the SNP IDs and computes z.gene = crossprod(wgt, -# z.s) / sqrt(t(wgt) %*% R_wgt %*% wgt), so wgt must be a numeric matrix -# (not a vector) and R_wgt must be the LD submatrix over the same SNPs. +# wgt_var (the weight variance w'Rw), and gene metadata. ctwas's +# compute_gene_z would compute z.gene = crossprod(wgt, z.s) / +# sqrt(t(wgt) %*% R_wgt %*% wgt) from a per-gene LD submatrix R_wgt; the +# pipeline computes the same z from wgt_var instead (.ctwasGeneZ()) and +# supplies it, so wgt must still be a numeric matrix (not a vector) but no +# weight LD matrix is formed. # # The panel variants a gene's cis span may reach. # @@ -2770,10 +2732,9 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # table wherever the two overlap, so nothing about the in-block result # changes. # -# Genes are prepared in one sweep and given their `R_wgt` in a second, so -# that choice is made with every gene's fitted size in hand -- which is what -# lets a block no gene needs densely skip its n x n correlation entirely. -# .ctwasRWgtSource() picks, and says why. Variants absent from the panel are +# Genes are prepared in one sweep and given their weight variances in a +# second, so the block's LD is read once, over every gene's fitted variants +# together (.ctwasWeightVariances()). Variants absent from the panel are # dropped from that gene's row set. # @noRd .ctwasBuildWeights <- function( @@ -2812,12 +2773,10 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { twasWeights = twasWeights, ctx = ctx )) - fullR <- .ctwasRWgtSource(ldPanel, map_int(prepared, .ctwasPreparedSize)) - genes <- map( + genes <- map2( prepared, + .ctwasWeightVariances(ldPanel, prepared), .ctwasGeneEntryFor, - ldPanel = ldPanel, - fullR = fullR, spanInfo = spanInfo ) # Later genes overwrite an earlier one sharing a key, which is what the @@ -2913,19 +2872,23 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { if (anyNA(varLookup)) { msg <- glue( ".ctwasBuildWeights: missing genotype variance for ", - "{sum(is.na(varLookup))} variant(s) in the LD panel." + "{sum(is.na(varLookup))} variant(s) in the LD panel; an LdMatrix ", + "needs an `AF` column to scale non-standardized weights." ) abort(msg) } renormalized * sqrt(varLookup) } -# The ctwas per-gene weight entry (weight matrix, LD submatrix, chrom/BP span, -# plus identity metadata). +# The ctwas per-gene weight entry (weight matrix, weight variance w'Rw, +# chrom/BP span, plus identity metadata). ctwas would read the weights' LD +# submatrix (R_wgt) only to compute the gene z (compute_gene_z); the pipeline +# supplies that z itself (.ctwasGeneZ()), so the entry carries the scalar +# w'Rw instead. # # `spanVids` is the variant set the chrom/p0/p1 span is measured over, and it is # deliberately allowed to be WIDER than the fitted `vids`: ctwas fine-maps one -# region at a time, so `wgt` / `R_wgt` must stay inside that region, while +# region at a time, so `wgt` must stay inside that region, while # `ctwas::get_boundary_genes` reads p0/p1 to decide which genes straddle a # region boundary. Measuring the span over the fitted subset would clip a # boundary gene down to its home region and hide it from merge_regions. @@ -2933,19 +2896,19 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { .ctwasGeneEntry <- function( vids, w, - rWgt, + wgtVar, meta, spanVids = vids, spanInfo ) { - # Span coordinates come from the whole-panel table, while `rWgt` was + # Span coordinates come from the whole-panel table, while `wgtVar` was # computed on the block's own panel: ctwas fine-maps one region at a # time, but a boundary gene's span has to reach past it. panelInfo <- spanInfo rowIdx <- match(spanVids, panelInfo$id) list( wgt = matrix(w, ncol = 1L, dimnames = list(vids, "wgt")), - R_wgt = rWgt, + wgt_var = wgtVar, type = meta$context, context = meta$context, gene_name = meta$trait, @@ -2984,7 +2947,7 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # @noRd .ctwasGenePrepared <- function(i, twasWeights, ctx) { # Polymorphic: the weight source may be a TwasWeights or a - # FineMappingResult, so the payload comes from the class-aware bridge. + # fine-mapping result, so the payload comes from the class-aware bridge. parts <- .rowParts(twasWeights, i) aligned <- .ctwasAlignGeneWeights(parts, ctx$refVariants, ctx$panelSnps) if (is.null(aligned)) { @@ -3021,9 +2984,8 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { if (!any(inRegion)) { return(NULL) } - # No LD yet. R_wgt is built in a second sweep, once every gene's fitted - # size is known and the block can choose between forming one full - # correlation and one small correlation per gene. + # No LD yet: the weight variances come in a second sweep, from one read + # of the block's LD over every gene's fitted variants together. list( key = meta$key, vids = kept$vids[inRegion], @@ -3033,27 +2995,15 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { ) } +# One prepared gene's ctwas entry, given its weight variance w'Rw. # @noRd -.ctwasPreparedSize <- function(g) { - length(g$vids) -} - -# One prepared gene's ctwas entry. `fullR` is the block's correlation when -# .ctwasRWgtSource() chose to form it, and NULL when each gene computes its -# own small one. -# @noRd -.ctwasGeneEntryFor <- function(g, ldPanel, fullR, spanInfo) { - rWgt <- if (is.null(fullR)) { - .ctwasPanelLdSubset(ldPanel, g$vids) - } else { - fullR[g$vids, g$vids, drop = FALSE] - } +.ctwasGeneEntryFor <- function(g, wgtVar, spanInfo) { list( key = g$key, entry = .ctwasGeneEntry( g$vids, g$w, - rWgt, + wgtVar, g$meta, spanVids = g$spanVids, spanInfo = spanInfo @@ -3061,6 +3011,102 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { ) } +# Each prepared gene's weight variance w'Rw, from one read of the block's LD +# over the union of the genes' weight variants. ctwas reads a gene's weight +# LD only for this quadratic form (compute_gene_z), so the pipeline computes +# the gene z itself (.ctwasGeneZ()) and no per-gene or block-wide LD matrix +# is ever formed for it. +# @noRd +.ctwasWeightVariances <- function(ldPanel, prepared) { + if (length(prepared) == 0L) { + return(numeric(0)) + } + vids <- unique(list_c(map(prepared, "vids"))) + ld <- .ctwasPanelWeightLd(ldPanel, vids) + map_dbl(prepared, .ctwasWeightVariance, ld = ld) +} + +# The block's LD over `vids`, for weight variances: list(F = ) holding a +# genotype reference's factor (.ldRefFactor()), or list(R = ) holding the +# matrix a panel already carries -- a caller who computed it, or a payload it +# travelled with -- or an LdMatrix's. +# @noRd +.ctwasPanelWeightLd <- function(ldPanel, vids) { + idx <- match(vids, ldPanel$snpInfo$id) + if (anyNA(idx)) { + msg <- glue( + "ctwasPipeline: {sum(is.na(idx))} weight variant(s) absent from ", + "the block's LD panel." + ) + abort(msg) + } + if (!is.null(ldPanel$R)) { + return(list(R = ldPanel$R[vids, vids, drop = FALSE])) + } + if (.ldIsGenotypeRef(ldPanel$sketch)) { + factor <- .ldRefFactor(ldPanel$sketch, idx) + return(list(F = `colnames<-`(factor, vids))) + } + list(R = .ctwasPanelLdFor(ldPanel, idx, vids)) +} + +# One gene's weight variance w'Rw from .ctwasPanelWeightLd()'s LD. +# @noRd +.ctwasWeightVariance <- function(g, ld) { + if (!is.null(ld$F)) { + f <- ld$F[, g$vids, drop = FALSE] + return(sum((f %*% g$w)^2) / (nrow(f) - 1)) + } + r <- ld$R[g$vids, g$vids, drop = FALSE] + as.numeric(crossprod(g$w, r %*% g$w)) +} + +# The gene z-scores ctwas::compute_gene_z() would compute, +# w'z / sqrt(w'Rw), from each entry's weights and the weight variance the +# pipeline already holds (`wgt_var`), with ctwas's columns and its rule of +# dropping a gene whose z is infinite. +# @noRd +.ctwasGeneZ <- function(weightsList, zSnp) { + if (length(weightsList) == 0L) { + return(NULL) + } + zGene <- list_rbind(map2( + weightsList, + names(weightsList), + .ctwasGeneZRow, + zSnp = zSnp + )) + infinite <- is.infinite(zGene$z) + if (any(infinite)) { + warn(glue( + "ctwasPipeline: dropped {sum(infinite)} gene(s) with an infinite ", + "z-score." + )) + } + as.data.frame(filter(zGene, !infinite)) +} + +# One gene's z row. Every weight variant must carry a GWAS z, as +# compute_gene_z() requires. +# @noRd +.ctwasGeneZRow <- function(entry, id, zSnp) { + wgt <- entry$wgt + zs <- zSnp$z[match(rownames(wgt), zSnp$id)] + if (anyNA(zs)) { + abort(glue( + "ctwasPipeline: {sum(is.na(zs))} weight variant(s) of gene ", + "'{id}' have no GWAS z-score." + )) + } + tibble( + id = id, + z = as.numeric(crossprod(wgt, zs)) / sqrt(entry$wgt_var), + type = entry$type, + context = entry$context, + group = str_c(entry$context, "|", entry$type) + ) +} + # Which of a gene's weight variants lie in the region being fine-mapped. A NULL # region set means the caller supplied none, so nothing is restricted. # @noRd @@ -3073,7 +3119,7 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # Look up the per-(study, context, trait, method) PIP vector and the # 95% credible-set membership / purity for one gene from the supplied -# FineMappingResult. Returns NULL when no FineMappingResult was passed +# fine-mapping result. Returns NULL when none was passed # or no matching tuple exists. Output is a list with: # pip : named numeric vector keyed by variant_id # csMembers : list of character vectors (one per CS at 95% coverage) @@ -3118,10 +3164,6 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { list(pip = pip, csMembers = cs$csMembers, csPurity = cs$csPurity) } -# Per-CS membership + purity at 95% coverage from a topLoci table. cs_95 stores -# `_` where idx == 0 means "not in any CS"; cs_95_purity (when -# present) broadcasts one purity value across a CS's rows. -# @noRd # One credible set's member variants and its purity. # @noRd .ctwasCsEntry <- function(k, tl, csIdx, keepIdx) { @@ -3134,11 +3176,15 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { list(members = as.character(tl$variant_id)[inCs], purity = purity) } +# Per-CS membership + purity at 95% coverage from a topLoci table. cs_95 stores +# `_` where idx == 0 means "not in any CS"; cs_95_purity (when +# present) broadcasts one purity value across a CS's rows. +# @noRd .ctwasCsMembership <- function(tl) { if (!is_in("cs_95", names(tl))) { return(list(csMembers = list(), csPurity = numeric(0))) } - csIdx <- suppressWarnings(as.integer(str_remove(tl$cs_95, "^.*_"))) + csIdx <- .csLabelIndex(tl$cs_95) keepIdx <- !is.na(csIdx) & csIdx > 0L entries <- map( sort(unique(csIdx[keepIdx])), @@ -3265,9 +3311,6 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { ) } -# Variants that must survive the cap: members of any high-purity (>= csMinCor) -# credible set, plus any with PIP > minPipCutoff. Empty when no finemapAux. -# @noRd # Variants rescued by credible set `k`, when that set is pure enough. # @noRd .ctwasCsRescued <- function(k, finemapAux, vids, csMinCor) { @@ -3277,6 +3320,9 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { intersect(finemapAux$csMembers[[k]], vids) } +# Variants that must survive the cap: members of any high-purity (>= csMinCor) +# credible set, plus any with PIP > minPipCutoff. Empty when no finemapAux. +# @noRd .ctwasMustKeep <- function(vids, finemapAux, csMinCor, minPipCutoff) { if (is.null(finemapAux)) { return(character(0)) @@ -3427,8 +3473,8 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { ) } -# The panel cache the loader closures were built over, or NULL when the -# payload carries loaders this package did not create. +# The panel cache a payload's loader carries, or NULL when the payload +# carries loaders this package did not create. # @noRd .ctwasCachedPanels <- function(payload) { loader <- payload$LD_loader_fun @@ -3442,57 +3488,70 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { # `LD_loader_fun(LD_file)` per region during region_data assembly and # fine-mapping; we dispatch by `LD_file` (the same string set on # `LD_map$LD_file`) into the cached per-sketch ldPanel. +# +# ctwas passes the loader that one argument and nothing else, so the panel +# cache travels on the function object itself, as its `ldPanelsByRegion` +# attribute: .ctwasLoadRegionLd() reads it back through sys.function(), and +# .ctwasCachedPanels() reads the same attribute, so the cache has one home. # @noRd .ctwasMultiBlockLdLoader <- function(ldPanelsByRegion) { - force(ldPanelsByRegion) - fn <- function(LD_file) { - panel <- ldPanelsByRegion[[LD_file]] - if (is.null(panel)) { - msg <- glue( - "ctwasPipeline LD loader: no cached panel for ", - "LD_file = '{LD_file}'" - ) - abort(msg) - } - # Built here rather than up front: this is the only place a region's - # full LD is actually needed, and ctwas reaches it only for regions - # that survived screening. - .ctwasPanelLd(panel) - } - # Published explicitly so .ctwasCachedPanels can recover the cache from a - # loader we built, instead of looking the name up inside the closure's - # environment. - `attr<-`(fn, "ldPanelsByRegion", ldPanelsByRegion) + `attr<-`(.ctwasLoadRegionLd, "ldPanelsByRegion", ldPanelsByRegion) +} + +# The LD loader ctwas calls: region `LD_file`'s full LD, from the panel cache +# carried on this function's own attribute (see .ctwasMultiBlockLdLoader()). +# Built here rather than up front: this is the only place a region's full LD +# is actually needed, and ctwas reaches it only for regions that survived +# screening. +# @noRd +.ctwasLoadRegionLd <- function(LD_file) { + .ctwasPanelLd(.ctwasLoaderPanel(sys.function(), LD_file, "LD loader")) } # Multi-block SNP-info loader for ctwas. Mirrors the LD loader. # @noRd .ctwasMultiBlockSnpInfoLoader <- function(ldPanelsByRegion) { - force(ldPanelsByRegion) - function(LD_file) { - panel <- ldPanelsByRegion[[LD_file]] - if (is.null(panel)) { - msg <- glue( - "ctwasPipeline snpInfo loader: no cached panel for ", - "LD_file = '{LD_file}'" - ) - abort(msg) - } - panel$snpInfo + `attr<-`(.ctwasLoadRegionSnpInfo, "ldPanelsByRegion", ldPanelsByRegion) +} + +# The snpInfo loader ctwas calls, reading the same attribute. +# @noRd +.ctwasLoadRegionSnpInfo <- function(LD_file) { + .ctwasLoaderPanel(sys.function(), LD_file, "snpInfo loader")$snpInfo +} + +# The cached panel `loader` carries for `LD_file`; an error naming the token +# when its cache has none. +# @noRd +.ctwasLoaderPanel <- function(loader, LD_file, what) { + panel <- attr(loader, "ldPanelsByRegion")[[LD_file]] + if (is.null(panel)) { + msg <- glue( + "ctwasPipeline {what}: no cached panel for ", + "LD_file = '{LD_file}'" + ) + abort(msg) } + panel } -# Derive the LD_file token for ctwas from a GenotypeHandle. We point -# at the on-disk file that already backs the sketch's data, so the -# `file.exists(LD_map$LD_file)` assertion in ctwas::ctwas_sumstats -# passes WITHOUT pecotmr doing any new I/O. The token also serves as -# the dispatch key for the multi-block LD / snpInfo loaders, so two -# blocks sharing the same on-disk LD payload share one cached panel. +# The on-disk file that backs an LD panel's data, checked to exist before the +# panel builder reads dosages out of it. An LdMatrix holds its LD in memory, +# and a panel held in memory its dosages, so neither has a payload to find. # @noRd .ctwasLdPanelKey <- function(sketch) { - handle <- .ldSketchHandle(sketch) - fmt <- genotypeFormat(handle) - stem <- .genotypeReadPath(handle) + if (.ldIsMatrixRef(sketch)) { + return(invisible(NULL)) + } + if (.ldIsMixtureRef(sketch)) { + walk(.gmComponents(sketch), .ctwasLdPanelKey) + return(invisible(NULL)) + } + fmt <- .ldRefFormat(sketch) + if (is.null(fmt)) { + return(invisible(NULL)) + } + stem <- .ldRefReadPath(sketch) candidates <- switch( fmt, "plink2" = c(str_c(stem, ".pgen")), @@ -3504,8 +3563,8 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { hit <- candidates[file.exists(candidates)] if (length(hit) == 0L) { msg <- glue( - "ctwasPipeline: could not derive an existing LD-file token for ", - "the GenotypeHandle (format={fmt}, path={stem}). Looked for: ", + "ctwasPipeline: could not find the file behind the LD panel ", + "(format={fmt}, path={stem}). Looked for: ", "{str_flatten(candidates, ', ')}" ) abort(msg) @@ -3572,11 +3631,6 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { invisible(NULL) } -# Build a per-block snpInfo table restricted to variants present in the -# GwasSumStats entry. Mirrors `.ctwasSnpInfoForBlock` but restricts to -# the block's GWAS variants (intersected against the cached panel) so -# snp_map[[region_id]] is sized to the block, not the whole panel. -# @noRd # One entry's variant ids, or none when it does not carry a SNP column. # @noRd .ctwasEntrySnpIds <- function(i, gwasSumStats) { @@ -3587,6 +3641,11 @@ asCtwasResult <- function(finemapResult, keepSnps = FALSE) { as.character(mc$SNP) } +# Build a per-block snpInfo table restricted to variants present in the +# GwasSumStats entry. Mirrors `.ctwasSnpInfoForBlock` but restricts to +# the block's GWAS variants (intersected against the cached panel) so +# snp_map[[region_id]] is sized to the block, not the whole panel. +# @noRd .ctwasSnpInfoForGwasBlock <- function(gwasSumStats, panelSnpInfo) { blockIds <- unique(.ctwasConcatChr(map( seq_len(nrow(gwasSumStats)), diff --git a/R/ctwasWrapper.R b/R/ctwasWrapper.R index 7595b0a99..790151539 100644 --- a/R/ctwasWrapper.R +++ b/R/ctwasWrapper.R @@ -7,6 +7,37 @@ # forwards arguments to one of those calls. # ============================================================================= +# Run a ctwas step with its console log lowered to warnings. ctwas narrates +# every step through the logging package, whose console handler writes to +# stdout with cat(), so neither suppressMessages() nor knitr's message = FALSE +# can hide it. Only that handler is lowered: a log file asked of the step +# (`logfile=`) still receives every record. A handler the step adds is removed +# after it, as ctwas never removes the one it opens for `logfile=`, which +# would otherwise go on receiving every later step's records. +# @noRd +.ctwasQuietly <- function(fn, args) { + if (!requireNamespace("logging", quietly = TRUE)) { + return(exec(fn, !!!args)) + } + before <- names(logging::getLogger()[["handlers"]]) + on.exit(.ctwasDropAddedHandlers(before), add = TRUE) + console <- logging::getHandler("basic.stdout") + if (!is.null(console)) { + level <- console[["level"]] + logging::setLevel("WARN", console) + on.exit(logging::setLevel(level, console), add = TRUE) + } + exec(fn, !!!args) +} + +# Remove the log handlers added since `before` was taken. +# @noRd +.ctwasDropAddedHandlers <- function(before) { + added <- setdiff(names(logging::getLogger()[["handlers"]]), before) + walk(added, logging::removeHandler) + invisible(NULL) +} + # z_gene for assemble_region_data (which requires it non-NULL): use the caller's # inputs$z_gene, else compute via ctwas::compute_gene_z (mirrors # ctwas_sumstats). Routed through .ctwasInvoke so `methodArgs` reaches it -- diff --git a/R/enloc.R b/R/enloc.R index 35c6605cf..5dd1c5c74 100644 --- a/R/enloc.R +++ b/R/enloc.R @@ -30,7 +30,7 @@ NULL # The GWAS Bayes factors fastenloc recovers from fine-mapped PIPs, contrasting # against "every variant in the cluster has no effect" (controller.cc): # log10bf = log10(pip) - log10(P_gwas) + log10(1 - P_gwas) - log10(1 - cpip) -# A zero PIP is floored, as upstream does, so the log is finite. +# A zero PIP is floored, as the original fastenloc does, so the log is finite. # @noRd .enlocGwasLog10Bf <- function(gwasPip, pGwas, clusterPip) { floored <- replace(gwasPip, gwasPip == 0, 1e-10) @@ -93,8 +93,8 @@ enlocClusterProbabilities <- function( )) } bf <- 10^gwasLog10Bf - # The cluster's total eQTL posterior, capped as upstream does so that - # log(1 - clusterPip) stays finite. + # The cluster's total eQTL posterior, capped as the original fastenloc + # does so that log(1 - clusterPip) stays finite. clusterPip <- min(sum(qtlPip), 1 - 1e-8) pri <- .enlocConfigPriors(pi1e, pi1ne, p) # case 4, split by whether the GWAS hit is the causal eQTL @@ -124,7 +124,7 @@ enlocClusterProbabilities <- function( # enloc's priors from the enrichment parameters, as # fastenloc's set_enrich_params(a0, a1) computes them. `a1` is capped first, -# matching the --cap_a1 option upstream. +# matching the original fastenloc's --cap_a1 option. # @noRd .enlocPriorsFromEnrichment <- function(a0, a1, pQtl, capA1 = Inf) { a1 <- min(a1, capA1) diff --git a/R/exampleData.R b/R/exampleData.R index 3527bba39..9c12051bf 100644 --- a/R/exampleData.R +++ b/R/exampleData.R @@ -80,8 +80,8 @@ NULL #' chr22) keyed by \code{study_1} / \code{susie}, carrying the SuSiE-RSS fit #' and a per-variant table with the standard post-processing columns #' (\code{pip}, \code{logBF}, \code{cs_95} / \code{cs_70} / \code{cs_50} and -#' their purities, marginal statistics). Four credible sets. \code{ldSketch} is -#' the bundled \code{toy_canonical} genotype panel, present +#' their purities, marginal statistics). Four credible sets. \code{ldReference} +#' is the bundled \code{toy_canonical} genotype panel, present #' because \code{\link{qtlEnrichmentPipeline}} requires the GWAS collection to #' carry one as its RSS-derived marker; the LD actually used for the fit is an #' in-memory correlation matrix computed from \code{\link{eqtlRegionExample}}. @@ -264,7 +264,7 @@ NULL #' @description A pre-QC'd \code{\link{QtlSumStats}} collection covering the #' same (study, context, trait) tuple as \code{\link{qtlDatasetExample}}; #' the per-variant Z / BETA / SE / N values were computed from the synthetic -#' phenotype by per-variant linear regression. The \code{ldSketch} slot +#' phenotype by per-variant linear regression. The \code{ldReference} slot #' references the same toy PLINK1 panel. #' #' @format A \code{QtlSumStats} S4 collection with one row (study1, brain, @@ -319,7 +319,7 @@ NULL #' synthetic trait (\code{"trait1"}, N = 50,000). The signal pattern shares #' one causal variant with \code{\link{qtlSumStatsExample}} (for #' colocalization demos) and adds a second GWAS-only causal variant. -#' The \code{ldSketch} slot references the same toy PLINK1 panel used +#' The \code{ldReference} slot references the same toy PLINK1 panel used #' for the QTL example. #' #' @format A \code{GwasSumStats} S4 collection with one row (trait1) @@ -605,7 +605,10 @@ NULL #' #' @format A list with the cTWAS input components \code{z_snp}, \code{z_gene}, #' \code{weights}, \code{region_info}, \code{snp_map}, \code{LD_map}, and the -#' LD / SNP-info loader closures. +#' LD / SNP-info loader closures. Each \code{weights} entry carries the +#' weights \code{wgt} and their variance \code{wgt_var} (w'Rw), from which +#' the pipeline computes \code{z_gene}, in place of ctwas's per-gene LD +#' matrix \code{R_wgt}. #' #' @keywords data #' @@ -673,16 +676,18 @@ NULL #' @description An \code{LdEigen} reference for the eigendecomposition-based #' heritability estimators (\code{\link{estimateH2}} with #' \code{method = "lder"} or \code{"hdl"}, and -#' \code{\link{computeLdScores}}). Built by \code{\link{buildLdEigen}} from -#' the first 14 chr22 LD blocks of the de-identified \code{protocol_example} -#' reference panel, so the LD is real rather than simulated. Describes the -#' same variants as \code{\link{ldScoreExample}}, so the four estimators can -#' be compared on one reference. -#' -#' The panel behind it has \code{nSamples = 1000}. That is small enough that -#' \code{method = "hdl"}, which models reference-panel noise explicitly, -#' shrinks its estimate hard -- correct behaviour for a panel this size -#' rather than a fault. +#' \code{\link{computeLdScores}}). Built by \code{\link{buildLdEigen}} +#' over the first 14 chr22 LD blocks of the de-identified +#' \code{protocol_example} reference. Describes the same variants as +#' \code{\link{ldScoreExample}}, so the four estimators can be compared on +#' one reference. +#' +#' The LD is that of a panel of 20,000 people simulated from the +#' \code{protocol_example} panel's own LD, which is a sketch of 60 people: +#' the estimators correct for the noise in a reference's LD, and at 60 that +#' correction swamps the signal. A large simulated panel keeps the LD +#' structure real while giving the reference a realistic size +#' (\code{nSamples = 20000}); see \code{inst/scripts/build_h2_examples.R}. #' #' @format An \code{LdEigen} object (extends \code{LdStatistic}) with an #' \code{eigenList} of per-block \code{values} / \code{vectors} / @@ -706,10 +711,10 @@ NULL #' @description An \code{LdScore} reference (per-variant LD scores, weights, #' and per-block LD matrices) for the LD-score-based heritability estimators #' (\code{\link{estimateH2}} with \code{method = "sldsc"} or -#' \code{"gldsc"}). Built by \code{\link{buildLdScore}} from the first 14 -#' chr22 LD blocks of the de-identified \code{protocol_example} reference -#' panel, so the LD is real rather than simulated. Describes the same variants -#' as \code{\link{ldEigenExample}}. +#' \code{"gldsc"}). Built by \code{\link{buildLdScore}} over the first 14 +#' chr22 LD blocks of the de-identified \code{protocol_example} reference, +#' from the same simulated 20,000-person panel as +#' \code{\link{ldEigenExample}}, and describes the same variants. #' #' The per-block LD matrices are retained because \code{"gldsc"} and #' stratified \code{"sldsc"} both need them; unstratified \code{"sldsc"} diff --git a/R/fineMappingPipeline.R b/R/fineMappingPipeline.R index 8b6ffcb50..821a8c754 100644 --- a/R/fineMappingPipeline.R +++ b/R/fineMappingPipeline.R @@ -64,13 +64,13 @@ #' @section QC contract: All \code{QtlSumStats} and \code{GwasSumStats} inputs #' must have been QC'd via \code{\link{summaryStatsQc}}; the pipeline errors #' on inputs where \code{length(qcInfo(x)) == 0L}. \code{summaryStatsQc} -#' also drops variants absent from the \code{ldSketch}, so by the time +#' also drops variants absent from the \code{ldReference}, so by the time #' per-entry processing runs every variant is guaranteed to be present in the #' LD panel and a local LD matrix can be built with #' \code{extractBlockGenotypes} + \code{computeLd("sample")}. #' #' @section Optional resume cache: Supplying \code{fineMappingResult} of an -#' existing \code{FineMappingResult} skips re-fitting any \code{(study, +#' existing fine-mapping result skips re-fitting any \code{(study, #' context, trait, method)} tuple that already has a matching row; cached #' entries are merged with the newly-fit entries in the returned collection. #' @@ -82,8 +82,8 @@ #' \itemize{ #' \item TWAS weights computation (\code{twasWeights = TRUE} path): #' lives in \code{\link{twasWeightsPipeline}} now. -#' \item Filtering knobs (now \code{genotypeFilter} / -#' \code{panelFilter}, \code{ldReferenceMetaFile}): individual- +#' \item Filtering knobs (now \code{genotypeFilterParam} / +#' \code{panelFilterParam}, \code{ldReferenceMetaFile}): individual- #' level QC lives on the \code{QtlDataset} constructor; sumstat #' QC lives in \code{summaryStatsQc()}. No filtering happens #' inside this pipeline. @@ -97,10 +97,10 @@ #' \item \code{loadRssData} and explicit #' \code{ldReferenceMetaFile} arguments: the new #' \code{QtlSumStats} / \code{GwasSumStats} carry the -#' (already-QC'd) sumstats and \code{ldSketch} directly. +#' (already-QC'd) sumstats and \code{ldReference} directly. #' \item Verbose \code{methodName} suffixing (e.g. #' \code{"susie_rss_NO_QC"}, \code{"susie_rss_SLALOM_RAISS_imputed"}): -#' the method column on the returned \code{FineMappingResult} +#' the method column on the returned collection #' carries the bare token (\code{"susie"}, #' \code{"susieInf"}, \code{"mvsusie"}, ...) only. QC #' provenance is recorded on the sumstats' \code{qcInfo}. @@ -131,10 +131,21 @@ #' by the pipeline (e.g. you can override \code{model_init} #' even when fitting from a susieInf chain). #' } -#' @param contexts Optional character vector of context names. Default -#' \code{NULL} (all contexts). -#' @param traitId Optional character vector of trait names to restrict -#' processing to. +#' @param contexts Optional context selection; \code{NULL} (the default) uses +#' every context. A character vector applies to every study: a context no +#' study has is an error, and one only some studies lack is skipped for +#' those, with a warning. A list keyed by study name picks each study's +#' contexts separately (\code{list(A = "liver")}); studies it does not name +#' keep all of theirs. +#' @param traitId Optional trait selection; \code{NULL} (the default) uses +#' every trait, or those overlapping \code{region} when one is given. A +#' character vector applies to every study. A list keyed by study name +#' picks each study's traits separately (\code{list(A = "ENSG1")}), and a +#' study's entry may itself be a list keyed by context +#' (\code{list(A = list(brain = "ENSG1"))}). Studies and contexts the list +#' does not name keep all of their traits. A joint fit spanning a study's +#' contexts uses every trait selected in any of them, minus the (context, +#' trait) pairs a per-context entry leaves out. #' @param region Optional variant window for QtlDataset trait selection: a #' \code{GRanges}, a \code{"chr:start-end"} string, or a one-row data.frame #' with \code{chrom}/\code{start}/\code{end}. Mutually exclusive with @@ -172,15 +183,22 @@ #' \code{maxNumSingleEffectsGreedy} are the exception: they are seeded #' onto each SuSiE-family token's own arguments, so changing them needs a #' refit. -#' @param fineMappingResult Optional existing \code{FineMappingResult} to use as -#' a resume cache; tuples already present are not refit. -#' @param crossValidationArgs Cross-validation settings, built with +#' @param fineMappingResult Optional existing \code{QtlFineMappingResult} or +#' \code{GwasFineMappingResult} to use as a resume cache; tuples already +#' present are not refit. +#' @param crossValidationParam Cross-validation settings, built with #' \code{\link{CrossValidationParam}}: \code{folds} (default \code{0}, no #' CV), \code{numThreads} for the per-fold refits, and \code{samplePartition} #' to reuse a fixed partition across methods. When \code{folds > 1} each #' method is refit on every fold's training samples and used to predict the #' held-out ones; the partition, the out-of-fold predictions and the metrics #' are stored on each \code{FineMappingRow}'s \code{cvResult} slot. +#' \code{fitFullData = FALSE} keeps only that: each row carries its +#' \code{cvResult} and no full-data fit, variants or top loci. A +#' \code{fineMappingResult} resume cache is only reused where it carries +#' what the run asks for -- a full-data fit unless the run is +#' cross-validation-only, and a \code{cvResult} when the run +#' cross-validates -- and the other tuples are refit. #' #' Cross-validation holds out \strong{samples}, so it is not possible on #' \code{QtlSumStats} / \code{GwasSumStats} input. Those methods accept @@ -194,7 +212,7 @@ #' reproducible fits; the global RNG state is restored on return. Default #' \code{NULL} #' (no seeding). -#' @param signalScreenArgs Individual-level single-effect (SER) pre-screen +#' @param signalScreenParam Individual-level single-effect (SER) pre-screen #' applied to each residualized \code{(X, y)} block before a full fit, #' built with \code{\link{SignalScreenParam}}. A susie model with #' \code{L = 1} is fit and the block is skipped unless the chosen metric @@ -211,38 +229,45 @@ #' \code{\link{SignalScreenParam}} enforces when you build it. Unset (the #' default) screens nothing. The summary-statistics analog lives in #' \code{\link{summaryStatsQc}}. -#' @param usePCA Logical (length 1). \code{QtlDataset} only. When \code{TRUE} +#' @param usePCA Logical (length 1). Individual-level data only: a +#' \code{QtlDataset}, or the \code{QtlDataset} components of a +#' \code{MultiStudyQtlDataset}. When \code{TRUE} #' (default \code{FALSE}), each multi-trait context's PCA-reduced phenotype is #' fine-mapped with univariate SuSiE on its top principal components (ports #' the legacy \code{fsusie.R} \code{susie_on_top_pc}). Each PC becomes a #' pseudo-trait row keyed \code{trait = "topPC\{i\}"}, \code{method = #' "susie"}. Single-trait contexts have no PCA and are skipped. -#' @param nPCs Integer (length 1). \code{QtlDataset} only. Caps the number of -#' top principal components fine-mapped per context when \code{usePCA = TRUE} -#' (default \code{10}). The effective count is \code{min(nPCs, usable -#' traits)}. -#' @param jointSpecification Optional joint-fit specification (NULL by default). -#' When NULL, the pipeline runs the implicit multi-context / multi-trait -#' mvSuSiE / fSuSiE branches as before. When non-NULL, the argument is parsed -#' and validated via the joint-spec grammar documented under -#' \code{parseJointSpecification} (a character vector of axes, or a list of -#' \code{list(axes, scope)} specs); the per-spec axis dispatcher -#' implementation is in progress and a non-NULL value currently errors with an -#' informative message. See the design notes in \code{R/jointSpecification.R} -#' for the accepted grammar. +#' @param nPCs Integer (length 1). Individual-level data only. Caps the +#' number of top principal components fine-mapped per context when +#' \code{usePCA = TRUE} (default \code{10}). The effective count is +#' \code{min(nPCs, usable traits)}. +#' @param jointSpecification Optional joint-fit specification saying which +#' axes \code{mvsusie} / \code{fsusie} fit across. When NULL (the default) +#' they fit across traits if the input has two or more, else across +#' contexts. Otherwise give a character vector of axes (one spec) or a list +#' of specs. A spec is a character vector drawn from \code{"study"}, +#' \code{"context"} and \code{"trait"}, or +#' \code{list(axes = , scope = )}, where \code{scope} +#' (keys \code{study}, \code{context}, \code{trait}) limits the fit to the +#' listed values, each of which must exist in \code{data}. A list-form +#' spec always goes inside the outer list, even on its own. Other methods +#' run per (study, context, trait) as usual. The \code{"study"} axis needs +#' summary statistics: it is rejected on a \code{QtlDataset}, and on a +#' \code{MultiStudyQtlDataset} only the summary-statistics studies take +#' part. #' @section Panel filters on the RSS path: On \code{QtlSumStats} / #' \code{GwasSumStats} input there is no genotype matrix to filter, so -#' \code{panelFilter}'s \code{mafCutoff} / \code{macCutoff} / +#' \code{panelFilterParam}'s \code{mafCutoff} / \code{macCutoff} / #' \code{imissCutoff} are measured against the \strong{LD reference panel} #' instead: a variant whose panel genotypes fall below the cutoffs is #' dropped before the z-scores and LD matrix are built. The thresholds mean -#' the same thing as \code{genotypeFilter}'s on the \code{QtlDataset} path -#' (MAC is converted to a MAF equivalent and the stricter of the two +#' the same thing as \code{genotypeFilterParam}'s on the \code{QtlDataset} +#' path (MAC is converted to a MAF equivalent and the stricter of the two #' applies), so one number carries across input types. The defaults filter #' nothing. #' #' This discards \emph{observed} variants, unlike -#' \code{summaryStatsQc(imputeArgs = ...)}, which only bounds which variants +#' \code{summaryStatsQc(raissParam = ...)}, which only bounds which variants #' RAISS will impute. Use it when the panel cannot support the LD estimate a #' rare variant would need. #' @@ -266,7 +291,7 @@ #' @param naAction Character. How to handle missing values in the extracted #' phenotype/genotype data. #' @param verbose Verbosity (0 silent, 1 default). Default \code{1}. -#' @param residualizationArgs Covariate residualization settings, built with +#' @param residualizationParam Covariate residualization settings, built with #' \code{\link{ResidualizationParam}}: \code{phenotypeCovariates} and #' \code{genotypeCovariates} name which covariates to regress out #' (\code{NULL}, the default, uses every available one), and @@ -276,7 +301,8 @@ #' Only meaningful for \code{QtlDataset} / \code{MultiStudyQtlDataset} #' input. A \code{QtlSumStats} / \code{GwasSumStats} run has no covariates #' to regress out, so the bundle is \strong{ignored} there rather than -#' refused --- unlike \code{crossValidation}, which errors. Residualization +#' refused --- unlike \code{crossValidationParam}, which errors. +#' Residualization #' is on by default, so refusing a non-default value would reject the #' default bundle; CV is off by default, so a non-default value there is an #' explicit request for something the input cannot do. @@ -291,7 +317,7 @@ #' posterior #' matrices. The per-variant \code{topLoci} table is fully populated #' either way --- its per-credible-set columns are governed by -#' \code{credibleSet}'s \code{perCsColumns}, not by this. +#' \code{credibleSetParam}'s \code{perCsColumns}, not by this. #' @param susieRssParam Summary-statistics solver settings for #' \code{QtlSumStats} / \code{GwasSumStats} input, built with #' \code{\link{SusieRssParam}}: \itemize{ @@ -311,7 +337,7 @@ #' \code{R_finite} correction. \code{NULL} (default) uses susieR's #' own, except when a finite/EB mode is active (\code{serFallback} or #' \code{rMismatch != "none"}), where it falls back to the LD panel's -#' \code{nSamples(ldSketch)}. +#' \code{nSamples(ldReference)}. #' \item \code{rMismatch} (default \code{"none"}) --- LD-mismatch #' correction forwarded as \code{R_mismatch}: also \code{"eb"} or #' \code{"eb_mix"}. @@ -349,10 +375,12 @@ #' is the scale the LD correlations and z-scores behind a TWAS Z are already #' on. #' -#' @return A \code{FineMappingResult} collection keyed by \code{(study, context, -#' trait, method)}. The \code{ldSketch} slot is set automatically: \code{NULL} +#' @return A \code{QtlFineMappingResult} keyed by \code{(study, context, +#' trait, method)} -- for \code{GwasSumStats} input, a +#' \code{GwasFineMappingResult} keyed by \code{(study, method)} and range. +#' The \code{ldReference} slot is set automatically: \code{NULL} #' for individual-level (QtlDataset / all-individual-level -#' MultiStudyQtlDataset) fits, the input's \code{ldSketch} for RSS-derived +#' MultiStudyQtlDataset) fits, the input's \code{ldReference} for RSS-derived #' fits. #' @examples #' data(qtlDatasetExample) @@ -464,10 +492,6 @@ setGeneric("fineMappingPipeline", function(data, ...) { # them with a clear pointer rather than an "unknown token" error. .fmTwasOnlyTokens <- c("mrmash") -# Normalize a user-supplied `methods` argument into a character vector of -# canonical tokens. Mirrors `.twasNormalizeMethods` but the fine-mapping -# pipeline takes only a character vector (no preset strings, no list form). -# @noRd # Normalize a user-supplied `methods` argument into `(tokens, methodArgs)`. # # Accepts: * character vector c("susie", "susieInf") -> empty kwargs per token @@ -821,6 +845,37 @@ setGeneric("fineMappingPipeline", function(data, ...) { .fmrRowParts(fineMappingResult, idx[[1L]]) } +# The rows of a resume cache this run may reuse. A cross-validation-only row +# (no full-data fit) cannot stand in for a run that needs the fit, and a row +# without cross-validation cannot stand in for a run that cross-validates; +# either would come back short of what was asked for, so those tuples are +# refit instead. `cvCfg` is the run's resolved CrossValidationParam. +# @noRd +.fmCacheForRun <- function(fineMappingResult, cvCfg) { + if (!is(fineMappingResult, "FineMappingResultBase")) { + return(fineMappingResult) + } + rows <- seq_len(nrow(fineMappingResult)) + hasFit <- map_lgl( + rows, + .fmrRowHasFit, + fineMappingResult = fineMappingResult + ) + hasCv <- map_lgl(rows, .fmrRowHasCv, fineMappingResult = fineMappingResult) + usable <- (hasFit | !isTRUE(cvCfg$fitFullData)) & + (hasCv | !.cvEnabled(cvCfg)) + if (all(usable)) { + return(fineMappingResult) + } + fineMappingResult[which(usable)] +} + +# Whether row `i` of a fine-mapping result carries a full-data fit. +# @noRd +.fmrRowHasFit <- function(i, fineMappingResult) { + length(.fmrPartsVariantIds(.fmrRowParts(fineMappingResult, i))) > 0L +} + # GwasFineMappingResult cache lookup using the (study, method, range) identity. # Multi-block FMRs carry one entry per block, so the key has to include the # block -- but the block is now the element's own RANGE rather than a stored @@ -903,7 +958,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { jointTraits = NULL, traitPos = NULL, region = NULL, - ldSketch = NULL, + ldReference = NULL, allowEmpty = FALSE ) { if (length(entries) == 0L && !allowEmpty) { @@ -924,7 +979,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { jointTraits = jointTraits, traitPos = traitPos, region = region, - ldSketch = ldSketch + ldReference = ldReference ) } @@ -982,7 +1037,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { entries, blockIds = NULL, region = NULL, - ldSketch = NULL, + ldReference = NULL, allowEmpty = FALSE ) { if (length(entries) == 0L && !allowEmpty) { @@ -998,7 +1053,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { blockId = blockIds, entry = entries, region = region, - ldSketch = ldSketch + ldReference = ldReference ) } @@ -1038,11 +1093,6 @@ setGeneric("fineMappingPipeline", function(data, ...) { set_names(as.numeric(mc$AF), as.character(mc$SNP))[variantIds] } -# Block label derived from a GwasSumStats entry's GRanges. Built through the -# same helper the collection uses for its range key, so a label minted here and -# the identity of the row it ends up on agree by construction rather than by -# two formatters happening to match. -# @noRd # The block key for GWAS entry `i`: the one the caller SUPPLIED when the # GwasSumStats carries it, else derived from the entry's variant span. # @@ -1052,6 +1102,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { # deriving from that element silently discarded a supplied key and replaced a # true block boundary (chr22_49355984_50799822) with the narrower span of the # variants that survived QC (chr22_49363017_50598205). +# # A supplied key is one that carries COORDINATES. GwasSumStats() always # materializes a blockId, but with no `ldBlocks` manifest and no explicit # `blockId` it falls back to the bare seqname ("chr22") -- which is the @@ -1073,9 +1124,12 @@ setGeneric("fineMappingPipeline", function(data, ...) { .fmGwasRangeKey(gr) } -# The entry's own range key. This is what the resume cache matches on -- it -# compares a cached row's DERIVED range key, so it must stay derived even when -# a block manifest supplies a wider boundary for the stored provenance. +# The entry's own range key, built through the same helper the collection uses +# for its range key, so a label minted here and the identity of the row it ends +# up on agree by construction rather than by two formatters happening to match. +# This is what the resume cache matches on -- it compares a cached row's +# DERIVED range key, so it must stay derived even when a block manifest +# supplies a wider boundary for the stored provenance. # @noRd .fmGwasRangeKey <- function(gr) { .rtlOneRangeKey(range(gr)) @@ -1130,7 +1184,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { # A row whose blockId holds no coordinates still needs a range; the # chrUn sentinel marks it, matching .anchorVector()'s convention. sentinel <- GenomicRanges::GRanges("chrUn", IRanges::IRanges(1L, 1L)) - exec(c, !!!map(gs, function(g) if (is.null(g)) sentinel else g)) + exec(c, !!!map(gs, `%||%`, sentinel)) } # @noRd @@ -1189,7 +1243,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { zn <- .fmExtractZn( gr, glue("fineMappingPipeline(GwasSumStats): study='{st}'"), - ldSketch = cfg$ldSketch, + ldReference = cfg$ldReference, cutoffs = .panelCutoffs(cfg$panelFilterParam) ) # Two different things that used to be one: the key the resume cache @@ -1234,7 +1288,10 @@ setGeneric("fineMappingPipeline", function(data, ...) { list( data = data, credibleSetParam = credibleSetParam, - fineMappingResult = fineMappingResult, + fineMappingResult = .fmCacheForRun( + fineMappingResult, + .cvResolve(CrossValidationParam()) + ), fitRetention = fitRetention, verbose = verbose, panelFilterParam = panelFilterParam @@ -1249,7 +1306,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { # @noRd .fmRssRunCfg <- function( common, - ldSketch, + ldReference, initializeWithSusieInf, methodArgs, susieRssParam @@ -1257,7 +1314,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { c( common, list( - ldSketch = ldSketch, + ldReference = ldReference, initializeWithSusieInf = initializeWithSusieInf, methodArgs = methodArgs, susieRssParam = susieRssParam @@ -1269,14 +1326,14 @@ setGeneric("fineMappingPipeline", function(data, ...) { # the block-specific arguments. The RSS analog of .fmFitXBlockP. # @noRd .fmFitRssBlockP <- function(z, zn, toRun, label, af, cfg, region = NULL) { - ldMat <- .ldFromSketch( - cfg$ldSketch, - zn$variantIds, - label = "fineMappingPipeline" - ) .fmFitRssBlock( z, - ldMat, + .fmRssLd( + cfg$ldReference, + zn$variantIds, + toRun, + cfg$initializeWithSusieInf + ), zn$n, toRun, cfg$initializeWithSusieInf, @@ -1292,6 +1349,24 @@ setGeneric("fineMappingPipeline", function(data, ...) { ) } +# The LD one block's RSS fits read, as list(R, X), in the cheapest form each +# fit accepts. A genotype reference gives its factor X (.ldFactorFromRef()): +# the plain susie model and every credible set's purity read it directly, +# never forming the p x p matrix. The matrix R is formed only when a fit has +# no factor route -- susieInf, and susieAsh, whose susie_rss low-rank paths +# fail (dev/ld-factor-audit.md) -- and is all an LdMatrix has. +# @noRd +.fmRssLd <- function(ldReference, variantIds, toRun, initializeWithSusieInf) { + label <- "fineMappingPipeline" + if (!.ldIsGenotypeRef(ldReference)) { + return(list(R = .ldFromRef(ldReference, variantIds, label = label))) + } + X <- .ldFactorFromRef(ldReference, variantIds, label = label) + chain <- .fmResolveSusieChain(toRun, initializeWithSusieInf) + needsR <- chain$runInf || is_in("susieAsh", toRun) + list(R = if (needsR) crossprod(X) / (nrow(X) - 1), X = X) +} + # Fit the still-to-run RSS tokens for one QtlSumStats entry and return one # row-record per fitted token. # @noRd @@ -1303,7 +1378,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { "fineMappingPipeline(QtlSumStats): entry {i} (study='{st}', ", "context='{ctx}', trait='{tr}')" ), - ldSketch = cfg$ldSketch, + ldReference = cfg$ldReference, cutoffs = .panelCutoffs(cfg$panelFilterParam) ) ents <- .fmFitRssBlockP( @@ -1368,20 +1443,20 @@ setGeneric("fineMappingPipeline", function(data, ...) { list(rows = c(cachedRows, computed), skipped = FALSE) } -# Concatenate two same-class FineMappingResult collections row-wise, carrying -# forward every column (delegates to the generic `.rbindCollections`). +# Concatenate two same-class fine-mapping result collections row-wise, +# carrying forward every column (delegates to the generic `.rbindCollections`). # @noRd #' @importFrom checkmate assertClass -.rbindFineMappingResult <- function(a, b, ldSketch = NULL) { +.rbindFineMappingResult <- function(a, b, ldReference = NULL) { assertClass(a, "FineMappingResultBase") assertClass(b, "FineMappingResultBase") # Carry forward every column (blockId / joint* / ...) and reconcile the # collection-level slots via the shared combine; the concrete class # (QTL vs GWAS) is preserved and checked there. - .combineTupleCollections(list(a, b), ldSketch, ".rbindFineMappingResult") + .combineTupleCollections(list(a, b), ldReference, ".rbindFineMappingResult") } -#' Combine FineMappingResult collections +#' Combine fine-mapping result collections #' #' Row-bind two or more fine-mapping result collections of the SAME concrete #' class (all \code{\link{QtlFineMappingResult}} or all @@ -1391,7 +1466,7 @@ setGeneric("fineMappingPipeline", function(data, ...) { #' #' @param ... Two or more \code{FineMappingResultBase} objects, or a single #' \code{list} of them. -#' @param ldSketch Optional genotype panel (see +#' @param ldReference Optional genotype panel (see #' \code{\link{readGenotypes}}) to attach to the #' combined collection. Default \code{NULL}. Applied when combining two or #' more inputs; a single input is returned unchanged. @@ -1401,13 +1476,13 @@ setGeneric("fineMappingPipeline", function(data, ...) { #' data(qtlFineMappingExample) #' combineFineMappingResults(qtlFineMappingExample) #' @export -combineFineMappingResults <- function(..., ldSketch = NULL) { +combineFineMappingResults <- function(..., ldReference = NULL) { parts <- .asCombineList( list(...), "FineMappingResultBase", "combineFineMappingResults" ) - .combineTupleCollections(parts, ldSketch, "combineFineMappingResults") + .combineTupleCollections(parts, ldReference, "combineFineMappingResults") } @@ -1417,7 +1492,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { # would also drop a legitimately zero-length `contexts` / `samples`. .fmResidPheno <- function( x, - residualizationArgs, + residualizationParam, contexts = NULL, traitId = NULL, naAction = NULL @@ -1431,7 +1506,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { !!!c( list( x = x, - residualizationArgs = residualizationArgs %||% + residualizationParam = residualizationParam %||% ResidualizationParam() ), sel @@ -1441,7 +1516,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { .fmResidGeno <- function( x, - residualizationArgs, + residualizationParam, contexts = NULL, traitId = NULL, region = NULL, @@ -1470,7 +1545,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { !!!c( list( x = x, - residualizationArgs = residualizationArgs %||% + residualizationParam = residualizationParam %||% ResidualizationParam() ), sel @@ -1620,6 +1695,37 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { } } +# `region` and `cisWindow` are alternative ways to name the variant window: +# `cisWindow` expands each trait's own coordinates, `region` is taken +# literally. A call supplying both signals a misunderstanding, so it is +# rejected. The one check behind the QtlDataset and MultiStudyQtlDataset +# methods of both fineMappingPipeline() and twasWeightsPipeline(); `label` +# names the caller in the message. +# @noRd +.rejectRegionWithCisWindow <- function(region, cisWindow, label) { + if (!is.null(region) && !is.null(cisWindow)) { + abort(glue( + "{label}: specify either `region` or `cisWindow`, not both. ", + "`cisWindow` expands each trait's own coordinates, whereas ", + "`region` is the literal variant window." + )) + } + invisible(NULL) +} + +# The MultiStudyQtlDataset inputs both pipelines resolve the same way: the +# region + cisWindow guard, the naAction choice and the joint-region spec. The +# per-call filter overrides are left to the components, which apply them to +# their own data. +# @noRd +.msResolveInputs <- function(naAction, region, cisWindow, jointRegions, label) { + .rejectRegionWithCisWindow(region, cisWindow, label) + list( + naAction = arg_match(naAction, c("drop", "impute")), + xRegions = .makeXRegions(region, jointRegions) + ) +} + # Rows of a TwasWeights whose method is mr.mash and whose identity matches # every axis the caller FIXED. A NULL axis means "do not filter on it". # @noRd @@ -1874,10 +1980,12 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { allNames <- unique(list_c(map(tls, names))) csCols <- allNames[str_detect(allNames, "^cs_[0-9]+$")] topLoci <- bind_rows(.fmRenumberCs(tls, csCols)) - susieFit <- set_names( - map(entries, .fmEntrySusieFit), - str_c("region", seq_along(entries)) - ) + fits <- map(entries, .fmEntrySusieFit) + susieFit <- if (all(map_lgl(fits, is.null))) { + NULL + } else { + set_names(fits, str_c("region", seq_along(entries))) + } # Per-region CV partitions/predictions kept under region* names so a # multi-region entry retains each block's CV (NULL when no region had CV). cvList <- set_names( @@ -1893,11 +2001,6 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } -# Renumber per-region credible-set columns so region-local cs indices do not -# collide once concatenated: each region's indices are shifted by the running -# max of the regions before it (a sequential offset fold, per cs_ -# column). -# @noRd # The highest credible-set index table `tl` uses in column `cc`, or 0 when it # has no such column. # @noRd @@ -1945,6 +2048,11 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } +# Renumber per-region credible-set columns so region-local cs indices do not +# collide once concatenated: each region's indices are shifted by the running +# max of the regions before it (a sequential offset fold, per cs_ +# column). +# @noRd .fmRenumberCs <- function(tls, csCols) { # A table's offset in a column is how many credible sets the tables # before it contributed there -- a cumulative count, so it is known up @@ -1959,12 +2067,6 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } -# Merge per-method user kwargs onto a base arg list. `userArgs` is the -# per-token kwargs supplied by the caller (e.g. `list(L = 1, refine = -# FALSE)`); the capability table's `args` default fills in any keys the -# user did not set. User-supplied values always win over base, capability -# defaults, and chain-derived args. Returns the merged list. -# @noRd # Split a merged argument list into the target's own formals plus a # `methodArgs` remainder. Callers build one flat list (base args, capability # defaults, user overrides); this routes each name to the right place, so a @@ -1999,6 +2101,12 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { exec(match.fun(as.character(d[[1L]])), !!!extra) } +# Merge per-method user kwargs onto a base arg list. `userArgs` is the +# per-token kwargs supplied by the caller (e.g. `list(L = 1, refine = +# FALSE)`); the capability table's `args` default fills in any keys the +# user did not set. User-supplied values always win over base, capability +# defaults, and chain-derived args. Returns the merged list. +# @noRd .fmMergeUserArgs <- function(baseArgs, token, userArgs = NULL) { if (is.null(userArgs)) { userArgs <- list() @@ -2028,13 +2136,13 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { # GwasSumStats entry GRanges. Errors when Z or N is missing. Wraps the # shared `.entryToSumstatDf` helper (R/sumstatsQc.R). # @noRd -.fmExtractZn <- function(gr, label, ldSketch = NULL, cutoffs = NULL) { +.fmExtractZn <- function(gr, label, ldReference = NULL, cutoffs = NULL) { allDf <- .entryToSumstatDf(gr, require = c("SNP", "Z", "N"), label = label) # Filtered HERE rather than at the LD build: z, the LD matrix and the # allele frequencies are all keyed off `variantIds`, so narrowing the id # set at its source keeps them aligned by construction instead of by three # subsetting steps staying in step with one another. - keep <- .panelKeepMask(allDf$variant_id, ldSketch, cutoffs, label) + keep <- .panelKeepMask(allDf$variant_id, ldReference, cutoffs, label) df <- allDf[keep, , drop = FALSE] list( variantIds = df$variant_id, @@ -2417,6 +2525,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } + # ============================================================================= # Residualized genotype for one X window: the trait-derived cis block when # `rg` is NULL, else the explicit region. Shared by the univariate and PCA @@ -2429,7 +2538,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { samples, data, cisWindow, - residualizationArgs + residualizationParam ) { if (is.null(rg)) { .fmResidGeno( @@ -2438,7 +2547,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { traitId = traitId, cisWindow = cisWindow, samples = samples, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) } else { .fmResidGeno( @@ -2446,7 +2555,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { contexts = ctx, region = rg, samples = samples, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) } } @@ -2491,14 +2600,15 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { cfg$verbose, ctx, label, - cvFolds = cfg$crossValidationArgs$folds, - cvThreads = cfg$crossValidationArgs$numThreads, - samplePartition = cfg$crossValidationArgs$samplePartition, + cvFolds = cfg$crossValidationParam$folds, + cvThreads = cfg$crossValidationParam$numThreads, + samplePartition = cfg$crossValidationParam$samplePartition, af = afVec, credibleSetParam = cfg$credibleSetParam, fitRetention = cfg$fitRetention, seed = cfg$seed, - region = region + region = region, + fitFullData = cfg$crossValidationParam$fitFullData ) } @@ -2514,22 +2624,11 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { rownames(Y), data = cfg$data, cisWindow = cfg$cisWindow, - residualizationArgs = cfg$residualizationArgs + residualizationParam = cfg$residualizationParam ) - common <- intersect(rownames(allX), rownames(Y)) - if (length(common) < 2L) { - abort(glue( - "fineMappingPipeline: too few shared samples between ", - "residualized X and Y for (context='{ctx}', trait='{tid}')." - )) - } - X <- allX[common, , drop = FALSE] - yBlock <- Y[common, , drop = FALSE] - y <- if (ncol(yBlock) > 1L) { - yBlock[, 1L, drop = TRUE] - } else { - drop(yBlock) - } + xy <- .fmAlignXY(allX, Y, ctx, tid) + X <- xy$X + y <- xy$y if (!.fmSerScreen(X, y, cfg$screen)) { if (cfg$verbose >= 1) { inform(glue( @@ -2559,6 +2658,26 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } +# Align the residualized genotype block to Y's samples; `y` is Y's first +# column. Errors when fewer than two samples are shared. +# @noRd +.fmAlignXY <- function(allX, Y, ctx, tid) { + common <- intersect(rownames(allX), rownames(Y)) + if (length(common) < 2L) { + abort(glue( + "fineMappingPipeline: too few shared samples between ", + "residualized X and Y for (context='{ctx}', trait='{tid}')." + )) + } + yBlock <- Y[common, , drop = FALSE] + y <- if (ncol(yBlock) > 1L) { + yBlock[, 1L, drop = TRUE] + } else { + drop(yBlock) + } + list(X = allX[common, , drop = FALSE], y = y) +} + # All univariate row-records for one (context, trait): cache hits, then (when # tokens remain) per-window fits merged per token. # @noRd @@ -2587,7 +2706,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { contexts = ctx, traitId = tid, naAction = cfg$naAction, - residualizationArgs = cfg$residualizationArgs + residualizationParam = cfg$residualizationParam ) blockEntries <- map( cfg$xRegions, @@ -2614,7 +2733,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { samples, data = cfg$data, cisWindow = cfg$cisWindow, - residualizationArgs = cfg$residualizationArgs + residualizationParam = cfg$residualizationParam ) common <- intersect(rownames(X), names(pcY)) if (length(common) < 2L) { @@ -2689,7 +2808,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { contexts = ctx, traitId = traits, naAction = cfg$naAction, - residualizationArgs = cfg$residualizationArgs + residualizationParam = cfg$residualizationParam ) scores <- .fmTopPcScores(Yctx, cfg$nPCs) if (is.null(scores)) { @@ -2716,54 +2835,50 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { # QtlDataset method # ============================================================================= -# The settings every QtlDataset joint-phase function forwards to the shared -# joint dispatcher: the common fine-mapping record plus the scope and -# TWAS-weight fields only the joint engine uses. +# The record a QtlDataset run hands both its joint dispatch and its per-tuple +# dispatch, and that a MultiStudyQtlDataset run hands each individual-level +# component's joint dispatch. One builder, so a setting added for one path +# reaches the others: the per-study joint dispatch used to assemble its own +# record and lost six settings that way. `data` is the dataset after its +# per-call filter overrides; `screen` is the resolved signal screen. # @noRd -.fmQdsJointCfg <- function( - common, +.fmIndividualJointCfg <- function( + data, contexts, traitId, + cisWindow, + xRegions, + credibleSetParam, + crossValidationParam, + residualizationParam, + screen, + fineMappingResult, + fitRetention, + verbose, + seed, mrmashPrior, dataDrivenPriorWeightsCutoff ) { - c( - common, - list( - contexts = contexts, - traitId = traitId, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff - ) + cvCfg <- .cvResolve(crossValidationParam) + list( + data = data, + contexts = contexts, + traitId = traitId, + cisWindow = cisWindow, + xRegions = xRegions, + credibleSetParam = credibleSetParam, + crossValidationParam = cvCfg, + residualizationParam = residualizationParam, + screen = screen, + fineMappingResult = .fmCacheForRun(fineMappingResult, cvCfg), + fitRetention = fitRetention, + verbose = verbose, + seed = seed, + mrmashPrior = mrmashPrior, + dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff ) } -# Run the joint engine for a QtlDataset with the given spec + token set. Shared -# by the explicit-jointSpecification path and the auto-detected multivariate -# path; only the spec and token arguments differ between them. -# @noRd -.fmQdsJointDispatch <- function(jointSpec, tokens, methodArgs, cfg) { - .fmDispatchJointSpecsQtlDataset( - jointSpec, - cfg$data, - tokens, - cfg$contexts, - cfg$traitId, - cfg$cisWindow, - cfg$verbose, - methodArgs = methodArgs, - xRegions = cfg$xRegions, - mrmashPrior = cfg$mrmashPrior, - dataDrivenPriorWeightsCutoff = cfg$dataDrivenPriorWeightsCutoff, - crossValidationArgs = cfg$crossValidationArgs, - residualizationArgs = cfg$residualizationArgs, - pipCutoffToSkip = cfg$screen, - fineMappingResult = cfg$fineMappingResult, - credibleSetParam = cfg$credibleSetParam, - fitRetention = cfg$fitRetention, - seed = cfg$seed - ) -} # Resolve the signal screen, apply per-call filter overrides to a validated # copy of the dataset, and derive the X windows. Rejects the region + cisWindow @@ -2771,23 +2886,19 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { # @noRd .fmQdsResolveInputs <- function( data, - signalScreenArgs, + signalScreenParam, genotypeFilterParam, region, cisWindow, jointRegions ) { - .assertMethodParam(signalScreenArgs, "SignalScreenParam", "signalScreen") - screen <- .screenResolve(signalScreenArgs) + screen <- .fmResolveScreen(signalScreenParam) data <- .qtlApplyFilterOverrides(data, genotypeFilterParam) - if (!is.null(region) && !is.null(cisWindow)) { - msg <- glue( - "fineMappingPipeline(QtlDataset): specify either `region` or ", - "`cisWindow`, not both. `cisWindow` expands each trait's own ", - "coordinates, whereas `region` is the literal variant window." - ) - abort(msg) - } + .rejectRegionWithCisWindow( + region, + cisWindow, + "fineMappingPipeline(QtlDataset)" + ) list( data = data, screen = screen, @@ -2795,8 +2906,13 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } -# Normalize methods, capability-check them, then hand off to the joint phase. +# The signal screen a run applies, from its SignalScreenParam. # @noRd +.fmResolveScreen <- function(signalScreenParam) { + .assertMethodParam(signalScreenParam, "SignalScreenParam") + .screenResolve(signalScreenParam) +} + # The joint-specification phase for the QtlDataset path: dispatch whatever the # spec covers, narrow the tokens the joint fits consumed, and report whether # anything is left for the per-tuple loop. `done = TRUE` means the joint fits @@ -2816,7 +2932,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { methodArgs = norm$methodArgs )) } - jointResult <- .fmQdsJointDispatch( + jointResult <- .fmDispatchJointSpecsQtlDataset( parsedJointSpec, intersect(norm$tokens, c("mvsusie", "fsusie")), norm$methodArgs, @@ -2843,6 +2959,8 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } +# Normalize methods, capability-check them, then hand off to the joint phase. +# @noRd .fmQdsResolveTokens <- function( jointSpecification, methods, @@ -2873,14 +2991,19 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { list(tokens = remaining, methodArgs = methodArgs[remaining]) } -# Trait ids available in one context, intersected with the requested traitId or -# overlapping the requested region (mirrors twasWeightsPipeline). +# Trait ids available in one context: the ones `traitId` selects there, else +# those overlapping the requested region (mirrors twasWeightsPipeline). A +# `traitId` that leaves this context out keeps all of its traits -- region +# picks traits only when no traitId is given at all. # @noRd .fmQdsTraitsForContext <- function(ctx, data, traitId, region) { se <- molecularTraits(data, contexts = ctx) allIds <- rownames(se) ids <- if (!is.null(traitId)) { - intersect(allIds, traitId) + intersect( + allIds, + .selectedTraits(traitId, studyName(data), ctx) %||% allIds + ) } else if (!is.null(region)) { allIds[IRanges::overlapsAny( SummarizedExperiment::rowRanges(se), @@ -2892,26 +3015,13 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ids } -# Resolve the study, the contexts to use (validating any requested ones), and -# the per-context trait lists. Returns `p` extended with study / useCtx / -# perCtxTraits / nCtx / nTraits. +# Resolve the study, the contexts to use, and the per-context trait lists. +# `contexts` / `traitId` arrive parsed (parseContexts() / parseTraitIds()), +# which is where requested contexts were checked. Returns `p` extended with +# study / useCtx / perCtxTraits / nCtx / nTraits. # @noRd .fmQdsResolveContexts <- function(data, contexts, traitId, region) { - allCtx <- contexts(data) - useCtx <- if (is.null(contexts)) { - allCtx - } else { - bad <- setdiff(contexts, allCtx) - if (length(bad) > 0L) { - badStr <- str_flatten(bad, ", ") - msg <- glue( - "fineMappingPipeline(QtlDataset): unknown context(s): ", - "{badStr}" - ) - abort(msg) - } - contexts - } + useCtx <- .selectedContexts(contexts, studyName(data)) %||% contexts(data) perCtxTraits <- set_names( map( useCtx, @@ -2965,9 +3075,6 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } -# Univariate + usePCA dispatch: each (context, trait) -> merged-per-token -# row-records; each multi-trait context's top PCs -> pseudo-trait rows. -# @noRd # The per-run settings the univariate / PCA row builders all need: the common # fine-mapping record plus the grid and method fields only the per-tuple # dispatch uses. The chain threads a single record instead of two dozen @@ -3002,6 +3109,9 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) } +# Univariate + usePCA dispatch: each (context, trait) -> merged-per-token +# row-records; each multi-trait context's top PCs -> pseudo-trait rows. +# @noRd .fmQdsDispatchRows <- function(cfg) { univRows <- if (length(cfg$univTokens) > 0L) { list_flatten(map(cfg$useCtx, .fmUnivContextRows, cfg = cfg)) @@ -3032,7 +3142,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { if (length(mvTokens) == 0L && length(fsTokens) == 0L) { return(jointResult) } - autoJoint <- .fmQdsJointDispatch( + autoJoint <- .fmDispatchJointSpecsQtlDataset( .fmSynthesizeJointSpec(nCtx, nTraits), c(mvTokens, fsTokens), methodArgs, @@ -3043,7 +3153,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { } else if (is.null(autoJoint)) { jointResult } else { - .rbindFineMappingResult(jointResult, autoJoint, ldSketch = NULL) + .rbindFineMappingResult(jointResult, autoJoint, ldReference = NULL) } } @@ -3079,7 +3189,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { cisWindow, length(rows) ), - ldSketch = NULL + ldReference = NULL ) } else { NULL @@ -3097,7 +3207,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { if (is.null(perTupleResult)) { return(jointResult) } - .rbindFineMappingResult(perTupleResult, jointResult, ldSketch = NULL) + .rbindFineMappingResult(perTupleResult, jointResult, ldReference = NULL) } # Resolve the context/trait tuples, split the tokens across the univariate, @@ -3107,9 +3217,7 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { # to be read together. # @noRd .fmQdsRunAndAssemble <- function( - data, - commonCfg, - jointCfg, + cfg, contexts, traitId, region, @@ -3122,14 +3230,14 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { usePCA ) { ctxInfo <- .fmQdsResolveContexts( - data = data, + data = cfg$data, contexts = contexts, traitId = traitId, region = region ) split <- .fmQdsSplitTokens(tokens, ctxInfo$nCtx, ctxInfo$nTraits) rows <- .fmQdsDispatchRows(.fmQdsRunCfg( - commonCfg, + cfg, initializeWithSusieInf = initializeWithSusieInf, methodArgs = methodArgs, nPCs = nPCs, @@ -3141,12 +3249,12 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { usePCA = usePCA )) .fmQdsAssemble( - data, + cfg$data, rows, region = region, - cisWindow = commonCfg$cisWindow, + cisWindow = cfg$cisWindow, .fmQdsAutoJoint( - jointCfg, + cfg, mvTokens = split$mvTokens, fsTokens = split$fsTokens, jointResult = jointResult, @@ -3162,6 +3270,13 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { # on a captured environment. The susieInf chaining is applied downstream # inside .fmFitXBlock / .fmFitRssBlock (which recompute # .fmResolveSusieChain), so no chain config is threaded here. +# +# `genotypeFilterParam` is a per-call override; NULL uses the QtlDataset's +# construct-time slot value (applied lazily at extraction). The +# CrossValidationParam bundle is the user-facing form; below it the cfg +# plumbing (and the joint engine that reads it) keeps plain scalars. +# `cfg` (.fmIndividualJointCfg()) holds the settings the joint phase and the +# per-tuple dispatch share, built once so the two cannot disagree. # @noRd .fmPipelineQtlDataset <- function( data, @@ -3170,17 +3285,15 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { traitId = NULL, region = NULL, cisWindow = NULL, - # Per-call genotype-filter overrides; NULL = use the QtlDataset's - # construct-time slot value (applied lazily at extraction). genotypeFilterParam = GenotypeFilterParam(), jointRegions = FALSE, jointSpecification = NULL, initializeWithSusieInf = TRUE, credibleSetParam = CredibleSetParam(), fineMappingResult = NULL, - crossValidationArgs = CrossValidationParam(), - residualizationArgs = ResidualizationParam(), - signalScreenArgs = SignalScreenParam(), + crossValidationParam = CrossValidationParam(), + residualizationParam = ResidualizationParam(), + signalScreenParam = SignalScreenParam(), usePCA = FALSE, nPCs = 10L, seed = NULL, @@ -3193,44 +3306,33 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { ) { fitRetention <- arg_match(fitRetention) naAction <- arg_match(naAction, c("drop", "impute")) + contexts <- parseContexts(contexts, data) + traitId <- parseTraitIds(traitId, data) if (!is.null(seed)) { withr::local_seed(as.integer(seed)) } - # Each stage returns only the values it derives; nothing is grafted onto a - # captured environment. resolved <- .fmQdsResolveInputs( data = data, - signalScreenArgs = signalScreenArgs, + signalScreenParam = signalScreenParam, genotypeFilterParam = genotypeFilterParam, region = region, cisWindow = cisWindow, jointRegions = jointRegions ) - data <- resolved$data - screen <- resolved$screen - xRegions <- resolved$xRegions - # The bundle is the user-facing form; below this line the existing cfg - # plumbing (and the joint engine that reads it) keeps plain scalars. - cvCfg <- .cvResolve(crossValidationArgs) - # The settings the joint phase and the per-tuple dispatch share, named - # once so the two records built from it cannot disagree. - commonCfg <- list( - data = data, + cfg <- .fmIndividualJointCfg( + data = resolved$data, + contexts = contexts, + traitId = traitId, cisWindow = cisWindow, + xRegions = resolved$xRegions, credibleSetParam = credibleSetParam, - screen = screen, - xRegions = xRegions, - crossValidationArgs = cvCfg, - residualizationArgs = residualizationArgs, + crossValidationParam = crossValidationParam, + residualizationParam = residualizationParam, + screen = resolved$screen, fineMappingResult = fineMappingResult, fitRetention = fitRetention, verbose = verbose, - seed = seed - ) - jointCfg <- .fmQdsJointCfg( - commonCfg, - contexts = contexts, - traitId = traitId, + seed = seed, mrmashPrior = mrmashPrior, dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff ) @@ -3239,36 +3341,30 @@ combineFineMappingResults <- function(..., ldSketch = NULL) { methods, credibleSetParam$maxNumSingleEffects %||% 10L, credibleSetParam$maxNumSingleEffectsGreedy, - jointCfg + cfg ) - if (rt$done) { - return(.fmRecordProvenance( - rt$result, - dataType, - "individual", - .qtlScaleResiduals(data, residualizationArgs) - )) + out <- if (rt$done) { + rt$result + } else { + .fmQdsRunAndAssemble( + cfg, + contexts = contexts, + traitId = traitId, + region = region, + tokens = rt$tokens, + methodArgs = rt$methodArgs, + jointResult = rt$result, + initializeWithSusieInf = initializeWithSusieInf, + nPCs = nPCs, + naAction = naAction, + usePCA = usePCA + ) } - out <- .fmQdsRunAndAssemble( - data, - commonCfg, - jointCfg, - contexts = contexts, - traitId = traitId, - region = region, - tokens = rt$tokens, - methodArgs = rt$methodArgs, - jointResult = rt$result, - initializeWithSusieInf = initializeWithSusieInf, - nPCs = nPCs, - naAction = naAction, - usePCA = usePCA - ) .fmRecordProvenance( out, dataType, "individual", - .qtlScaleResiduals(data, residualizationArgs) + .qtlScaleResiduals(cfg$data, residualizationParam) ) } @@ -3291,9 +3387,9 @@ setMethod( # own slots do, and a collection whose studies disagree gets NA rather than a # guess. A sumstats-only collection is in standardized units regardless. # @noRd -.fmMsScaleResiduals <- function(data, residualizationArgs) { - if (!is.null(residualizationArgs$scaleResiduals)) { - return(residualizationArgs$scaleResiduals) +.fmMsScaleResiduals <- function(data, residualizationParam) { + if (!is.null(residualizationParam$scaleResiduals)) { + return(residualizationParam$scaleResiduals) } if (identical(.fmMsDataSource(data), "sumstat")) { return(TRUE) @@ -3326,6 +3422,13 @@ setMethod( if (length(m) == 0L) { return(NULL) } + # `cfg` carries the parsed selection for every study; this study gets its + # own share, and a study with none of its contexts selected sits out. + s <- studyName(qd) + contexts <- .selectedContexts(cfg$contexts, s) + if (!is.null(contexts) && length(contexts) == 0L) { + return(NULL) + } # Every setting is named. `cfg` also carries panelFilterParam and # susieRssParam, # which belong to the summary-statistics path: a study's own genotypes @@ -3337,8 +3440,8 @@ setMethod( data = qd, methods = m, jointSpecification = NULL, - contexts = cfg$contexts, - traitId = cfg$traitId, + contexts = contexts, + traitId = .studyTraitIdArg(cfg$traitId, s), region = cfg$region, cisWindow = cfg$cisWindow, jointRegions = cfg$jointRegions, @@ -3346,11 +3449,11 @@ setMethod( credibleSetParam = cfg$credibleSetParam, fineMappingResult = cfg$fineMappingResult, verbose = cfg$verbose, - crossValidationArgs = cfg$crossValidationArgs, - residualizationArgs = cfg$residualizationArgs, + crossValidationParam = cfg$crossValidationParam, + residualizationParam = cfg$residualizationParam, seed = cfg$seed, naAction = cfg$naAction, - signalScreenArgs = cfg$signalScreenArgs, + signalScreenParam = cfg$signalScreenParam, genotypeFilterParam = cfg$genotypeFilterParam, usePCA = cfg$usePCA, nPCs = cfg$nPCs, @@ -3369,17 +3472,24 @@ setMethod( if (length(m) == 0L) { return(NULL) } + # The parsed selection is applied here, to the rows, so the recursion runs + # on what it keeps with nothing left to filter -- and rows it leaves out + # are not an error when another component holds the selected tuples. + keepRows <- .sumStatsRowsSelected(ss, cfg$contexts, cfg$traitId) + if (!any(keepRows)) { + return(NULL) + } # Summary statistics carry no genotypes and no per-study region # selection: the region is already baked into the input, and there is # nothing to residualize, screen, cross-validate or PCA. `cfg` carries # those settings for the individual-level sibling and they are not # forwarded here. fineMappingPipeline( - data = ss, + data = if (all(keepRows)) ss else ss[which(keepRows)], methods = m, jointSpecification = NULL, - contexts = cfg$contexts, - traitId = cfg$traitId, + contexts = NULL, + traitId = NULL, initializeWithSusieInf = cfg$initializeWithSusieInf, credibleSetParam = cfg$credibleSetParam, fineMappingResult = cfg$fineMappingResult, @@ -3393,42 +3503,6 @@ setMethod( ) } -# Validate the naAction choice and the region + cisWindow combination, then -# derive the X windows. Mirrors .fmQdsResolveInputs, minus the filter -# overrides the components apply themselves. -# @noRd -.fmMsResolveInputs <- function(naAction, region, cisWindow, jointRegions) { - if (!is.null(region) && !is.null(cisWindow)) { - msg <- glue( - "fineMappingPipeline(MultiStudyQtlDataset): specify either ", - "`region` or `cisWindow`, not both." - ) - abort(msg) - } - list( - naAction = arg_match(naAction, c("drop", "impute")), - xRegions = .makeXRegions(region, jointRegions) - ) -} - -# Run the joint engine for a MultiStudyQtlDataset with the given spec + token -# set. -# @noRd -.fmMsJointDispatch <- function(jointSpec, tokens, methodArgs, cfg) { - .fmDispatchJointSpecsMultiStudy( - jointSpec, - cfg$data, - tokens, - cfg$contexts, - cfg$traitId, - cfg$cisWindow, - cfg$verbose, - methodArgs = methodArgs, - xRegions = cfg$xRegions, - mrmashPrior = cfg$mrmashPrior, - dataDrivenPriorWeightsCutoff = cfg$dataDrivenPriorWeightsCutoff - ) -} # The joint-specification phase for the MultiStudyQtlDataset path: dispatch # whatever the spec covers, narrow the tokens the joint fits consumed, and @@ -3447,7 +3521,7 @@ setMethod( methodArgs = norm$methodArgs )) } - jointResult <- .fmMsJointDispatch( + jointResult <- .fmDispatchJointSpecsMultiStudy( parsedJointSpec, intersect(norm$tokens, c("mvsusie", "fsusie")), norm$methodArgs, @@ -3530,6 +3604,13 @@ setMethod( # MultiStudyQtlDataset fine-mapping worker. After resolving the explicit # joint spec it routes each remaining method to the components it supports, # via the shared multi-study driver. +# +# The joint rows' provenance is recorded here; each per-study recursion +# records its own, so both sides of the driver's rbind carry the same columns. +# A sumstats-only collection is in standardized units whatever the +# residualization hatch says; otherwise the hatch decides. `jointCfg` is one +# record of the settings the joint phase and the shared dispatcher both need, +# so each names them once. # @noRd .fmPipelineMultiStudy <- function( data, @@ -3547,9 +3628,9 @@ setMethod( fineMappingResult = NULL, mrmashPrior = NULL, dataDrivenPriorWeightsCutoff = 1e-10, - crossValidationArgs = CrossValidationParam(), - residualizationArgs = ResidualizationParam(), - signalScreenArgs = SignalScreenParam(), + crossValidationParam = CrossValidationParam(), + residualizationParam = ResidualizationParam(), + signalScreenParam = SignalScreenParam(), susieRssParam = SusieRssParam(), usePCA = FALSE, nPCs = 10L, @@ -3560,23 +3641,44 @@ setMethod( dataType = NULL ) { fitRetention <- arg_match(fitRetention) - # The joint rows are recorded here; each per-study recursion records its - # own, so both sides of the driver's rbind carry the same columns. - inputs <- .fmMsResolveInputs(naAction, region, cisWindow, jointRegions) + inputs <- .msResolveInputs( + naAction, + region, + cisWindow, + jointRegions, + "fineMappingPipeline(MultiStudyQtlDataset)" + ) naAction <- inputs$naAction - # One record of the settings the joint phase and the shared dispatcher - # both need, so each names them once. - jointCfg <- list( - data = data, - contexts = contexts, - traitId = traitId, + if (!is.null(seed)) { + withr::local_seed(as.integer(seed)) + } + cfg <- list( + methods = methods, + contexts = parseContexts(contexts, data), + traitId = parseTraitIds(traitId, data), + region = region, cisWindow = cisWindow, + jointRegions = jointRegions, + initializeWithSusieInf = initializeWithSusieInf, credibleSetParam = credibleSetParam, + fineMappingResult = fineMappingResult, verbose = verbose, - xRegions = inputs$xRegions, + crossValidationParam = crossValidationParam, + residualizationParam = residualizationParam, + seed = seed, + naAction = naAction, + signalScreenParam = signalScreenParam, + genotypeFilterParam = genotypeFilterParam, + panelFilterParam = panelFilterParam, + susieRssParam = susieRssParam, + usePCA = usePCA, + nPCs = nPCs, + fitRetention = fitRetention, mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff + dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, + dataType = dataType ) + jointCfg <- .fmMsJointCfg(cfg, data, inputs$xRegions) rt <- .fmMsResolveTokens( jointSpecification, methods, @@ -3588,51 +3690,54 @@ setMethod( rt$result, dataType, .fmMsDataSource(data), - # A sumstats-only collection is in standardized units whatever the - # hatch says; otherwise the hatch decides. - .fmMsScaleResiduals(data, residualizationArgs) + .fmMsScaleResiduals(data, residualizationParam) ) if (rt$done) { return(jointResult) } - methods <- rt$methods .multiStudyPipelineDriver( data, jointResult, .fmPerStudy, .fmSumStats, - list( - methods = methods, - contexts = contexts, - traitId = traitId, - region = region, - cisWindow = cisWindow, - jointRegions = jointRegions, - initializeWithSusieInf = initializeWithSusieInf, - credibleSetParam = credibleSetParam, - fineMappingResult = fineMappingResult, - verbose = verbose, - crossValidationArgs = crossValidationArgs, - residualizationArgs = residualizationArgs, - seed = seed, - naAction = naAction, - signalScreenArgs = signalScreenArgs, - genotypeFilterParam = genotypeFilterParam, - panelFilterParam = panelFilterParam, - susieRssParam = susieRssParam, - usePCA = usePCA, - nPCs = nPCs, - fitRetention = fitRetention, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - dataType = dataType - ), + list_assign(cfg, methods = rt$methods), .rbindFineMappingResult, QtlFineMappingResult, "fineMappingPipeline" ) } +# The MultiStudyQtlDataset joint record, derived from the record the +# per-component recursion receives (`cfg`): the individual-level joint record +# its components' own QtlDataset runs build, plus the two filters the +# components apply. Deriving it, rather than re-listing the settings, is what +# keeps the per-study joint fits on exactly the settings of the per-study +# fits. +# @noRd +.fmMsJointCfg <- function(cfg, data, xRegions) { + list_assign( + .fmIndividualJointCfg( + data = data, + contexts = cfg$contexts, + traitId = cfg$traitId, + cisWindow = cfg$cisWindow, + xRegions = xRegions, + credibleSetParam = cfg$credibleSetParam, + crossValidationParam = cfg$crossValidationParam, + residualizationParam = cfg$residualizationParam, + screen = .fmResolveScreen(cfg$signalScreenParam), + fineMappingResult = cfg$fineMappingResult, + fitRetention = cfg$fitRetention, + verbose = cfg$verbose, + seed = cfg$seed, + mrmashPrior = cfg$mrmashPrior, + dataDrivenPriorWeightsCutoff = cfg$dataDrivenPriorWeightsCutoff + ), + genotypeFilterParam = cfg$genotypeFilterParam, + panelFilterParam = cfg$panelFilterParam + ) +} + #' @rdname fineMappingPipeline #' @export setMethod( @@ -3667,27 +3772,6 @@ setMethod( ) } -# Run the joint engine for a QtlSumStats with the given spec + token set. -# Shared by the explicit-jointSpecification path and the auto-detected -# cross-context path; only the spec and token arguments differ between them. -# @noRd -.fmQssJointDispatch <- function(jointSpec, tokens, methodArgs, cfg) { - .fmDispatchJointSpecsQtlSumStats( - jointSpec, - cfg$data, - tokens, - cfg$contexts, - cfg$traitId, - cfg$verbose, - methodArgs = methodArgs, - mrmashPrior = cfg$mrmashPrior, - dataDrivenPriorWeightsCutoff = cfg$dataDrivenPriorWeightsCutoff, - fineMappingResult = cfg$fineMappingResult, - credibleSetParam = cfg$credibleSetParam, - fitRetention = cfg$fitRetention, - panelFilterParam = cfg$panelFilterParam %||% PanelFilterParam() - ) -} # The joint-specification phase for the QtlSumStats path: dispatch whatever # the spec covers, narrow the tokens the joint fits consumed, and report @@ -3706,7 +3790,7 @@ setMethod( methodArgs = norm$methodArgs )) } - jointResult <- .fmQssJointDispatch( + jointResult <- .fmDispatchJointSpecsQtlSumStats( parsedJointSpec, intersect(norm$tokens, "mvsusie"), norm$methodArgs, @@ -3739,23 +3823,6 @@ setMethod( # input is individual-level or summary statistics, so each constructor checks # against BOTH, taken live from the capability table rather than transcribed. -# The engine(s) a fine-mapping token forwards to, from the capability table. -# @noRd -.fmMethodCallees <- function(token) { - info <- .fineMappingMethodCapabilities[[token]] - unique(compact(list(info$individualImpl, info$sumstatImpl))) -} - -# Shared body for the per-engine constructors: pecotmr keeps no defaults of -# its own here (they are seeded per run from `L` / `Lgreedy`), so everything -# the caller gives is checked against that ONE engine and carried through. -# -# One constructor per engine, not per token. A method whose two input paths -# run different engines gets one for each: checking the union instead would -# accept a susie_rss-only name on an individual-level run and then drop it, -# and mvsusie could not be checked at all, since mvsusie_rss takes `...` and -# a union containing it accepts anything. -# @noRd # Engine arguments pecotmr supplies itself, per engine, with where the caller # sets each one instead. Keyed on the engine rather than the token because one # engine serves several tokens, and derived from what the wrappers actually @@ -3764,6 +3831,20 @@ setMethod( # caller's `SusieOptions(L = 3)` is a valid susie formal that pecotmr then # overwrites (or, on the mvsusie path, that silently overwrites pecotmr's own # X / Y). +# +# Per-engine notes: +# - mvsusie: `residual_variance` is deliberately NOT owned, though the +# pipeline does pass it: the prior's V can be NULL, compact() then drops it +# from the base, and a user value is the only source left. Refusing it would +# close the only path -- the QggOptions(nit=) mistake. +# - mvsusie_rss: `L` does not reach it, but `coverage` does: the pipeline +# threads csCoverage() into fitMvsusieRss()'s own `coverage` formal, so this +# engine takes that one entry of csHome and not the rest. +# - fsusie: only what the pipeline ACTUALLY supplies. `prior`, `max_scale` and +# `max_SNP_EM` used to be listed because this row described the retired +# fsusieWrapper(), which passed them; the pipeline calls fitFsusie() and +# never did, so refusing them closed the only path to setting them at all -- +# the same trap as `residual_variance` above. # @noRd .fmOwnedEngineArgs <- function(callee) { fromData <- "supplied from the data by the pipeline" @@ -3789,10 +3870,6 @@ setMethod( set_names(rep(fromData, 2L), c("z", "n")), csHome["coverage"] ), - # `residual_variance` is deliberately NOT owned, though the pipeline - # does pass it: the prior's V can be NULL, compact() then drops it - # from the base, and a user value is the only source left. Refusing - # it would close the only path -- the QggOptions(nit=) mistake. "mvsusieR::mvsusie" = c( set_names( rep(fromData, 3L), @@ -3800,9 +3877,6 @@ setMethod( ), csHome ), - # `L` does not reach mvsusie_rss, but `coverage` does: the pipeline - # threads csCoverage() into fitMvsusieRss()'s own `coverage` formal, - # so this engine takes that one entry of csHome and not the rest. "mvsusieR::mvsusie_rss" = c( set_names( rep(fromData, 4L), @@ -3810,12 +3884,6 @@ setMethod( ), csHome["coverage"] ), - # Only what the pipeline ACTUALLY supplies. `prior`, `max_scale` and - # `max_SNP_EM` used to be listed here because this row described the - # retired fsusieWrapper(), which passed them; the pipeline calls - # fitFsusie() and never did, so refusing them closed the only path to - # setting them at all -- the same trap the `residual_variance` note - # above records. "fsusieR::susiF" = c( set_names(rep(fromData, 3L), c("X", "Y", "pos")), c( @@ -3828,6 +3896,16 @@ setMethod( ) } +# Shared body for the per-engine constructors: pecotmr keeps no defaults of +# its own here (they are seeded per run from `L` / `Lgreedy`), so everything +# the caller gives is checked against that ONE engine and carried through. +# +# One constructor per engine, not per token. A method whose two input paths +# run different engines gets one for each: checking the union instead would +# accept a susie_rss-only name on an individual-level run and then drop it, +# and mvsusie could not be checked at all, since mvsusie_rss takes `...` and +# a union containing it accepts anything. +# @noRd .fmMethodOptions <- function(callee, label, engine, extra) { .configRefuseOwned(extra, .fmOwnedEngineArgs(callee), label) .newMethodOptions( @@ -3839,11 +3917,6 @@ setMethod( ) } -# The union constructor behind a token, for splicing a PLAIN LIST in the -# aggregator. A plain list declares no engine and the run's input class is -# not known yet, so it must stay acceptable for either path; the per-input -# check in .fmCheckMethodArgsForInput narrows it once the class is known. -# @noRd #' @rdname FineMappingMethodsParam #' @aliases FineMappingMethodsParam-class #' @exportClass FineMappingMethodsParam @@ -3927,46 +4000,6 @@ FineMappingMethodsParam <- function( !identical(inputKind, "GwasSumStats") || isTRUE(info$gwasAllowed) } -# One method's Options constructor for ONE input path, validating against -# just that path's callee. -# -# Deliberately not the union of both callees: validating against the union -# would accept a name valid on only one of them -- `susie` takes 41 -# arguments on individual data and 49 on summary statistics, sharing 37, so -# the union admits 16 names that are wrong for whichever path actually runs. -# Once the caller knows its input class there is no reason to be that -# lenient. -# @noRd -.fmTokenPathConfig <- function(token, inputKind) { - force(token) - force(inputKind) - callee <- .fmMethodCalleeFor(token, inputKind) - if (is.null(callee)) { - return(NULL) - } - function(...) { - .newMethodOptions( - callee, - defaults = list(), - extra = list(...), - label = glue("method '{token}' on {inputKind}"), - engine = token - ) - } -} - -# Every method's constructor for one input path, omitting the methods that -# do not run on it -- `fsusie` has no summary-statistics implementation, -# `ser` no individual one. -# @noRd -.fmMethodCtorsFor <- function(inputKind) { - toks <- names(.fineMappingMethodCapabilities) - compact(set_names( - map(toks, .fmTokenPathConfig, inputKind = inputKind), - toks - )) -} - #' @title Arguments For A Fine-Mapping Engine #' @description Options forwarded to one fine-mapping engine, checked #' against that engine's live formals. @@ -4051,16 +4084,7 @@ NULL studyCol <- as.character(data$study) contextCol <- as.character(data$context) traitCol <- as.character(data$trait) - byContext <- if (is.null(contexts)) { - seq_len(nrow(data)) - } else { - which(is_in(contextCol, contexts)) - } - selRows <- if (is.null(traitId)) { - byContext - } else { - byContext[is_in(traitCol[byContext], traitId)] - } + selRows <- which(.sumStatsRowsSelected(data, contexts, traitId)) if (length(selRows) == 0L) { msg <- glue( "fineMappingPipeline(QtlSumStats): no entries matched the ", @@ -4109,11 +4133,17 @@ NULL # joint) for mvsusie WITHOUT an explicit jointSpecification, merged with any # explicit-spec result the joint phase already produced. # @noRd -.fmQssAutoJoint <- function(cfg, mvTokens, methodArgs, jointResult, ldSketch) { +.fmQssAutoJoint <- function( + cfg, + mvTokens, + methodArgs, + jointResult, + ldReference +) { if (length(mvTokens) == 0L) { return(jointResult) } - autoJoint <- .fmQssJointDispatch( + autoJoint <- .fmDispatchJointSpecsQtlSumStats( list(list(axes = "context", scope = NULL)), mvTokens, methodArgs, @@ -4124,7 +4154,11 @@ NULL } else if (is.null(autoJoint)) { jointResult } else { - .rbindFineMappingResult(jointResult, autoJoint, ldSketch = ldSketch) + .rbindFineMappingResult( + jointResult, + autoJoint, + ldReference = ldReference + ) } } @@ -4135,37 +4169,12 @@ NULL # @noRd .fmQssAssemble <- function( data, - ldSketch, + ldReference, rows, nSkipped, jointResult ) { - rowContext <- map_chr(rows, "context") - rowTrait <- map_chr(rows, "trait") - perTupleResult <- if (length(rows) > 0L) { - .fmBuildQtlResult( - map_chr(rows, "study"), - rowContext, - rowTrait, - map_chr(rows, "method"), - map(rows, "entry"), - traitPos = try_fetch( - .anchorVector(data, rowContext, rowTrait, "traitPos"), - error = function(cnd) NULL - ), - region = .fmRowWindows( - data, - rowContext, - rowTrait, - NULL, - NULL, - length(rows) - ), - ldSketch = ldSketch - ) - } else { - NULL - } + perTupleResult <- .fmQssPerTupleResult(data, rows, ldReference) if (is.null(jointResult)) { if (!is.null(perTupleResult)) { return(perTupleResult) @@ -4177,7 +4186,7 @@ NULL character(0), character(0), list(), - ldSketch = ldSketch, + ldReference = ldReference, allowEmpty = TRUE )) } @@ -4188,7 +4197,43 @@ NULL if (is.null(perTupleResult)) { return(jointResult) } - .rbindFineMappingResult(perTupleResult, jointResult, ldSketch = ldSketch) + .rbindFineMappingResult( + perTupleResult, + jointResult, + ldReference = ldReference + ) +} + +# The per-tuple QtlFineMappingResult from the collected row-records, or NULL +# when there are none. Each row's region is its entry's variant span (no +# cis-window). +# @noRd +.fmQssPerTupleResult <- function(data, rows, ldReference) { + if (length(rows) == 0L) { + return(NULL) + } + rowContext <- map_chr(rows, "context") + rowTrait <- map_chr(rows, "trait") + .fmBuildQtlResult( + map_chr(rows, "study"), + rowContext, + rowTrait, + map_chr(rows, "method"), + map(rows, "entry"), + traitPos = try_fetch( + .anchorVector(data, rowContext, rowTrait, "traitPos"), + error = function(cnd) NULL + ), + region = .fmRowWindows( + data, + rowContext, + rowTrait, + NULL, + NULL, + length(rows) + ), + ldReference = ldReference + ) } # The settings the joint phase and the RSS row builders share, named once @@ -4248,6 +4293,8 @@ NULL dataType = NULL ) { .fmAssertQcd(data) + contexts <- parseContexts(contexts, data) + traitId <- parseTraitIds(traitId, data) cfgs <- .fmQssConfigs( data = data, credibleSetParam = credibleSetParam, @@ -4260,23 +4307,17 @@ NULL mrmashPrior = mrmashPrior, dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff ) - commonCfg <- cfgs$common - jointCfg <- cfgs$joint rt <- .fmQssResolveTokens( jointSpecification, methods, credibleSetParam$maxNumSingleEffects %||% 10L, credibleSetParam$maxNumSingleEffectsGreedy, - jointCfg + cfgs$joint ) - if (rt$done) { - return(.fmRecordProvenance(rt$result, dataType, "sumstat", TRUE)) - } out <- .fmQssAfterJoint( data, rt = rt, - commonCfg = commonCfg, - jointCfg = jointCfg, + cfgs = cfgs, contexts = contexts, traitId = traitId, initializeWithSusieInf = initializeWithSusieInf, @@ -4288,34 +4329,38 @@ NULL # Everything after the joint phase has had its say: select the rows the # univariate tokens run on, resolve the LD-finiteness setting against the # sketch, and fit. `rt` is .fmQssResolveTokens()'s record -- it carries the -# remaining tokens, their options, and whatever the joint phase produced. +# remaining tokens, their options, and whatever the joint phase produced; when +# the joint phase consumed every method (`rt$done`) its result is returned +# as is. `cfgs` is .fmQssConfigs()'s (common, joint) pair. # @noRd .fmQssAfterJoint <- function( data, rt, - commonCfg, - jointCfg, + cfgs, contexts, traitId, initializeWithSusieInf, susieRssParam ) { + if (rt$done) { + return(rt$result) + } sel <- .fmQssSelectRows(data = data, contexts = contexts, traitId = traitId) - ldSketch <- ldSketch(data) + ldReference <- ldReference(data) cfg <- .fmRssRunCfg( - commonCfg, - ldSketch = ldSketch, + cfgs$common, + ldReference = ldReference, initializeWithSusieInf = initializeWithSusieInf, methodArgs = rt$methodArgs, - susieRssParam = .fmRssArgsResolved(susieRssParam, ldSketch) + susieRssParam = .fmRssArgsResolved(susieRssParam, ldReference) ) .fmQssRunRows( data, sel = sel, split = .fmQssSplitTokens(rt$tokens, sel), cfg = cfg, - jointCfg = jointCfg, - ldSketch = ldSketch, + jointCfg = cfgs$joint, + ldReference = ldReference, methodArgs = rt$methodArgs, jointResult = rt$result ) @@ -4332,7 +4377,7 @@ NULL split, cfg, jointCfg, - ldSketch, + ldReference, methodArgs, jointResult ) { @@ -4351,7 +4396,7 @@ NULL } .fmQssAssemble( data = data, - ldSketch = ldSketch, + ldReference = ldReference, rows = list_flatten(map(univOut, "rows")), nSkipped = sum(map_lgl(univOut, "skipped")), jointResult = .fmQssAutoJoint( @@ -4359,7 +4404,7 @@ NULL mvTokens = split$mvTokens, methodArgs = methodArgs, jointResult = jointResult, - ldSketch = ldSketch + ldReference = ldReference ) ) } @@ -4384,13 +4429,13 @@ setMethod( fitRetention = c("slim", "full"), susieRssParam = SusieRssParam(), panelFilterParam = PanelFilterParam(), - crossValidationArgs = CrossValidationParam(), - residualizationArgs = ResidualizationParam(), + crossValidationParam = CrossValidationParam(), + residualizationParam = ResidualizationParam(), dataType = NULL ) { fitRetention <- arg_match(fitRetention) .cvRefuseOnSumstats( - crossValidationArgs, + crossValidationParam, "fineMappingPipeline", "QtlSumStats" ) @@ -4418,22 +4463,17 @@ setMethod( # GwasSumStats method # ============================================================================= -# Default the finite-sample LD reference size when an EB / SER-fallback LD- -# mismatch mode is active (serFallback on, or rMismatch other than "none") and -# rFinite is unset: use the LD-panel sample size (the notebook's `B`). Shared by -# the QtlSumStats and GwasSumStats workers. -# @noRd # The resolved record, still a SusieRssParam. Previously the resolved value was # carried alongside the record as a second cfg field and spliced in with # list_assign(as.list(...)) at the point of use, which handed everything # downstream a bare list exactly where the value became concrete. # @noRd -.fmRssArgsResolved <- function(susieRssParam, ldSketch) { +.fmRssArgsResolved <- function(susieRssParam, ldReference) { resolved <- .fmResolveRFinite( susieRssParam$rFinite, susieRssParam$serFallback, susieRssParam$rMismatch, - ldSketch + ldReference ) if (identical(resolved, susieRssParam$rFinite)) { return(susieRssParam) @@ -4441,15 +4481,29 @@ setMethod( .rssParamWithRFinite(susieRssParam, resolved) } -.fmResolveRFinite <- function(rFinite, serFallback, rMismatch, ldSketch) { +# Default the finite-sample LD reference size when an EB / SER-fallback LD- +# mismatch mode is active (serFallback on, or rMismatch other than "none") and +# rFinite is unset: use the LD-panel sample size (the notebook's `B`), or the +# size an LdMatrix records. Shared by the QtlSumStats and GwasSumStats workers. +# @noRd +.fmResolveRFinite <- function(rFinite, serFallback, rMismatch, ldReference) { if ( - is.null(rFinite) && - (isTRUE(serFallback) || !identical(rMismatch, "none")) + !is.null(rFinite) || + (!isTRUE(serFallback) && identical(rMismatch, "none")) ) { - .ldSketchNSamples(ldSketch) - } else { - rFinite + return(rFinite) } + n <- .ldRefNSamples(ldReference) + if (is.na(n)) { + abort(c( + str_c( + "The finite-reference LD mismatch model needs the LD ", + "reference's size." + ), + i = "Set `rFinite`, or record it with LdMatrix(nSamples = )." + )) + } + n } # GwasSumStats fine-mapping worker. The resolved tokens, LD sketch and @@ -4479,11 +4533,11 @@ setMethod( ) .fmCheckMethodCapabilities(norm$tokens, "GwasSumStats") .fmCheckMethodArgsForInput(norm$methodArgs, "GwasSumStats") - ldSketch <- ldSketch(data) + ldReference <- ldReference(data) # Derived values are ordinary locals now, not fields grafted onto a bundle. tokens <- norm$tokens methodArgs <- norm$methodArgs - susieRssParam <- .fmRssArgsResolved(susieRssParam, ldSketch) + susieRssParam <- .fmRssArgsResolved(susieRssParam, ldReference) studyCol <- as.character(data$study) commonCfg <- .fmSsCommonCfg( data = data, @@ -4498,7 +4552,7 @@ setMethod( studyCol = studyCol, tokens = tokens, commonCfg = commonCfg, - ldSketch = ldSketch, + ldReference = ldReference, initializeWithSusieInf = initializeWithSusieInf, methodArgs = methodArgs, susieRssParam = susieRssParam @@ -4513,7 +4567,7 @@ setMethod( studyCol, tokens, commonCfg, - ldSketch, + ldReference, initializeWithSusieInf, methodArgs, susieRssParam @@ -4525,7 +4579,7 @@ setMethod( tokens = tokens, cfg = .fmRssRunCfg( commonCfg, - ldSketch = ldSketch, + ldReference = ldReference, initializeWithSusieInf = initializeWithSusieInf, methodArgs = methodArgs, susieRssParam = susieRssParam @@ -4544,7 +4598,7 @@ setMethod( # the same window .fmRssFitWindow() gives the per-row grange columns, # so the two cannot disagree. region = .fmGwasRowWindows(rows), - ldSketch = ldSketch, + ldReference = ldReference, allowEmpty = (nSkipped > 0L || nrow(data) == 0L) ) } @@ -4564,13 +4618,13 @@ setMethod( fitRetention = c("slim", "full"), susieRssParam = SusieRssParam(), panelFilterParam = PanelFilterParam(), - crossValidationArgs = CrossValidationParam(), - residualizationArgs = ResidualizationParam(), + crossValidationParam = CrossValidationParam(), + residualizationParam = ResidualizationParam(), dataType = NULL ) { fitRetention <- arg_match(fitRetention) .cvRefuseOnSumstats( - crossValidationArgs, + crossValidationParam, "fineMappingPipeline", "GwasSumStats" ) @@ -4754,93 +4808,6 @@ setMethod( # see colocPipeline(), crossValidation() and fineMappingPipeline(). # ============================================================================= -# --- CredibleSetParam accessors -------------------------------------------- -# Setters revalidate: slot assignment checks the declared type on its own but -# does not re-run a validity method, and this package does write those (see -# SignalScreenParam). - -#' @rdname CredibleSetParam -setMethod("csCoverage", "CredibleSetParam", function(x) x@coverage) - -#' @rdname CredibleSetParam -setReplaceMethod("csCoverage", "CredibleSetParam", function(x, value) { - x@coverage <- value - validObject(x) - x -}) - -#' @rdname CredibleSetParam -setMethod("secondaryCoverage", "CredibleSetParam", function(x) { - x@secondaryCoverage -}) - -#' @rdname CredibleSetParam -setReplaceMethod("secondaryCoverage", "CredibleSetParam", function(x, value) { - x@secondaryCoverage <- value - validObject(x) - x -}) - -#' @rdname CredibleSetParam -setMethod("signalCutoff", "CredibleSetParam", function(x) x@signalCutoff) - -#' @rdname CredibleSetParam -setReplaceMethod("signalCutoff", "CredibleSetParam", function(x, value) { - x@signalCutoff <- value - validObject(x) - x -}) - -#' @rdname CredibleSetParam -setMethod("minAbsCorr", "CredibleSetParam", function(x) x@minAbsCorr) - -#' @rdname CredibleSetParam -setReplaceMethod("minAbsCorr", "CredibleSetParam", function(x, value) { - x@minAbsCorr <- value - validObject(x) - x -}) - -#' @rdname CredibleSetParam -setMethod("medianAbsCorr", "CredibleSetParam", function(x) x@medianAbsCorr) - -#' @rdname CredibleSetParam -setReplaceMethod("medianAbsCorr", "CredibleSetParam", function(x, value) { - x@medianAbsCorr <- value - validObject(x) - x -}) - -#' @rdname CredibleSetParam -setMethod("includeAllCs", "CredibleSetParam", function(x) x@includeAllCs) - -#' @rdname CredibleSetParam -setReplaceMethod("includeAllCs", "CredibleSetParam", function(x, value) { - x@includeAllCs <- value - validObject(x) - x -}) - -#' @rdname CredibleSetParam -setMethod("perCsColumns", "CredibleSetParam", function(x) x@perCsColumns) - -#' @rdname CredibleSetParam -setReplaceMethod("perCsColumns", "CredibleSetParam", function(x, value) { - x@perCsColumns <- value - validObject(x) - x -}) - -#' @rdname CredibleSetParam -setMethod("maxNumSingleEffects", "CredibleSetParam", function(x) { - x@maxNumSingleEffects -}) - -#' @rdname CredibleSetParam -setMethod("maxNumSingleEffectsGreedy", "CredibleSetParam", function(x) { - x@maxNumSingleEffectsGreedy -}) - #' @rdname SignalScreenParam #' @aliases SignalScreenParam-class #' @exportClass SignalScreenParam @@ -5131,6 +5098,93 @@ CredibleSetParam <- function( ) } +# --- CredibleSetParam accessors -------------------------------------------- +# Setters revalidate: slot assignment checks the declared type on its own but +# does not re-run a validity method, and this package does write those (see +# SignalScreenParam). + +#' @rdname CredibleSetParam +setMethod("csCoverage", "CredibleSetParam", function(x) x@coverage) + +#' @rdname CredibleSetParam +setReplaceMethod("csCoverage", "CredibleSetParam", function(x, value) { + x@coverage <- value + validObject(x) + x +}) + +#' @rdname CredibleSetParam +setMethod("secondaryCoverage", "CredibleSetParam", function(x) { + x@secondaryCoverage +}) + +#' @rdname CredibleSetParam +setReplaceMethod("secondaryCoverage", "CredibleSetParam", function(x, value) { + x@secondaryCoverage <- value + validObject(x) + x +}) + +#' @rdname CredibleSetParam +setMethod("signalCutoff", "CredibleSetParam", function(x) x@signalCutoff) + +#' @rdname CredibleSetParam +setReplaceMethod("signalCutoff", "CredibleSetParam", function(x, value) { + x@signalCutoff <- value + validObject(x) + x +}) + +#' @rdname CredibleSetParam +setMethod("minAbsCorr", "CredibleSetParam", function(x) x@minAbsCorr) + +#' @rdname CredibleSetParam +setReplaceMethod("minAbsCorr", "CredibleSetParam", function(x, value) { + x@minAbsCorr <- value + validObject(x) + x +}) + +#' @rdname CredibleSetParam +setMethod("medianAbsCorr", "CredibleSetParam", function(x) x@medianAbsCorr) + +#' @rdname CredibleSetParam +setReplaceMethod("medianAbsCorr", "CredibleSetParam", function(x, value) { + x@medianAbsCorr <- value + validObject(x) + x +}) + +#' @rdname CredibleSetParam +setMethod("includeAllCs", "CredibleSetParam", function(x) x@includeAllCs) + +#' @rdname CredibleSetParam +setReplaceMethod("includeAllCs", "CredibleSetParam", function(x, value) { + x@includeAllCs <- value + validObject(x) + x +}) + +#' @rdname CredibleSetParam +setMethod("perCsColumns", "CredibleSetParam", function(x) x@perCsColumns) + +#' @rdname CredibleSetParam +setReplaceMethod("perCsColumns", "CredibleSetParam", function(x, value) { + x@perCsColumns <- value + validObject(x) + x +}) + +#' @rdname CredibleSetParam +setMethod("maxNumSingleEffects", "CredibleSetParam", function(x) { + x@maxNumSingleEffects +}) + +#' @rdname CredibleSetParam +setMethod("maxNumSingleEffectsGreedy", "CredibleSetParam", function(x) { + x@maxNumSingleEffectsGreedy +}) + #' @rdname SusieRssParam #' @aliases SusieRssParam-class #' @exportClass SusieRssParam diff --git a/R/fineMappingRow.R b/R/fineMappingRow.R index e70c541b6..793f7681f 100644 --- a/R/fineMappingRow.R +++ b/R/fineMappingRow.R @@ -2,10 +2,10 @@ # FineMappingRow S4 class # ----------------------------------------------------------------------------- # One row's fine-mapping payload: the variants (with topLoci as their metadata -# columns) plus the stored fit. This is what a FineMappingResult collection is +# columns) plus the stored fit. This is what a fine-mapping result collection is # built FROM and what its per-row views are computed OVER. # -# It replaces the retired FineMappingRow, but is a different object in the +# It replaces the retired FineMappingEntry, but is a different object in the # two ways that made that class expensive: # # * It is NOT a dispatch target. The per-row views (.fmrRowTopLoci, .fmrRowCs, @@ -123,6 +123,20 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) { obj } +# The row a cross-validation-only fit leaves (CrossValidationParam(fitFullData = +# FALSE)): no variants, no fit, no top loci -- only the cross-validation result +# attached afterwards. The writers refuse such rows (.fmbIncompleteRows()), and +# a resume cache only reuses them for another cross-validation-only run. +# @noRd +.fmCvOnlyRow <- function(cvResult = NULL) { + fineMappingRow( + variantIds = character(0), + susieFit = NULL, + topLoci = tibble(), + cvResult = cvResult + ) +} + # Attach topLoci's payload columns to the variant GRanges, keeping the # identity columns `.variantIdsToGRanges()` already put in mcols. Replacing # mcols wholesale would drop those, and the variant ids could no longer be @@ -162,40 +176,6 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) { } -# ---- FineMappingRow validity helpers ------------------------------------- - -# Full validity check: collect all contract violations (empty vector = valid). -# @noRd - -# cvResult must be NULL or a list. -# @noRd - -# topLoci contract checks (only meaningful when topLoci has rows). -# @noRd - -# Minimal column contract: variant_id + pip. Canonical projector columns -# (marginal_*, posterior_*, ...) are pipeline-populated; skeletal entries may -# omit them (accessor projections then return NA-filled cols). -# @noRd - -# topLoci row count must match variantIds length. -# @noRd - -# topLoci$variant_id must equal variantIds in order. -# -# Still earns its place after the migration. The collection stores topLoci AS -# the element's inner mcols, where alignment is definitional -- but the entry -# VIEW keeps `variantIds` and `topLoci` in separate slots, and -# .fmeEntryToGRanges() builds the range from the former while taking mcols -# positionally from the latter. Without this check a misaligned entry would -# convert into silently mis-assigned mcols. It becomes deletable only if the -# entry stops carrying the two separately. -# @noRd - -# Drift check: a susieFit$pip vector must match the topLoci pip column. Catches -# adjustPips() (or any mutator) updating one and forgetting the other. -# @noRd - # ---- fSuSiE credible-band / affected-region helpers -------------------- .isFsusieFit <- function(fit) { @@ -359,14 +339,6 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) { bind_rows(parts) } -# Map each fit-effect index (1..L, as affected_reg's `CS`) to pecotmr's -# canonical CS label + purity. The raw fSuSiE fit stores no purity (fsusieR -# computes it from the genotype matrix, which the fit does not retain), so the -# canonical value is `cs_95_purity` in the entry's topLoci. Matching is by the -# CS's variant -# MEMBERSHIP (not by index), which is robust to pecotmr's CS -# filtering/renumbering. -# @noRd # One fit effect's variant ids, resolving positional membership through the # fit's own variant order. # @noRd @@ -397,6 +369,13 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) { as.numeric(topLoci$cs_95_purity[topLoci$cs_95 == lab][1]) } +# Map each fit-effect index (1..L, as affected_reg's `CS`) to pecotmr's +# canonical CS label + purity. The raw fSuSiE fit stores no purity (fsusieR +# computes it from the genotype matrix, which the fit does not retain), so the +# canonical value is `cs_95_purity` in the entry's topLoci. Matching is by the +# CS's variant MEMBERSHIP (not by index), which is robust to pecotmr's CS +# filtering/renumbering. +# @noRd .fsusieCsMapFromTopLoci <- function(fit, topLoci) { L <- length(fit$cs) defaultLabel <- set_names(str_c("fsusie_", seq_len(L)), seq_len(L)) @@ -645,7 +624,7 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) { m <- filter(tl, !is.na(.data[[csCol]]) & .data[[csCol]] == lab) list( ids = as.character(m$variant_id), - eff = suppressWarnings(as.integer(str_remove(lab, "^.*_"))), + eff = .csLabelIndex(lab), lab = lab ) } @@ -1453,6 +1432,12 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) { # the two views silently joinable into a mix of the two quantities. posterior_effect = .tlCol(tl, "posterior_mean", "numeric"), posterior_se = .tlCol(tl, "posterior_sd", "numeric"), + # The infinitesimal part of `posterior_effect`, on the same scale. + # susieInf / susieAsh split their posterior into a sparse and an + # infinitesimal term and coef.susie() sums them, so the published + # effect is correct for those methods either way -- `theta` is what + # makes the split visible. Zero where the method has no such term. + theta = .tlCol(tl, "posterior_theta", "numeric"), pip = .tlCol(tl, "pip", "numeric"), logBF = .tlCol(tl, "logBF", "numeric") ) @@ -1478,6 +1463,7 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) { af = numeric(0), posterior_effect = numeric(0), posterior_se = numeric(0), + theta = numeric(0), pip = numeric(0), logBF = numeric(0) ) @@ -1576,22 +1562,6 @@ fineMappingRow <- function(variantIds, susieFit, topLoci, cvResult = NULL) { # @noRd .fmeIdentityCols <- c("variant_id", "chrom", "pos", "A1", "A2") -# Every `entry` must be a FineMappingRow. Checked before the payload is -# unpacked, so a wrong type names itself rather than failing as a missing -# method on whatever was passed instead. -# @noRd - -# One entry -> its element. -# @noRd - -# The entry view for a collection row (or rows). A tuple split across -# chromosomes owns several elements; they are stitched back into the single -# entry callers expect, the same contract sumStats() keeps. -# @noRd - -# One element (plus its outer-mcols payload) -> the entry view. -# @noRd - # Rebuild the canonical topLoci table from an element: the identity columns are # re-rendered from the range and alleles, the rest come straight from mcols. # @noRd diff --git a/R/fineMappingWrappers.R b/R/fineMappingWrappers.R index 9c01a6f9a..b000a8bb6 100644 --- a/R/fineMappingWrappers.R +++ b/R/fineMappingWrappers.R @@ -293,7 +293,7 @@ fitSusieInfThenSusie <- function( #' @param fitRetention How much of each fit is kept: \code{"slim"} (default) #' trims the retained fit to what downstream needs, \code{"full"} keeps the #' whole fit object. The \code{topLoci} table's per-credible-set columns -#' are governed by \code{credibleSet}'s \code{perCsColumns} instead. +#' are governed by \code{credibleSetParam}'s \code{perCsColumns} instead. #' @param otherQuantities Optional named list of extra per-method quantities to #' carry on the result untouched (returned as the \code{otherQuantities} #' element). Default \code{NULL}. @@ -771,6 +771,8 @@ extractVariantNames <- function(fit) { normalizeVariantId(variantNames) } +#' @importFrom susieR univariate_regression +#' @noRd extractSumstats <- function( dataX, dataY, @@ -973,7 +975,8 @@ computeCsTable <- function( # fSuSiE credible sets: purity is the min |correlation| WITHIN each CS # (fsusieR::cal_purity), recorded as sets$purity$min.abs.corr for the canonical -# .csPurityVec() reader; cs_corr keeps the BETWEEN-CS correlation matrix. +# .csPurityVec() reader. The correlation BETWEEN sets is not kept; +# computeCsCorrelation() derives it from an LD source on demand. .csTableFsusie <- function(fit, dataX, coverage) { sets <- try_fetch( fsusieGetCs(fit, dataX, requestedCoverage = coverage), @@ -1009,7 +1012,15 @@ computeCsTable <- function( # X vs Xcorr only changes how susie_get_cs computes purity; the between-CS # correlation is derived on demand later by computeCsCorrelation(), so it is # no longer stored on the fit. - ldArg <- if (csInput == "X") list(X = dataX) else list(Xcorr = dataX) + ldArg <- if (csInput == "Xcorr") { + list(Xcorr = dataX) + } else if (inherits(fit, "susieRss")) { + # An RSS fit's X is its LD reference's factor, standing in for the + # matrix, so its purity must be the matrix route's: never subsampled. + list(X = dataX, n_purity = Inf) + } else { + list(X = dataX) + } sets <- exec(susie_get_cs, !!!c(list(fit), csArgs, ldArg)) list(sets = sets, pip = fit$pip) } @@ -1020,9 +1031,43 @@ computeCsTable <- function( # fit. get_cs_correlation() needs only the CS membership + PIP + the LD, with # the LD columns/rows ALIGNED to the fit's variable order (variantIds). +# A fit's credible sets: a susie fit keeps them in sets$cs, an fSuSiE fit +# (class "susiF") in cs. Reading only the first made every fSuSiE fit look +# set-less, so its between-CS correlation was never computed. +# @noRd +.csSetsOf <- function(fit) { + if (inherits(fit, "susiF")) fit$cs else fit$sets$cs +} + # TRUE when the fit has fewer than two credible sets (no between-CS corr). .csCountBelowTwo <- function(fit) { - is.null(fit$sets) || is.null(fit$sets$cs) || length(fit$sets$cs) < 2L + length(.csSetsOf(fit)) < 2L +} + +# An fSuSiE fit's between-CS correlation as fsusieR::cal_cor_cs() defines it +# -- for each pair of sets, the largest correlation between a member of one +# and a member of the other, so 1 on the diagonal -- read from the LD among +# the sets' members alone. +# @noRd +.fsusieCsCorrelationLd <- function(cs, ldReference, ids, label) { + members <- sort(unique(as.integer(list_c(cs)))) + R <- .ldFromRef(ldReference, ids[members], label, onMissing = "error") + sets <- map(cs, match, table = members) + n <- length(cs) + values <- map2_dbl( + rep(seq_len(n), times = n), + rep(seq_len(n), each = n), + .csPairMaxCorrelation, + sets = sets, + R = R + ) + matrix(values, n, n) +} + +# The largest correlation between a member of set `l` and one of set `k`. +# @noRd +.csPairMaxCorrelation <- function(l, k, sets, R) { + max(R[sets[[l]], sets[[k]]]) } # GRanges spanning the fit's variants (parsed from chrom:pos in the ids). @@ -1058,6 +1103,7 @@ computeCsTable <- function( } #' @rdname computeCsCorrelation +#' @importFrom purrr map2_dbl setMethod( "computeCsCorrelation", signature(x = "FineMappingResultBase", ldSource = "SumStatsBase"), @@ -1073,22 +1119,29 @@ setMethod( if (.csCountBelowTwo(fit)) { return(NULL) } - ldSketch <- ldSketch(ldSource) - if (is.null(ldSketch)) { + ldReference <- ldReference(ldSource) + if (is.null(ldReference)) { abort(glue( "computeCsCorrelation(): the summary-statistics ldSource ", - "carries no LD sketch to derive the correlation from." + "carries no LD reference to derive the correlation from." )) } # onMissing = "error": every fit variant must be in the panel, else the # 1..p sets$cs indices would misalign with a shrunken LD matrix. - xcorr <- .ldFromSketch( - ldSketch, - .fmrPartsVariantIds(parts), - label = "computeCsCorrelation", - onMissing = "error" - ) - get_cs_correlation(list(sets = fit$sets, pip = fit$pip), Xcorr = xcorr) + ids <- .fmrPartsVariantIds(parts) + label <- "computeCsCorrelation" + if (inherits(fit, "susiF")) { + return(.fsusieCsCorrelationLd(fit$cs, ldReference, ids, label)) + } + csFit <- list(sets = fit$sets, pip = fit$pip) + if (!.ldIsGenotypeRef(ldReference)) { + xcorr <- .ldFromRef(ldReference, ids, label, onMissing = "error") + return(get_cs_correlation(csFit, Xcorr = xcorr)) + } + # A genotype reference's factor: get_cs_correlation() correlates only + # the credible sets' lead columns of it, never forming the p x p LD. + factor <- .ldFactorFromRef(ldReference, ids, label, onMissing = "error") + get_cs_correlation(csFit, X = factor) } } @@ -1780,12 +1833,19 @@ buildTopLoci <- function( mu <- .fmSliceCond(fit$mu, conditionIdx) mu2 <- .fmSliceCond(fit$mu2, conditionIdx) scaleFactors <- .btlScaleFactors(fit, nV) + infinitesimal <- .btlInfinitesimal(fit, nV) meanStd <- if (!is.null(mu) && all(dim(alpha) == dim(mu))) { - colSums(alpha * mu) + .btlInfinitesimal(fit, nV) + colSums(alpha * mu) + infinitesimal } else { rep(NA_real_, nV) } postMean <- meanStd / scaleFactors + # Reported on the SAME scale as postMean, because that is the question it + # answers: how much of the published weight is the infinitesimal term + # rather than the sparse one. The sum is already inside postMean -- this + # only lets a reader see the split, which is otherwise unrecoverable. + # Zero for every method that has no infinitesimal component. + postTheta <- infinitesimal / scaleFactors postSd <- if (!is.null(mu2) && all(dim(alpha) == dim(mu2))) { # From the STANDARDIZED mean, then rescaled: an SD rescales the same # way its mean does, but the mu2 identity only holds in the units mu2 @@ -1810,6 +1870,7 @@ buildTopLoci <- function( mu2 = mu2, postMean = postMean, postSd = postSd, + postTheta = postTheta, logBF = logBF ) } @@ -2014,9 +2075,6 @@ buildTopLoci <- function( list_c(pieces) } -# One credible set's conditional-lfsr values, or NULL when its effect is out -# of range or it covers no in-range variant. -# @noRd # The conditional lfsr of a univariate susie fit, which reports none. # # It has everything needed to compute one: the single-effect posterior for @@ -2049,6 +2107,9 @@ buildTopLoci <- function( array(lfsr, dim = c(dim(mu), 1L)) } +# One credible set's conditional-lfsr values, or NULL when its effect is out +# of range or it covers no in-range variant. +# @noRd .btlCondLfsrForSet <- function( csPos, setsPrim, @@ -2190,6 +2251,7 @@ buildTopLoci <- function( pip = as.numeric(fit$pip), posterior_mean = unname(post$postMean), posterior_sd = unname(post$postSd), + posterior_theta = unname(post$postTheta), logBF = unname(post$logBF) ) meta <- tibble( @@ -2229,6 +2291,7 @@ buildTopLoci <- function( pip = numeric(), posterior_mean = numeric(), posterior_sd = numeric(), + posterior_theta = numeric(), logBF = numeric(), cs_95 = character(), cs_70 = character(), @@ -2438,7 +2501,7 @@ trimFinemappingFit <- function(fit, effectIdx, method, csTables) { #' Promotes the primary method's per-method post-processing payload to the root #' level and attaches the unified \code{top_loci} table. The primary method's #' bare \code{FineMappingRow} appears at \code{$finemappingEntry}; wrap it -#' into a \code{FineMappingResult} collection at the pipeline level once (study, +#' into a fine-mapping result collection at the pipeline level once (study, #' context, trait, method) identity tags are known. #' #' @param post Output from \code{\link{postprocessFinemappingFits}}. @@ -2503,28 +2566,6 @@ csInfo <- function(susieOutputSetsCs, topVariantsIdx) { ) bind_rows(rows) } -#' @title Calculate Purity Measures for Credible Sets -#' -#' @description As an extension of the internal cal_purity function. This -#' function computes purity metrics (minimum, mean, and median absolute -#' correlations) for each credible set in a list of credible set indices, -#' based on the provided X matrix. The output Purity depends on the method -#' specified: for the 'min' method, it returns a single value for -#' single-element sets or the minimum absolute correlation for others. For -#' other methods, it returns a vector of three values (min, mean, median) for -#' each set. -#' -#' @param lCs A list of credible set indices, where each element is a vector of -#' indices corresponding to variables in a credible set. -#' @param X The data matrix used to compute correlations between variables in -#' each credible set. -#' @param method A character string specifying the method to use for calculating -#' purity. Defaults to 'min'. Other methods return a vector of min, mean, and -#' median absolute correlations for each credible set. -#' @return A list where each element corresponds to a credible set and contains -#' either a single purity value (for 'min' method and single-element sets) or -#' a vector of purity metrics (for other methods and multi-element sets). -#' @noRd # Absolute off-diagonal correlations among one credible set's variants. The # diagonal is blanked so a set is never judged pure by its self-correlation. @@ -2556,6 +2597,28 @@ csInfo <- function(susieOutputSetsCs, topVariantsIdx) { ) } +#' @title Calculate Purity Measures for Credible Sets +#' +#' @description As an extension of the internal cal_purity function. This +#' function computes purity metrics (minimum, mean, and median absolute +#' correlations) for each credible set in a list of credible set indices, +#' based on the provided X matrix. The output Purity depends on the method +#' specified: for the 'min' method, it returns a single value for +#' single-element sets or the minimum absolute correlation for others. For +#' other methods, it returns a vector of three values (min, mean, median) for +#' each set. +#' +#' @param lCs A list of credible set indices, where each element is a vector of +#' indices corresponding to variables in a credible set. +#' @param X The data matrix used to compute correlations between variables in +#' each credible set. +#' @param method A character string specifying the method to use for calculating +#' purity. Defaults to 'min'. Other methods return a vector of min, mean, and +#' median absolute correlations for each credible set. +#' @return A list where each element corresponds to a credible set and contains +#' either a single purity value (for 'min' method and single-element sets) or +#' a vector of purity metrics (for other methods and multi-element sets). +#' @noRd calPurity <- function(lCs, X, method = "min") { # Each `lCs[[k]]` is documented (and always supplied) as a plain index # vector; the unlist() that used to sit here was a no-op that quietly @@ -2597,12 +2660,12 @@ fsusieGetCs <- function(fsusieObj, X, requestedCoverage = 0.95) { str_c("L", seq_along(fsusieObj$cs)) ) - # Create 'purity' data frame - purity <- `colnames<-`( - bind_rows( - map(calPurity(fsusieObj$cs, X = X, method = "susie"), .asDataFrameT) - ), - c("minAbsCorr", "meanAbsCorr", "medianAbsCorr") + # Create 'purity' data frame: each set's (min, mean, median) |r|. + setPurity <- calPurity(fsusieObj$cs, X = X, method = "susie") + purity <- tibble( + minAbsCorr = map_dbl(setPurity, 1L), + meanAbsCorr = map_dbl(setPurity, 2L), + medianAbsCorr = map_dbl(setPurity, 3L) ) # Credible-set label as a `cs` column (was rownames; tibbles carry none). purityDf <- bind_cols(tibble(cs = names(csNamed)), purity) @@ -2957,10 +3020,6 @@ fitFsusie <- function( # extractors live with the rest of the fine-mapping/SuSiE wrappers). # ============================================================================= -# Shared helper for susie/susieAsh/susieInf weight extraction. -# @param fit A susie fit object (or NULL to fit from X, y). -# @param X Genotype matrix (optional). -# @param y Phenotype vector (optional). # Drop-intercept TWAS coefficient weights from a fitted SuSiE(-RSS) model # (zero the intercept, then coef.susie without the intercept row). # @noRd @@ -2968,13 +3027,18 @@ fitFsusie <- function( coef.susie(list_assign(fit, intercept = 0))[-1] } -# @param requiredFields Fields that must be present in the fit to extract -# weights. -# @param token SuSiE-family token ("susie" / "susieInf" / "susieAsh") selecting -# the unmappable_effects mode. The fit is delegated to .fmFitSusieIndiv so the -# package keeps a single susie-invocation point. -# @param userArgs Extra arguments forwarded to susieR::susie via -# .fmFitSusieIndiv. +# Shared helper for susie/susieAsh/susieInf weight extraction: the +# drop-intercept coefficients of an EXISTING fit. These never fine-map, so a +# NULL `fit` is an error naming the run that would produce one. +# @param fit A susie fit object. +# @param X Genotype matrix, or NULL; when given, its column count is checked +# against the fit's variants. +# @param requiredFields Fields the fit must carry for weights to be extracted; +# the weights are all zero when any is missing. +# @param token SuSiE-family token ("susie" / "susieInf" / "susieAsh") the +# error names. +# @param fitRetention "none" returns the weights alone; otherwise the fit rides +# along as their "fit" attribute. #' @importFrom susieR coef.susie susie #' @noRd .susieExtractWeights <- function( @@ -3191,7 +3255,7 @@ susieInfWeights <- function( #' @noRd .susieRssExtractWeights <- function( fit, - R, + nVariants, requiredFields, token = "susie", fitRetention = c("none", "slim", "full") @@ -3206,12 +3270,11 @@ susieInfWeights <- function( ) abort(msg) } - if (length(fit$pip) != nrow(R)) { + if (!is.na(nVariants) && length(fit$pip) != nVariants) { nPip <- length(fit$pip) - nR <- nrow(R) msg <- glue( - "Dimension mismatch: susieRss fit has {nPip} variants but R ", - "has {nR} rows." + "Dimension mismatch: susieRss fit has {nPip} variants but the ", + "statistics describe {nVariants}." ) abort(msg) } @@ -3232,8 +3295,11 @@ susieInfWeights <- function( #' \code{susieR::susie_rss()} from summary statistics and LD. #' #' @param stat List with components \code{z} (z-scores), \code{n} (sample -#' sizes). -#' @param LD LD correlation matrix. +#' sizes) and, to check against an \code{ldReference}, \code{variantNames}. +#' @param ldReference Optional LD reference: a genotype panel, a +#' \code{\link{GenotypeMixture}} or an \code{\link{LdMatrix}}. The fit is +#' checked against the statistics' variants and, with a reference, those +#' variants must also be in it. No LD is read. #' @param susieRssFit A fitted SuSiE-RSS object. Required: these wrappers #' extract weights and never run fine-mapping themselves. #' @param fitRetention How much of the fit is kept on the returned weights: @@ -3255,12 +3321,12 @@ susieInfWeights <- function( #' n = rep(nrow(X), ncol(X))) #' LD <- cor(X) #' fit <- susieR::susie_rss(z = stat$z, R = LD, n = nrow(X), L = 5) -#' susieRssWeights(stat, LD, susieRssFit = fit) +#' susieRssWeights(stat, susieRssFit = fit) #' @importFrom checkmate assertList assertFlag #' @export susieRssWeights <- function( stat, - LD, + ldReference = NULL, susieRssFit = NULL, fitRetention = c("slim", "none", "full") ) { @@ -3268,7 +3334,7 @@ susieRssWeights <- function( fitRetention <- arg_match(fitRetention) .susieRssExtractWeights( fit = susieRssFit, - R = LD, + nVariants = .rssVariantCount(stat, ldReference, "susieRssWeights"), requiredFields = c("alpha", "mu", "X_column_scale_factors"), token = "susie", fitRetention = fitRetention @@ -3297,12 +3363,12 @@ susieRssWeights <- function( #' n = rep(nrow(X), ncol(X))) #' LD <- cor(X) #' fit <- susieR::susie_rss(z = stat$z, R = LD, n = nrow(X), L = 5) -#' susieInfRssWeights(stat, LD, susieInfRssFit = fit) +#' susieInfRssWeights(stat, susieInfRssFit = fit) #' @importFrom checkmate assertList assertFlag #' @export susieInfRssWeights <- function( stat, - LD, + ldReference = NULL, susieInfRssFit = NULL, fitRetention = c("slim", "none", "full") ) { @@ -3310,7 +3376,7 @@ susieInfRssWeights <- function( fitRetention <- arg_match(fitRetention) .susieRssExtractWeights( fit = susieInfRssFit, - R = LD, + nVariants = .rssVariantCount(stat, ldReference, "susieInfRssWeights"), requiredFields = c("alpha", "mu", "theta", "X_column_scale_factors"), token = "susieInf", fitRetention = fitRetention @@ -3339,12 +3405,12 @@ susieInfRssWeights <- function( #' n = rep(nrow(X), ncol(X))) #' LD <- cor(X) #' fit <- susieR::susie_rss(z = stat$z, R = LD, n = nrow(X), L = 5) -#' susieAshRssWeights(stat, LD, susieAshRssFit = fit) +#' susieAshRssWeights(stat, susieAshRssFit = fit) #' @importFrom checkmate assertList assertFlag #' @export susieAshRssWeights <- function( stat, - LD, + ldReference = NULL, susieAshRssFit = NULL, fitRetention = c("slim", "none", "full") ) { @@ -3352,7 +3418,7 @@ susieAshRssWeights <- function( fitRetention <- arg_match(fitRetention) .susieRssExtractWeights( fit = susieAshRssFit, - R = LD, + nVariants = .rssVariantCount(stat, ldReference, "susieAshRssWeights"), requiredFields = c("alpha", "mu", "theta", "X_column_scale_factors"), token = "susieAsh", fitRetention = fitRetention @@ -3656,15 +3722,18 @@ fsusieWeights <- function( #' extracts coefficients from an existing \code{mvsusieR::mvsusie_rss} fit. #' It never runs fine-mapping itself --- \code{mvsusieRssFit} is required. #' -#' Follows the \code{*_rss_weights(stat, LD, ...)} contract: \code{stat} and -#' \code{LD} describe the block the fit is being extracted against, and the -#' fit is checked against them rather than refitted. +#' Follows the RSS weights contract: \code{stat} (and \code{ldReference}, +#' when given) describe the block the fit is being extracted against, and +#' the fit is checked against them rather than refitted. #' -#' @param stat A list with \code{z} (matrix variants x conditions) and \code{n} -#' (numeric vector or scalar), describing the block the fit came from. -#' @param LD LD correlation matrix for that block. The fit must cover the -#' same number of variants; a mismatch is an error, since weights from one -#' block are not comparable against another's LD. +#' @param stat A list with \code{z} (matrix variants x conditions), \code{n} +#' (numeric vector or scalar) and, to check against an \code{ldReference}, +#' \code{variantNames}, describing the block the fit came from. The fit +#' must cover the same number of variants; a mismatch is an error, since +#' weights from one block are not comparable against another's. +#' @param ldReference Optional LD reference for that block: a genotype +#' panel, a \code{\link{GenotypeMixture}} or an \code{\link{LdMatrix}}. +#' With one, the statistics' variants must also be in it. No LD is read. #' @param mvsusieRssFit A fitted \code{mvsusieRss} object. Required: this #' extracts weights and never runs fine-mapping itself. #' @param fitRetention How much of the fit is kept on the returned weights: @@ -3689,11 +3758,11 @@ fsusieWeights <- function( #' LD <- cor(X) #' fit <- fitMvsusieRss(Z = stat$z, R = LD, N = nrow(X), #' prior_variance = 1) -#' mvsusieRssWeights(stat, LD, mvsusieRssFit = fit) +#' mvsusieRssWeights(stat, mvsusieRssFit = fit) #' @export mvsusieRssWeights <- function( stat, - LD, + ldReference = NULL, mvsusieRssFit = NULL, fitRetention = c("none", "slim", "full") ) { @@ -3712,11 +3781,14 @@ mvsusieRssWeights <- function( abort(msg) } weights <- mvsusieR::coef.mvsusie(mvsusieRssFit)[-1, , drop = FALSE] - # `stat` / `LD` describe the block the fit is being combined with. The - # susie *RssWeights wrappers check the fit against them; this one used - # to take both and read neither, so a fit from a different block was - # extracted without complaint. - .mvsusieRssAssertBlock(weights, LD) + # `stat` (and `ldReference`) describe the block the fit is being combined + # with. The susie *RssWeights wrappers check the fit against them; this + # one used to take both and read neither, so a fit from a different block + # was extracted without complaint. + .mvsusieRssAssertBlock( + weights, + .rssVariantCount(stat, ldReference, "mvsusieRssWeights") + ) if (identical(fitRetention, "none")) { return(weights) } @@ -3726,13 +3798,13 @@ mvsusieRssWeights <- function( # The supplied fit must describe the LD block it is being extracted against; # the pairing is what makes the weights comparable across a region. # @noRd -.mvsusieRssAssertBlock <- function(weights, LD) { - if (is.null(LD) || nrow(weights) == nrow(LD)) { +.mvsusieRssAssertBlock <- function(weights, nVariants) { + if (is.na(nVariants) || nrow(weights) == nVariants) { return(invisible(NULL)) } abort(glue( "Dimension mismatch: mvsusieRss fit has {nrow(weights)} variants ", - "but LD has {nrow(LD)} rows." + "but the statistics describe {nVariants}." )) } @@ -3741,8 +3813,6 @@ mvsusieRssWeights <- function( # Cross-condition credible-set merging # ============================================================================= -# Identify variant IDs that are associated with more than one credible set. -# @noRd # The sets a variant belongs to, but only when it belongs to more than one. # @noRd .ovlMultiSets <- function(entry) { @@ -3750,6 +3820,8 @@ mvsusieRssWeights <- function( if (length(sets) > 1) sets else NULL } +# Identify variant IDs that are associated with more than one credible set. +# @noRd .identifyOverlapSets <- function(variantsSetsAndPipsList) { compact(map(variantsSetsAndPipsList, .ovlMultiSets)) } @@ -3776,8 +3848,6 @@ mvsusieRssWeights <- function( `[[<-`(parent, rootB, value = rootA) } -# Merge overlapping credible sets using connected components (union-find). -# @noRd # @noRd .ufPair <- function(s, first) { list(a = first, b = s) @@ -3804,6 +3874,8 @@ mvsusieRssWeights <- function( set_names(as.list(rep(label, length(members))), members) } +# Merge overlapping credible sets using connected components (union-find). +# @noRd .mergeAndUpdateOverlapSets <- function(variantsSetsAndPipsList, overlapSets) { allSets <- unique(list_c(overlapSets)) if (length(allSets) == 0) { @@ -3899,7 +3971,7 @@ mvsusieRssWeights <- function( #' the \code{"strong"} input for \code{\link{mashPipeline}}. #' #' @param fineMappingResult A \code{\link{QtlFineMappingResult}} (or any -#' \code{FineMappingResult}) produced by per-condition SuSiE fine-mapping. +#' \code{FineMappingResultBase}) produced by per-condition SuSiE fine-mapping. #' Each entry's \code{topLoci} must carry a credible-set column #' (\code{cs_}, e.g. \code{cs_95}, with values such as #' \code{"susie_1"} where the trailing integer is the set index and \code{_0} @@ -3919,8 +3991,8 @@ mvsusieRssWeights <- function( mergeSusieCs <- function(fineMappingResult, coverage = 0.95) { if (!is(fineMappingResult, "FineMappingResultBase")) { msg <- glue( - "`fineMappingResult` must be a QtlFineMappingResult (or ", - "FineMappingResult)." + "`fineMappingResult` must be a QtlFineMappingResult or ", + "GwasFineMappingResult." ) abort(msg) } @@ -3957,28 +4029,30 @@ mergeSusieCs <- function(fineMappingResult, coverage = 0.95) { #' Extract the trimmed SuSiE fit from a finemapping pipeline result #' -#' Returns the trimmed model fit underlying \code{con_data$finemappingEntry} (a -#' \code{FineMappingRow} S4 object), or NULL if no fine-mapping entry is -#' attached. +#' Returns the trimmed model fit underlying \code{conData$finemappingEntry} (a +#' \code{FineMappingRow}, or a single-row fine-mapping result), or NULL if no +#' fine-mapping entry is attached. #' #' @param conData List. The method-layer entry from a finemapping pipeline #' result, expected to carry \code{$finemappingEntry} as a -#' \code{FineMappingRow} object. +#' \code{FineMappingRow} or a single-row fine-mapping result. #' @return The trimmed fit (a list with \code{pip}, \code{sets}, etc.) or NULL. #' @examples -#' data(qtlSumStatsExample) -#' susieResult(qtlSumStatsExample) +#' data(qtlFineMappingExample) +#' fit <- susieResult(list(finemappingEntry = qtlFineMappingExample[1])) +#' head(fit$pip) #' @importFrom checkmate assertList #' @export susieResult <- function(conData) { - # No type guard: this is duck-typed on `$` and `length()` and returns NULL - # for anything without a `finemappingEntry`. Its own @example passes a - # QtlSumStats, which assertList rejects. + # Duck-typed on `$` and `length()`: NULL for anything without a + # fine-mapping entry. The entry is one row's worth of fine-mapping -- a + # FineMappingRow, or the single-row result fineMappingResult() returns -- + # which .fmrPartsSusieFit() reads alike. if (length(conData) == 0) { return(NULL) } fm <- conData$finemappingEntry - if (is.null(fm) || !is(fm, "FineMappingResultBase")) { + if (!is(fm, "FineMappingRow") && !is(fm, "FineMappingResultBase")) { return(NULL) } trimmed <- .fmrPartsSusieFit(fm) @@ -4019,8 +4093,8 @@ susieResult <- function(conData) { #' CS (its row of the between-CS matrix, self-correlation on the diagonal), #' computed on demand from \code{ldSource}. Absent when there are fewer than #' two credible sets.} -#' \item{cs_corr_max}{Maximum absolute between-CS correlation (excluding the -#' self == 1); \code{NA} for a single CS.} +#' \item{cs_corr_max}{Maximum absolute correlation with the other credible +#' sets; \code{NA} for a single CS.} #' \item{cs_corr_min}{Minimum absolute between-CS correlation; \code{NA} for a #' single CS.} #' @@ -4135,43 +4209,40 @@ extractTopPipInfo <- function(fmRow, sumstats) { ) } -# Reduce one credible set's correlation vector (a row of the between-CS matrix, -# the self-correlation == 1 on the diagonal) to its |corr| max/min, excluding -# every self / perfect correlation (== 1). An empty result yields NA. +# Credible set `i`'s largest and smallest |correlation| with the other sets +# in the between-CS matrix `csCorr`. Only its own entry is dropped, by +# position: two sets in perfect LD still report |r| = 1, and a diagonal a few +# ulps short of 1 (as a factor's correlation gives) is still the set with +# itself. `csCorr` exists only for two or more sets, so others always remain. # @noRd -.extractCorrelations <- function(x) { - filtered <- abs(x[x != 1]) - if (length(filtered) == 0L) { - return(list(max_corr = NA_real_, min_corr = NA_real_)) - } +.extractCorrelations <- function(i, csCorr) { + others <- abs(csCorr[i, -i]) list( - max_corr = max(filtered, na.rm = TRUE), - min_corr = min(filtered, na.rm = TRUE) + max_corr = max(others, na.rm = TRUE), + min_corr = min(others, na.rm = TRUE) ) } # Append the between-CS correlation columns to the per-CS summary `base` from # the m x m matrix `csCorr` (whose rows are aligned to `base`): the expanded # cs_corr_1..m (each CS's row, self-correlation on the diagonal) plus -# cs_corr_max / cs_corr_min (|corr| excluding the self == 1). A NULL matrix +# cs_corr_max / cs_corr_min (|corr| with the other sets). A NULL matrix # (fewer than two credible sets) yields NA max/min and no expanded columns. # @noRd .csAppendCorrelationCols <- function(base, csCorr) { if (is.null(csCorr)) { return(mutate(base, cs_corr_max = NA_real_, cs_corr_min = NA_real_)) } - perRow <- apply(csCorr, 1L, .extractCorrelations, simplify = FALSE) + perRow <- map(seq_len(nrow(csCorr)), .extractCorrelations, csCorr = csCorr) expanded <- `names<-`( as_tibble(csCorr, .name_repair = "minimal"), str_c("cs_corr_", seq_len(ncol(csCorr))) ) - # unname(): apply() names its result by the matrix rownames, which map_dbl - # then carries into the column (tibbles preserve element names). base |> bind_cols(expanded) |> mutate( - cs_corr_max = unname(map_dbl(perRow, "max_corr")), - cs_corr_min = unname(map_dbl(perRow, "min_corr")) + cs_corr_max = map_dbl(perRow, "max_corr"), + cs_corr_min = map_dbl(perRow, "min_corr") ) } @@ -4267,7 +4338,9 @@ extractTopPipInfo <- function(fmRow, sumstats) { # Sumstat counterpart of .fmFitSusieIndiv. Calls susieR::susie_rss with -# the same unmappable_effects switch, chained init, and userArgs merge. +# the same unmappable_effects switch, chained init, and userArgs merge. The +# LD is either the matrix `R` or, in its place, a factor `X` of it (see +# .ldRefFactor()), which susie_rss correlates itself without forming R. # @noRd .fmFitSusieRss <- function( z, @@ -4279,14 +4352,16 @@ extractTopPipInfo <- function(fmRow, sumstats) { userArgs = NULL, rFinite = NULL, rMismatch = "none", - rssControl = NULL + rssControl = NULL, + X = NULL ) { info <- .fmRssValidateToken(token) + ldArgs <- if (is.null(X)) list(R = R) else list(X = X) baseArgs <- .fmRssAddControl( c( + list(z = z), + ldArgs, list( - z = z, - R = R, n = n, coverage = coverage, unmappable_effects = info$unmappableEffects @@ -4306,7 +4381,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { userArgs, chainFromInf, z, - R, + ldArgs, n, coverage ) @@ -4354,14 +4429,15 @@ extractTopPipInfo <- function(fmRow, sumstats) { } # SuSiE-RSS(-ash) initialised from a SuSiE-inf fit; userArgs folded into the arg -# prep so L_greedy is clamped rather than passed through raw. +# prep so L_greedy is clamped rather than passed through raw. `ldArgs` is +# list(R = ) or list(X = ), whichever form of the LD the fit reads. .fmRssChainedArgs <- function( baseArgs, token, userArgs, chainFromInf, z, - R, + ldArgs, n, coverage ) { @@ -4374,7 +4450,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { list_assign( list_modify(baseArgs, !!!compact(chainedArgs)), z = z, - R = R, + !!!ldArgs, n = n, coverage = coverage ) @@ -4481,8 +4557,12 @@ extractTopPipInfo <- function(fmRow, sumstats) { tid, af, fitRetention, - region = NULL + region = NULL, + fitFullData = TRUE ) { + if (isFALSE(fitFullData)) { + return(set_names(rep(list(.fmCvOnlyRow()), length(toRun)), toRun)) + } chainLocal <- .fmResolveSusieChain(toRun, initializeWithSusieInf) infFit <- .fmXInfFit( chainLocal, @@ -4531,7 +4611,8 @@ extractTopPipInfo <- function(fmRow, sumstats) { af = NULL, fitRetention = "slim", seed = NULL, - region = NULL + region = NULL, + fitFullData = TRUE ) { out <- .fmXFitAll( toRun, @@ -4545,7 +4626,8 @@ extractTopPipInfo <- function(fmRow, sumstats) { tid = tid, af = af, fitRetention = fitRetention, - region = region + region = region, + fitFullData = fitFullData ) .fmXCrossValidate( out, @@ -4708,13 +4790,15 @@ extractTopPipInfo <- function(fmRow, sumstats) { .fmAttachCv(out[[tk]], .fmSliceCv(cv, tk)) } -# Fit every requested RSS token on one (z, R, n) sumstat block, returning a +# Fit every requested RSS token on one (z, LD, n) sumstat block, returning a # named list (token -> FineMappingRow). The sumstat analog of .fmFitXBlock: # the QtlSumStats and GwasSumStats methods both call it and differ only in how -# they push the returned entries (tuple shape) and the progress `label`. +# they push the returned entries (tuple shape) and the progress `label`. `ld` +# is list(R, X) as .fmRssLd() builds it: the LD matrix, the LD reference's +# factor, or both. .fmFitRssBlock <- function( z, - R, + ld, n, toRun, initializeWithSusieInf, @@ -4732,7 +4816,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { infFit <- .fmRssInfFit( chainLocal, z = z, - R = R, + R = ld$R, n = n, coverage = credibleSetParam$coverage, methodArgs = methodArgs, @@ -4747,7 +4831,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { chainLocal = chainLocal, infFit = infFit, z = z, - R = R, + ld = ld, n = n, credibleSetParam = credibleSetParam, methodArgs = methodArgs, @@ -4771,7 +4855,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { chainLocal, infFit, z, - R, + ld, n, credibleSetParam, methodArgs, @@ -4788,7 +4872,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { chainLocal, infFit, z = z, - R = R, + ld = ld, n = n, coverage = credibleSetParam$coverage, methodArgs = methodArgs, @@ -4801,7 +4885,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { } entry <- .fmRssPostprocess( f$fit, - R = R, + ld = ld, z = z, credibleSetParam = credibleSetParam, af = af, @@ -4849,12 +4933,14 @@ extractTopPipInfo <- function(fmRow, sumstats) { # Standard multi-effect SuSiE-RSS fit (susie / susieAsh): the only branch that # carries susieR's finite-sample R diagnostics and honours the SER fallback. +# The plain susie model reads the LD reference's factor when there is one; +# susieAsh needs the matrix (see .fmRssLd()). .fmRssFitStd <- function( tk, chainLocal, infFit, z, - R, + ld, n, coverage, methodArgs, @@ -4874,9 +4960,10 @@ extractTopPipInfo <- function(fmRow, sumstats) { msg <- glue("Fitting {tk} (RSS) for {label} ...") inform(msg) } + useFactor <- tk == "susie" && !is.null(ld$X) fit <- .fmFitSusieRss( z, - R, + if (useFactor) NULL else ld$R, n, tk, chainFromInf = chainFrom, @@ -4884,7 +4971,8 @@ extractTopPipInfo <- function(fmRow, sumstats) { userArgs = methodArgs[[tk]], rFinite = susieRssParam$rFinite, rMismatch = susieRssParam$rMismatch, - rssControl = .rssControlList(susieRssParam$control) + rssControl = .rssControlList(susieRssParam$control), + X = if (useFactor) ld$X ) .fmRssSerFallback(fit, susieRssParam) } @@ -4921,7 +5009,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { chainLocal, infFit, z, - R, + ld, n, coverage, methodArgs, @@ -4953,7 +5041,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { chainLocal, infFit, z = z, - R = R, + ld = ld, n = n, coverage = coverage, methodArgs = methodArgs, @@ -4964,10 +5052,12 @@ extractTopPipInfo <- function(fmRow, sumstats) { list(fit = std$fit, flag = std$flag, isStd = TRUE, multiFit = std$multiFit) } -# Post-process one RSS fit into a finemapping entry. +# Post-process one RSS fit into a finemapping entry. The credible sets' +# purity reads the LD reference's factor when there is one (only the sets' +# own columns are correlated), else the LD matrix. .fmRssPostprocess <- function( fit, - R, + ld, z, credibleSetParam, af, @@ -4978,7 +5068,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { .fmPostprocessOne( fit = fit, method = "susieRss", - dataX = R, + dataX = ld$X %||% ld$R, dataY = list(z = z), af = af, # Per-variant effective N (reporting-only, top_loci$N). NULL on any @@ -4986,7 +5076,7 @@ extractTopPipInfo <- function(fmRow, sumstats) { # never 1. This is NOT `n` (the scalar median the RSS fit consumes). n = nVar, region = region, - csInput = "Xcorr", + csInput = if (is.null(ld$X)) "Xcorr" else "X", credibleSetParam = credibleSetParam, fitRetention = fitRetention ) @@ -5130,13 +5220,13 @@ extractTopPipInfo <- function(fmRow, sumstats) { str_c(methodTag, "_", ix) } -# Trailing integer of a "_" cs string (0 when empty / NA). +# Trailing integer of "_" credible-set labels, the inverse of +# .btlCsLabel ("susie_3" -> 3L). Vectorized. 0 marks "in no credible set", +# which is also what an empty or NA label means. # @noRd -.cs95ToIndex <- function(s) { - if (is.na(s) || str_length(s) == 0L) { - return(0L) - } - suppressWarnings(as.integer(str_remove(s, "^.*_"))) +.csLabelIndex <- function(labels) { + idx <- suppressWarnings(as.integer(str_remove(labels, "^.*_"))) + replace(idx, is.na(labels) | str_length(labels) == 0L, 0L) } # @noRd @@ -5166,11 +5256,6 @@ extractTopPipInfo <- function(fmRow, sumstats) { ) } -# @noRd -.asDataFrameT <- function(x) { - as.data.frame(t(x)) -} - # @noRd .amRow <- function(l, am) { as.numeric(am[l, ]) diff --git a/R/genotypeIo.R b/R/genotypeIo.R index cc5d515c2..0fdb4feab 100644 --- a/R/genotypeIo.R +++ b/R/genotypeIo.R @@ -1,7 +1,8 @@ -#' @title Genotype I/O via GenotypeHandle -#' @description Read genotype data from various formats (VCF, plink1, plink2, -#' GDS) and provide block-level genotype extraction without requiring format -#' conversion. +#' @title Genotype I/O +#' @description Read genotype data in its own format (VCF, plink1, plink2, +#' GDS, BGEN) as a lazily read genotype panel, without converting it: +#' PLINK2 and BGEN files through a GenotypeHandle, VCF, GDS and PLINK1 +#' files through a GenotypeDosageSeed. #' @name pecotmr-genotype-io #' @keywords internal #' @importFrom SummarizedExperiment SummarizedExperiment rowRanges @@ -34,9 +35,7 @@ setMethod( "readGenotypes", signature(path = "character"), function(path, format = NULL, vcfArgs = list()) { - .genotypeExperiment( - .readGenotypeHandle(path, format = format, vcfArgs = vcfArgs) - ) + .readGenotypePanel(path, format = format, vcfArgs = vcfArgs) } ) @@ -63,10 +62,9 @@ setMethod( vcfArgs = list() ) { # The keyword sources are declared here rather than absorbed by `...` - # and forwarded: GenotypeHandle() is internal, so this signature is - # the only place a caller can name them, and a misspelled source - # should be an error at the call rather than a silently unset one. - .genotypeExperiment(GenotypeHandle( + # and forwarded, so a misspelled source is an error at the call + # rather than a silently unset one. + .genotypePanelFromSources( plink1Prefix = plink1Prefix, plink2Prefix = plink2Prefix, bed = bed, @@ -81,24 +79,100 @@ setMethod( chroms = chroms, format = format, vcfArgs = vcfArgs - )) + ) } ) -# The handle behind a panel. Internal: the handle is the seed layer, and the +# The panel a keyword source names: exactly one source, checked as +# GenotypeHandle() checks it. A PLINK2 triplet is read through a handle, a +# PLINK1 one as a panel; a file an LD-meta row or a per-chromosome meta +# names, through whatever reads its format. +# @noRd +.genotypePanelFromSources <- function( + plink1Prefix = NULL, + plink2Prefix = NULL, + bed = NULL, + bim = NULL, + fam = NULL, + pgen = NULL, + pvar = NULL, + psam = NULL, + ldMeta = NULL, + region = NULL, + genoMeta = NULL, + chroms = NULL, + format = NULL, + vcfArgs = list() +) { + flags <- .ghValidateArgs( + bed = bed, + bim = bim, + fam = fam, + pgen = pgen, + pvar = pvar, + psam = psam, + ldMeta = ldMeta, + region = region + ) + sources <- .ghResolveSources( + path = NULL, + plink1Prefix = plink1Prefix, + plink2Prefix = plink2Prefix, + ldMeta = ldMeta, + genoMeta = genoMeta, + chroms = chroms, + flags = flags + ) + switch( + names(sources)[which(sources)], + plink1Prefix = .readGenotypePanel(plink1Prefix, format = "plink1"), + plink2Prefix = .genotypeExperiment(.makePlink2Handle(plink2Prefix)), + plink1Triplet = .readGenotypePanel( + .plink1TripletStem(bed, bim, fam), + format = "plink1" + ), + plink2Triplet = .genotypeExperiment( + .genotypeHandleFromPlink2Triplet(pgen, pvar, psam) + ), + ldMeta = .ghLdPathToPanel(.ghResolveLdPath(ldMeta, region), vcfArgs), + genoMeta = .chromMetaPanel(genoMeta, chroms, format) + ) +} + +# The formats read as a panel directly, through a GenotypeDosageSeed; every +# other format is read through a GenotypeHandle. +# @noRd +.DOSAGE_PANEL_FORMATS <- c("vcf", "gds", "plink1") + +# The panel of one genotype file or PLINK prefix. A VCF, GDS or PLINK1 file +# is read as a panel directly; a PLINK2 or BGEN file through a +# GenotypeHandle. +# @noRd +.readGenotypePanel <- function(path, format = NULL, vcfArgs = list()) { + format <- format %||% .requireGenotypeFormat(path, "readGenotypes") + if (!is_in(format, .DOSAGE_PANEL_FORMATS)) { + return(.genotypeExperiment(.readGenotypeHandle(path, format = format))) + } + .withSourceProjection(.readDosagePanel(path, format, vcfArgs)) +} + +# The handle behind a panel of a format a GenotypeHandle reads: PLINK2 or +# BGEN. Internal: the handle is the seed layer, and the # handle-construction machinery below calls this rather than readGenotypes() # so it does not wrap and immediately unwrap a panel on every hop. # @noRd -.readGenotypeHandle <- function(path, format = NULL, vcfArgs = list()) { - if (is.null(format)) { - format <- .h2DetectFormat(path) - } +.readGenotypeHandle <- function(path, format = NULL) { + format <- format %||% .requireGenotypeFormat(path, "readGenotypes") switch( format, - "gds" = .makeGdsHandle(path), - "vcf" = .makeVcfHandle(path, vcfArgs = vcfArgs), - "plink1" = .makePlink1Handle(path), "plink2" = .makePlink2Handle(path), + "bgen" = .makeBgenHandle(path), + "vcf" = , + "gds" = , + "plink1" = abort(c( + glue("A {format} file is read as a genotype panel, not a handle."), + i = "Read it with readGenotypes()." + )), .abortUnsupportedFormat(format) ) } @@ -111,21 +185,13 @@ setMethod( # lets @snpInfo be row-subset (e.g. to the range of a study's summary stats) # while genotype reads that index BY FILE POSITION still resolve correctly: # PLINK2's ReadList(variant_subset=) and the per-chromosome PLINK2 view inside -# sharded routing read fileIdx[snpIdx]; the by-id backends (plink1/gds/vcf) look -# up snpInfo$SNP[snpIdx] / $BP[snpIdx] and ignore fileIdx entirely. For a full -# (unsubset) handle fileIdx == seq_len(nrow), so reads are unchanged. +# sharded routing read fileIdx[snpIdx]. For a full (unsubset) handle fileIdx +# == seq_len(nrow), so reads are unchanged. # @noRd .withFileIdx <- function(snpInfo) { mutate(snpInfo, fileIdx = seq_len(nrow(snpInfo))) } -# Restrict a GenotypeHandle's @snpInfo to `keep` (a logical mask or integer row -# indices into @snpInfo). Genotype reads stay correct because fileIdx carries -# each kept variant's original file position; everything else (path/format/ -# pgenPtr/chromPaths/samples) is preserved. NULL-safe; a no-op when nothing is -# dropped. Handles built before the fileIdx column existed are NOT subset (the -# read path would be positional) -- return them unchanged. -# @noRd # A handle trimmed to zero variants, every other property preserved. # # Separate from .subsetGenotypeHandle() because that declines to subset a @@ -142,7 +208,7 @@ setMethod( # dead weight (~10k anonymous names, the bulk of a skipped-region file). # nSamples must go to 0 alongside sampleIds, or dm (variants x nSamples) # would disagree with the now-empty derived sample dimnames. Reached only - # via .emptySketch (the empty / PIP-skip path), never on a surviving region. + # via .emptyLdRef (the empty / PIP-skip path), never on a surviving region. methods::initialize( handle, snpInfo = slice(snpInfo(handle), integer(0)), @@ -151,6 +217,13 @@ setMethod( ) } +# Restrict a GenotypeHandle's @snpInfo to `keep` (a logical mask or integer row +# indices into @snpInfo). Genotype reads stay correct because fileIdx carries +# each kept variant's original file position; everything else (path/format/ +# pgenPtr/chromPaths/samples) is preserved. NULL-safe; a no-op when nothing is +# dropped. Handles built before the fileIdx column existed are NOT subset (the +# read path would be positional) -- return them unchanged. +# @noRd .subsetGenotypeHandle <- function(handle, keep) { if (is.null(handle)) { return(NULL) @@ -166,149 +239,13 @@ setMethod( methods::initialize(handle, snpInfo = slice(si, keepIdx)) } -#' @importFrom checkmate assertFileExists -#' @keywords internal -.makeGdsHandle <- function(path) { - if (!requireNamespace("SNPRelate", quietly = TRUE)) { - abort("Package 'SNPRelate' is required for reading GDS files.") - } - if (!requireNamespace("gdsfmt", quietly = TRUE)) { - abort("Package 'gdsfmt' is required for reading GDS files.") - } - assertFileExists(path, access = "r", .var.name = "GDS file") - - snpInfo <- .gdsSnpInfo(path) - - sampleIds <- .withGds(path, .gdsReadSampleIds) - nSamples <- length(sampleIds) - - new( - "GenotypeHandle", - path = path, - format = "gds", - snpInfo = .withFileIdx(snpInfo), - nSamples = as.integer(nSamples), - sampleIds = sampleIds, - pgenPtr = NULL - ) -} - -#' @importFrom checkmate assertFileExists -#' @keywords internal -.makeVcfHandle <- function(path, vcfArgs = list()) { - if (!requireNamespace("VariantAnnotation", quietly = TRUE)) { - abort("Package 'VariantAnnotation' is required for reading VCF files.") - } - assertFileExists(path, access = "r", .var.name = "VCF file") - - hdr <- VariantAnnotation::scanVcfHeader(path) - sampleIds <- as.character(VariantAnnotation::samples(hdr)) - nSamples <- length(sampleIds) - - param <- VariantAnnotation::ScanVcfParam( - fixed = c("ALT"), - info = NA, - geno = NA - ) - vcf <- exec( - VariantAnnotation::readVcf, - path, - param = param, - !!!vcfArgs - ) - rd <- rowRanges(vcf) - - # pecotmr convention: A1 = ALT (effect), A2 = REF - snpInfo <- tibble( - SNP = names(rd), - CHR = as.character(seqnames(rd)), - BP = as.integer(start(rd)), - A1 = map_chr(rd$ALT, .gtFirstAllele), - A2 = as.character(rd$REF) - ) - - new( - "GenotypeHandle", - path = normalizePath(path), - format = "vcf", - snpInfo = .withFileIdx(snpInfo), - nSamples = as.integer(nSamples), - sampleIds = sampleIds, - pgenPtr = NULL - ) -} - -#' @keywords internal -.makePlink1Handle <- function(path) { - if (!requireNamespace("snpStats", quietly = TRUE)) { - abort("Package 'snpStats' is required for reading plink1 files.") - } - stem <- .plink1RequireFiles(path) - meta <- .plink1ReadMeta(stem) - new( - "GenotypeHandle", - path = stem, - format = "plink1", - snpInfo = .withFileIdx(meta$snpInfo), - nSamples = as.integer(meta$nSamples), - sampleIds = meta$sampleIds, - pgenPtr = NULL - ) -} - -# Resolve the plink1 stem and assert its .bed/.bim/.fam all exist. -# @noRd -#' @importFrom checkmate assertFileExists -.plink1RequireFiles <- function(path) { - stem <- .plinkStem(path) - assertFileExists( - str_c(stem, c(".bed", ".bim", ".fam")), - access = "r", - .var.name = "Plink file" - ) - stem -} - -# Read the .bim / .fam sidecars into snpInfo + sample metadata. Returns -# list(snpInfo, sampleIds, nSamples). -# @noRd -.plink1ReadMeta <- function(stem) { - # col_types all-character so readr never coerces a column: a bim whose - # allele column is uniformly "T"/"F" (all-A/T SNPs) would otherwise become - # logical, silently corrupting A1/A2 (BP is cast to integer below). - bim <- read_table( - str_c(stem, ".bim"), - col_names = c("CHR", "SNP", "CM", "BP", "A1", "A2"), - col_types = cols(.default = col_character()) - ) - fam <- read_table( - str_c(stem, ".fam"), - col_names = FALSE, - col_types = cols(.default = col_character()) - ) - # plink1 bim: col5 = A1 (minor/effect), col6 = A2 (major/ref); matches the - # pecotmr convention directly. - snpInfo <- tibble( - SNP = bim$SNP, - CHR = as.character(bim$CHR), - BP = as.integer(bim$BP), - A1 = bim$A1, - A2 = bim$A2 - ) - list( - snpInfo = snpInfo, - sampleIds = as.character(fam[[2]]), - nSamples = nrow(fam) - ) -} - #' @keywords internal .makePlink2Handle <- function(path) { if (!requireNamespace("pgenlibr", quietly = TRUE)) { abort("Package 'pgenlibr' is required for reading plink2 files.") } - stem <- .plinkStem(path) + stem <- .genotypeStem(path) # Use pecotmr's resolvePlink2Paths for robust path detection (.pvar.zst) paths <- resolvePlink2Paths(stem) @@ -376,11 +313,8 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { if (length(.genotypeChromPaths(handle)) > 0L) { return(.extractBlockSharded(handle, snpIdx, meanImpute = meanImpute)) } - # An empty request is answered without reaching a reader. This is not - # defensive tidiness: snpStats::read.plink(select.snps = character(0)) - # SEGFAULTS, taking the R session with it rather than raising a condition - # tryCatch could see. The sharded branch above already returns early for - # the same reason; this is the single-file path catching up. + # An empty request is answered without reaching a reader, which need not + # accept one, as the sharded branch above already answers it. if (length(snpIdx) == 0L) { return(.emptyBlockSe(sampleIds(handle))) } @@ -388,9 +322,6 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { # .restoreRequestedOrder() for why this is not merely tidiness. ord <- order(.genotypeFilePos(handle, snpIdx)) raw <- .extractBlockByFormat(handle, snpIdx[ord]) - if (is.null(raw)) { - return(NULL) - } ordered <- .restoreRequestedOrder(raw, ord) geno <- if (meanImpute) .meanImputeGeno(ordered) else ordered .blockGenotypesToSe(geno, handle, snpIdx) @@ -408,17 +339,14 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { # Put a block's variant columns back into the order the caller asked for. # -# snpStats (`select.snps=`) and SNPRelate (`snp.id=`) return variants in FILE -# order however they were asked for, while .blockGenotypesToSe() labels the -# block from snpInfo[snpIdx], i.e. the REQUESTED order. An unsorted request -# therefore produced a block whose names and dosages described different -# variants -- silently, since both are the right length. pgenlibr reads -# positionally and was unaffected. -# -# Reading ascending and permuting back is correct for every backend, including -# the ones that do honour the request: given a sorted request they return -# sorted output either way, so the inverse permutation restores the caller's -# order in both cases. +# .blockGenotypesToSe() labels the block from snpInfo[snpIdx], i.e. the +# REQUESTED order, so a backend returning variants in FILE order however they +# were asked for -- snpStats did, when it read PLINK1 -- would yield a block +# whose names and dosages describe different variants, silently, since both +# are the right length. Reading ascending and permuting back is correct for +# every backend, whether or not it honours the requested order: given a +# sorted request it returns sorted output either way, so the inverse +# permutation restores the caller's order. # @noRd .restoreRequestedOrder <- function(geno, ord) { if (ncol(geno) != length(ord)) { @@ -449,10 +377,18 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { as.matrix(genotypeCovariates) } cd <- .genotypeColData(gCov, colnames(dosage)) - SummarizedExperiment::SummarizedExperiment( - assays = list(dosage = dosage), - rowRanges = .genotypeSnpRanges(genotypes, rownames(dosage)), - colData = cd + # A stochastic sketch's dosages are projections, so its real sample count + # and allele frequencies are recorded from its sidecar here, once; and + # where the genotypes come from is recorded for what needs the file + # (.ldRefSource()). + .withLdProjection( + SummarizedExperiment::SummarizedExperiment( + assays = list(dosage = dosage), + rowRanges = .genotypeSnpRanges(genotypes, rownames(dosage)), + colData = cd, + metadata = list(genotypeSource = .genotypeSourceRecord(genotypes)) + ), + .sketchProjectionRecord(genotypes) ) } @@ -518,7 +454,28 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { # describe variants the same way, and overlaps between the two actually hit. # @noRd .genotypeSnpRanges <- function(genotypes, variantIds) { - si <- snpInfo(genotypes) + .snpInfoRanges(snpInfo(genotypes), variantIds) +} + +# A panel's variants snpInfo-style, one row per rowRanges element: SNP (the +# file's own ids), CHR (canonical), BP, A1, A2. The inverse of +# .snpInfoRanges(), so genotypes from any backend answer alike. +# @noRd +.rangesSnpInfo <- function(gr) { + mc <- S4Vectors::mcols(gr) + tibble( + SNP = as.character(mc$SNP), + CHR = canonChrom(as.character(seqnames(gr))), + BP = as.integer(start(gr)), + A1 = as.character(mc$A1), + A2 = as.character(mc$A2) + ) +} + +# The rowRanges of variants described snpInfo-style (SNP, CHR, BP, A1, A2), +# named `variantIds`: a handle's, or a VCF, GDS or PLINK1 panel's rows. +# @noRd +.snpInfoRanges <- function(si, variantIds) { `names<-`( GenomicRanges::GRanges( seqnames = withChrPrefix(as.character(si$CHR)), @@ -537,10 +494,8 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { fmt <- genotypeFormat(handle) switch( fmt, - "gds" = .extractBlockGds(handle, snpIdx), - "vcf" = .extractBlockVcf(handle, snpIdx), - "plink1" = .extractBlockPlink1(handle, snpIdx), "plink2" = .extractBlockPlink2(handle, snpIdx), + "bgen" = .extractBlockBgen(handle, snpIdx), .abortUnsupportedFormat(fmt, "extractBlockGenotypes") ) } @@ -668,60 +623,6 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { ) } -#' @keywords internal -.extractBlockGds <- function(handle, snpIdx) { - .withGds( - .genotypeReadPath(handle), - .gdsBlockGeno, - fnArgs = list(handle = handle, snpIdx = snpIdx), - allow.fork = TRUE - ) -} - -#' @keywords internal -.extractBlockVcf <- function(handle, snpIdx) { - si <- slice(snpInfo(handle), snpIdx) - gr <- GRanges( - seqnames = si$CHR, - ranges = IRanges(start = si$BP, end = si$BP) - ) - param <- VariantAnnotation::ScanVcfParam( - which = gr, - fixed = NA, - info = NA, - geno = "GT" - ) - vcf <- VariantAnnotation::readVcf( - .genotypeReadPath(handle), - genome = "", - param = param - ) - gt <- VariantAnnotation::geno(vcf)$GT - - # Convert GT strings to ALT dosage (A1 dosage): one column per variant, - # laid side by side rather than filled into a preallocated matrix. - matrix( - map_dbl(as.vector(t(gt)), .gtStringToDosage), - nrow = ncol(gt), - ncol = nrow(gt) - ) -} - -#' @keywords internal -.extractBlockPlink1 <- function(handle, snpIdx) { - snpIds <- snpInfo(handle)$SNP[snpIdx] - pathStem <- .genotypeReadPath(handle) - plinkData <- snpStats::read.plink( - bed = str_c(pathStem, ".bed"), - bim = str_c(pathStem, ".bim"), - fam = str_c(pathStem, ".fam"), - select.snps = snpIds - ) - # snpStats as(x, "numeric") gives count of B allele (A2/bim col 6). - # Flip to count A1 (bim col 5 / effect allele). - `storage.mode<-`(2 - as(plinkData$genotypes, "numeric"), "double") -} - #' @keywords internal #' @importFrom rlang try_fetch .extractBlockPlink2 <- function(handle, snpIdx) { @@ -769,21 +670,17 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { # @noRd .dosageMatrix <- function(handle, snpIdx, meanImpute = TRUE) { block <- extractBlockGenotypes(handle, snpIdx, meanImpute = meanImpute) - # NULL, not an error, when the reader declines the block: callers read - # that as "no genotypes here" (computeLd answers with an identity). - if (is.null(block)) { - return(NULL) - } t(SummarizedExperiment::assay(block, "dosage")) } -# Open a GDS read-only, guarantee it is closed on exit, and return fn(gds). -# The shared form of the snpgdsOpen(...) + on.exit(snpgdsClose(gds)) idiom -# used across the GDS readers. +# Resource bracket: open the SNPRelate GDS at `path` read-only, guarantee it +# is closed on exit, and run `fn(gds, ...)` -- the snpgdsOpen(...) + +# on.exit(snpgdsClose(gds)) idiom of the on-disk LD routine, which needs +# SNPRelate's own handle. `fn` is a top-level function (not an inline +# closure); its per-call inputs are threaded through `...`. A file a panel's +# GDSArray backend already holds open in this session is opened again rather +# than refused. # @noRd -# Resource bracket: open the GDS at `path`, guarantee it is closed on exit, and -# run `fn(gds, ...)`. `fn` is a top-level function (not an inline closure); its -# per-call inputs are threaded through `...`. .withGds <- function( path, fn, @@ -794,6 +691,7 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { gds <- SNPRelate::snpgdsOpen( path, readonly = readonly, + allow.duplicate = TRUE, allow.fork = allow.fork ) on.exit(SNPRelate::snpgdsClose(gds)) @@ -801,11 +699,11 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { } #' @keywords internal -.computeBlockLdGds <- function(handle, snpIdx) { +.computeBlockLdGds <- function(path, snpIds) { .withGds( - .genotypeReadPath(handle), + path, .gdsBlockLd, - fnArgs = list(handle = handle, snpIdx = snpIdx), + fnArgs = list(snpIds = snpIds), allow.fork = TRUE ) } @@ -861,88 +759,86 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { replace(geno, naMask, means[col(geno)[naMask]]) } -#' @keywords internal -.gdsSnpInfo <- function(gdsPath) { - .withGds(gdsPath, .gdsReadSnpInfo) -} +# ============================================================================= +# Genotype format detection +# ----------------------------------------------------------------------------- +# The one place a genotype path's format is told. A file is told by its +# extension, in any case; a PLINK prefix by the PLINK file beside it, a .pgen +# before a .bed (PLINK2 can hold dosages, so it wins when both are there). A +# VCF, GDS or BGEN file is always named by its own path. +# ============================================================================= -# Map a lowercase file extension to a genotype format, or NULL if unrecognized. +# The extensions naming each genotype format, as regular expressions. # @noRd -.h2FormatFromExt <- function(ext) { - switch( - ext, - "vcf" = "vcf", - "bcf" = "vcf", - "bed" = "plink1", - "bim" = "plink1", - "fam" = "plink1", - "pgen" = "plink2", - "pvar" = "plink2", - "psam" = "plink2", - "gds" = "gds", - "rds" = "rds", - "rdata" = "rds", - "annot" = "ldsc_annot", - "bw" = "bigwig", - "bigwig" = "bigwig", - NULL - ) -} - -.h2DetectFormat <- function(path) { - lpath <- str_to_lower(path) - if ( - str_detect(lpath, "\\.vcf\\.gz$") || - str_detect(lpath, "\\.vcf\\.bgz$") - ) { - return("vcf") - } - if (str_detect(lpath, "\\.annot\\.gz$")) { - return("ldsc_annot") - } +.GENOTYPE_EXTENSIONS <- c( + vcf = "vcf(\\.b?gz)?|bcf", + gds = "gds", + bgen = "bgen", + plink1 = "bed|bim|fam", + plink2 = "pgen|pvar(\\.zst)?|psam" +) + +# The genotype format a file's extension names, or NULL when it names none. +# @noRd +.genotypeFileFormat <- function(path) { + named <- str_detect( + str_to_lower(path), + str_c("\\.(", .GENOTYPE_EXTENSIONS, ")$") + ) + if (any(named)) names(.GENOTYPE_EXTENSIONS)[which(named)[[1L]]] else NULL +} - ext <- str_to_lower(file_ext(path)) - if (str_length(ext) > 0L) { - detected <- .h2FormatFromExt(ext) - if (!is.null(detected)) return(detected) +# The genotype format of a file or PLINK prefix: by the file's extension, or +# for a prefix by the PLINK file beside it; NULL when neither tells. +# @noRd +.genotypePathFormat <- function(path) { + byExtension <- .genotypeFileFormat(path) + if (!is.null(byExtension)) { + return(byExtension) } - # Check for file stems, including dotted prefixes such as sample.EUR.chr21. - if ( - file.exists(str_c(path, ".pgen")) || file.exists(str_c(path, ".pvar")) - ) { + if (file.exists(str_c(path, ".pgen"))) { return("plink2") } - if (file.exists(str_c(path, ".bed")) || file.exists(str_c(path, ".bim"))) { + if (file.exists(str_c(path, ".bed"))) { return("plink1") } - if (file.exists(str_c(path, ".gds"))) { - return("gds") - } - if (str_length(ext) > 0L) { - msg <- glue("Cannot detect format from extension: {ext}") - abort(msg) - } - msg <- glue("Cannot detect genotype format for path: {path}") - abort(msg) + NULL } -#' @title Detect Plink File Stem -#' @description Given any plink file path, return the stem. -#' @param path Character, path to any plink file. -#' @return Character, file stem without extension. -#' @keywords internal -.plinkStem <- function(path) { - # Only strip known plink extensions; leave other paths as-is (they may - # already be the stem, e.g. "prefix.genotype" -> "prefix.genotype.bed") - ext <- file_ext(path) - plinkExts <- c("bed", "bim", "fam", "pgen", "pvar", "psam") - if (is_in(str_to_lower(ext), plinkExts)) { - file_path_sans_ext(path) - } else { - path - } +# .genotypePathFormat(), or an error from `label` when the path names no +# genotype format. +# @noRd +.requireGenotypeFormat <- function(path, label) { + format <- .genotypePathFormat(path) + if (!is.null(format)) { + return(format) + } + abort(c( + glue( + "{label}: '{path}' is neither a genotype file nor a PLINK prefix." + ), + i = str_c( + "A genotype file is named by its extension (.vcf.gz, .vcf.bgz, ", + ".gds, .bgen, .bed, .pgen); a PLINK prefix by the .pgen or .bed ", + "beside it." + ) + )) } +# The stem of a genotype path: the path without the extension naming its +# format, which is what PLINK's readers and a file's sidecars (.afreq, +# .stochastic_meta.tsv, .sample) are named after. A PLINK prefix is its own +# stem. +# @noRd +.genotypeStem <- function(path) { + str_remove( + path, + regex( + str_c("\\.(", str_flatten(.GENOTYPE_EXTENSIONS, "|"), ")$"), + ignore_case = TRUE + ) + ) +} # ============================================================================= # Format-specific file readers @@ -956,34 +852,17 @@ extractBlockGenotypes <- function(handle, snpIdx, meanImpute = TRUE) { # read PLINK files #' @importFrom vroom vroom -#' @importFrom tools file_path_sans_ext #' @importFrom Rsamtools TabixFile seqnamesTabix scanTabix headerTabix #' @importFrom GenomicRanges GRanges seqnames #' @importFrom SummarizedExperiment assay readBim <- function(bed) { - bimf <- str_c(file_path_sans_ext(bed), ".bim") + bimf <- str_c(.genotypeStem(bed), ".bim") `colnames<-`( vroom(bimf, col_names = FALSE), c("chrom", "id", "gpos", "pos", "a1", "a0") ) } -#' @importFrom vroom vroom -#' @importFrom tools file_path_sans_ext -readFam <- function(bed) { - famf <- str_c(file_path_sans_ext(bed), ".fam") - return(vroom(famf, col_names = FALSE)) -} - -# open bed/bim/fam: A PLINK 1 .bed is a valid .pgen -openBed <- function(bed) { - if (!requireNamespace("pgenlibr", quietly = TRUE)) { - abort("Package 'pgenlibr' is required for this function.") - } - rawSCt <- nrow(readFam(bed)) - return(pgenlibr::NewPgen(bed, raw_sample_ct = rawSCt)) -} - #' Read a PLINK2 allele frequency file (.afreq or .afreq.zst) #' #' @param prefix File prefix (without .afreq extension). @@ -1103,19 +982,16 @@ readStochasticMeta <- function(path, format = NULL) { #' Search for a stochastic genotype sidecar file alongside a genotype path. #' #' Looks for \code{.afreq}, \code{.afreq.zst}, and \code{.stochastic_meta.tsv} -#' files next to the given genotype path. For extension-based paths (VCF, GDS), -#' the extension is stripped first. For prefix-based paths (PLINK1/2), the -#' prefix is used directly. +#' files next to the given genotype path. For extension-based paths (VCF, GDS, +#' BGEN, or a PLINK file), the extension is stripped first. For prefix-based +#' paths (PLINK1/2), the prefix is used directly. #' #' @param genotypePath Path to the genotype data (prefix or file path). #' @return Path to the first sidecar file found, or \code{NULL}. #' @noRd findStochasticMeta <- function(genotypePath) { # Strip known genotype extensions to get the stem - stem <- str_remove( - genotypePath, - "\\.(vcf|vcf\\.gz|bcf|gds|bed|bim|fam|pgen|pvar|psam)$" - ) + stem <- .genotypeStem(genotypePath) candidates <- c( str_c(stem, ".afreq"), str_c(stem, ".afreq.zst"), @@ -1125,7 +1001,6 @@ findStochasticMeta <- function(genotypePath) { if (length(found) > 0) found[1] else NULL } - #' Invert min-max [0,2] scaling to recover the original U matrix #' #' Stochastic genotype data is stored after min-max scaling: U_scaled = 2 * (U - @@ -1207,64 +1082,13 @@ readPvar <- function(pvarPath) { ) } -# PLINK1 .bim files come in a 6-column form and a 9-column extended form; -# the column meanings are positional, so the width selects the header. -# @noRd -.plinkVariantColnames <- function(n, snpFilePath) { - if (n == 6) { - return(c("chrom", "id", "gpos", "pos", "A1", "A2")) - } - if (n == 9) { - return(c( - "chrom", - "id", - "gpos", - "pos", - "A1", - "A2", - "variance", - "allele_freq", - "n_nomiss" - )) - } - abort(glue( - "Unexpected number of columns ({n}) in variant file: ", - "{snpFilePath}" - )) -} - -#' Read variant metadata from either .bim or .pvar/.pvar.zst file. -#' -#' Auto-detects the format by extension and header, then returns a standardized -#' data.frame. For PLINK1 .bim files, assigns column names based on the number -#' of columns (6 or 9). For PLINK2 .pvar files, delegates to \code{readPvar()}. -#' -#' @param snpFilePath Path to .bim, .pvar, or .pvar.zst file. -#' @return data.frame with at minimum columns: chrom, id, pos, A2, A1. Extended -#' .bim files (9 columns) also include: variance, allele_freq, n_nomiss. -#' @importFrom readr read_table cols col_character -#' @noRd -readVariantMetadata <- function(snpFilePath) { - # Extension first, then the header line: a .pvar written without its - # extension still announces itself with a #CHROM header. - byExtension <- str_detect(snpFilePath, "\\.(pvar|pvar\\.zst)$") - isPvar <- byExtension || - str_detect(read_lines(snpFilePath, n_max = 1), "^#CHROM") - - if (isPvar) { - readPvar(snpFilePath) - } else { - df <- read_table(snpFilePath, col_names = FALSE, col_types = cols()) - `names<-`(df, .plinkVariantColnames(ncol(df), snpFilePath)) - } -} - #' Get variant information from any LD reference source #' -#' Auto-detects the source type (PLINK2, PLINK1, VCF, GDS, or pre-computed LD -#' metadata) and returns variant metadata. For PLINK2, opens only the .pvar -#' file. For PLINK1, reads only the .bim file. For VCF and GDS, loads the full -#' file and extracts variant info. +#' Auto-detects the source type (PLINK2, PLINK1, VCF, GDS or BGEN, given +#' directly or through LD metadata whose rows point at genotype files) and +#' returns variant metadata. For PLINK2, opens only the .pvar file. For PLINK1, +#' reads only the .bim file. For VCF, GDS and BGEN, loads the full file and +#' extracts variant info. #' #' @param source Genotype file path/prefix or LD metadata file path. #' @param region Region of interest: "chr:start-end" string or data.frame with @@ -1285,10 +1109,8 @@ refVariantInfo <- function(source, region = NULL) { info <- .refInfoPlink2(dataPath) } else if (resolved$type == "plink1") { info <- .refInfoPlink1(dataPath) - } else if (is_in(resolved$type, c("vcf", "gds"))) { - return(.refInfoVcfGds(dataPath, region)) } else { - return(.refInfoPrecomputedLd(resolved, region)) + return(.refInfoVcfGds(dataPath, region)) } # Region filter for plink2 / plink1 (the loader-based paths self-filter). .refRegionFilter(info, region) @@ -1298,7 +1120,10 @@ refVariantInfo <- function(source, region = NULL) { # else the resolved data path. # @noRd .refDataPath <- function(resolved, region) { - usesMeta <- is_in(resolved$type, c("plink2", "plink1", "vcf", "gds")) && + usesMeta <- is_in( + resolved$type, + c("plink2", "plink1", "vcf", "gds", "bgen") + ) && !is.null(resolved$metaPath) && !is.null(region) if (usesMeta) { @@ -1351,42 +1176,6 @@ refVariantInfo <- function(source, region = NULL) { ) } -# Pre-computed LD variant info: read the per-intersection bim/pvar metadata -# (already region-filtered by regionalLdMeta). -# @noRd -.refInfoPrecomputedLd <- function(resolved, region) { - bimPaths <- regionalLdMeta( - resolved$metaPath, - region - )$intersections$bimFilePaths - bind_rows(map(bimPaths, .refReadBimMeta)) |> - mutate(id = normalizeVariantId(.data$id)) -} - -# Read one bim/pvar metadata file into a canonical variant-info data.frame, -# carrying the optional variance / allele_freq / n_nomiss columns when present. -# @noRd -.refReadBimMeta <- function(path) { - df <- readVariantMetadata(path) - out <- tibble( - chrom = df$chrom, - id = df$id, - pos = df$pos, - A2 = df$A2, - A1 = df$A1 - ) - optional <- intersect(c("variance", "allele_freq", "n_nomiss"), names(df)) - mutate( - out, - !!!set_names(map(optional, .refMetaColumn, df = df), optional) - ) -} - -# @noRd -.refMetaColumn <- function(col, df) { - df[[col]] -} - # Filter plink2 / plink1 variant info to the requested region. # @noRd .refRegionFilter <- function(info, region) { @@ -1480,17 +1269,16 @@ matchVariantsToKeep <- function(variantInfo, keepVariantsPath) { } } - #' Load genotype data for a specific region #' #' Auto-detects PLINK2 (.pgen/.pvar[.zst]/.psam), PLINK1 (.bed/.bim/.fam), VCF -#' (.vcf/.vcf.gz/.bcf), or GDS (.gds) format and loads genotype data via -#' \code{\link{readGenotypes}} and \code{\link{extractBlockGenotypes}}. If a -#' stochastic genotype sidecar file (.afreq or .stochastic_meta.tsv) is found -#' alongside the genotype file, non-integer dosages are automatically rescaled -#' using the stored U_MIN/U_MAX values. +#' (.vcf.gz/.vcf.bgz, bgzipped with a tabix index), GDS (.gds) or BGEN (.bgen) +#' format, reads the file as a panel with \code{\link{readGenotypes}} and +#' loads the region's dosages. If a stochastic genotype sidecar file (.afreq +#' or .stochastic_meta.tsv) is found alongside the genotype file, non-integer +#' dosages are automatically rescaled using the stored U_MIN/U_MAX values. #' -#' @param genotype Path to the genotype data file (without extension). +#' @param genotype Path to the genotype data file, or a PLINK prefix. #' @param region The target region in the format "chr:start-end". #' @param keepIndel Whether to keep indel SNPs. #' @param keepVariantsPath Path to a file listing variants to keep. @@ -1521,15 +1309,22 @@ loadGenotypeRegion <- function( stochasticMetaPath = NULL, stochasticMetaFormat = NULL ) { - handle <- .loadGenoHandle(genotype) - handleSnpInfo <- snpInfo(handle) - snpIdx <- .loadGenoSnpIdx(handleSnpInfo, region) - # Samples x variants matrix (pecotmr convention); callers handle missing. + panel <- .readGenotypePanel( + genotype, + format = .requireGenotypeFormat(genotype, "loadGenotypeRegion") + ) + variants <- .rangesSnpInfo(SummarizedExperiment::rowRanges(panel)) + snpIdx <- .loadGenoSnpIdx(variants, region) + # Samples x variants matrix (pecotmr convention), its columns named by + # the file's own variant ids; callers handle missing. extracted <- list( - X = .dosageMatrix(handle, snpIdx, meanImpute = FALSE), + X = `colnames<-`( + .ldRefDosage(panel, snpIdx, meanImpute = FALSE), + variants$SNP[snpIdx] + ), variant_info = .loadGenoAttachAfreq( - handle, - .snpInfoToVariantInfo(slice(handleSnpInfo, snpIdx)) + panel, + .snpInfoToVariantInfo(slice(variants, snpIdx)) ) ) filtered <- .loadGenoPostFilter(extracted, keepIndel, keepVariantsPath) @@ -1542,37 +1337,13 @@ loadGenotypeRegion <- function( if (returnVariantInfo) result else result$X } -# Detect the genotype file format and open the matching GenotypeHandle. -# @noRd -.loadGenoHandle <- function(genotype) { - if (str_detect(genotype, "\\.(vcf|vcf\\.gz|bcf)$")) { - return(.readGenotypeHandle(genotype, format = "vcf")) - } - if (str_detect(genotype, "\\.gds$")) { - return(.readGenotypeHandle(genotype, format = "gds")) - } - if (hasPlink2Files(genotype)) { - return(.readGenotypeHandle(genotype, format = "plink2")) - } - if (hasPlink1Files(genotype)) { - return(.readGenotypeHandle(genotype, format = "plink1")) - } - msg <- glue( - "Genotype files not found at: {genotype}\n", - " Expected: .vcf/.vcf.gz/.bcf, .gds, or PLINK prefix ", - "(.pgen/.pvar[.zst]/.psam or .bed/.bim/.fam)", - .trim = FALSE - ) - abort(msg) -} - # Resolve the SNP indices for a region (all variants when region is NULL). # @noRd -.loadGenoSnpIdx <- function(handleSnpInfo, region) { +.loadGenoSnpIdx <- function(variants, region) { if (is.null(region)) { - return(seq_len(nrow(handleSnpInfo))) + return(seq_len(nrow(variants))) } - snpIdx <- .regionToSnpIdx(handleSnpInfo, region) + snpIdx <- .regionToSnpIdx(variants, region) if (length(snpIdx) == 0) { msg <- glue("No SNPs found in the specified region {region}") abort(msg, class = "NoSnpsError") @@ -1583,11 +1354,11 @@ loadGenotypeRegion <- function( # Attach allele frequency from the .afreq sidecar (plink2 only). #' @importFrom dplyr left_join #' @noRd -.loadGenoAttachAfreq <- function(handle, variantInfo) { - if (genotypeFormat(handle) != "plink2") { +.loadGenoAttachAfreq <- function(panel, variantInfo) { + if (!identical(.ldRefFormat(panel), "plink2")) { return(variantInfo) } - afreq <- readAfreq(.genotypeReadPath(handle)) + afreq <- readAfreq(.ldRefReadPath(panel)) if (is.null(afreq)) { return(variantInfo) } @@ -1631,8 +1402,10 @@ loadGenotypeRegion <- function( ) } -# Detect stochastic genotype scaling and restore the original scale from a -# metadata sidecar; warn (only) when non-integer dosages have no sidecar. +# Restore a stochastic sketch's original scale from its metadata sidecar. +# Without one the dosages are taken as they are: a dosage need not be a whole +# number (imputed BGEN, VCF FORMAT/DS, PLINK2 dosages), so whether the values +# are whole says nothing about whether the file is a sketch. # @noRd .loadGenoInvertStochastic <- function( result, @@ -1642,7 +1415,6 @@ loadGenotypeRegion <- function( ) { metaPath <- stochasticMetaPath %||% findStochasticMeta(genotype) if (is.null(metaPath)) { - .loadGenoWarnStochastic(result$X) return(result) } smeta <- readStochasticMeta(metaPath, format = stochasticMetaFormat) @@ -1681,45 +1453,14 @@ loadGenotypeRegion <- function( restored } -# Warn when non-integer dosages are present but no stochastic sidecar was found. -# @noRd -.loadGenoWarnStochastic <- function(X) { - if (all(X == round(X), na.rm = TRUE)) { - return(invisible(NULL)) - } - msg <- glue( - "Non-integer genotype values detected but no stochastic metadata ", - "sidecar found. Place a .afreq or .stochastic_meta.tsv file with ", - "u_min/u_max columns alongside the genotype files to restore the ", - "original scale." - ) - warn(msg) -} - # ---- map/apply helpers (lambda-free callbacks) --------------------------- -# The first ALT allele of a variant (as character). -# @noRd -.gtFirstAllele <- function(x) { - as.character(x)[1] -} - # The dosage assay matrix of one per-chromosome SummarizedExperiment. # @noRd .seDosage <- function(se) { SummarizedExperiment::assay(se, "dosage") } -# ALT (A1) dosage from a VCF GT string; NA for missing ("./." or NA). -# @noRd -.gtStringToDosage <- function(x) { - if (is.na(x) || x == "./.") { - return(NA_real_) - } - alleles <- str_split(x, "[/|]")[[1L]] - sum(alleles != "0") -} - # Variant `i`'s chromosome from an open pvar handle. # @noRd .pvarChrom <- function(i, pvar) { @@ -1744,7 +1485,7 @@ loadGenotypeRegion <- function( pgenlibr::GetAlleleCode(pvar, i, k) } -# ---- .withGds resource-body functions (run inside the open-GDS bracket) --- +# ---- GDS resource-body functions (run inside an open-GDS bracket) --------- # The sample ids stored in an open GDS. # @noRd @@ -1752,42 +1493,26 @@ loadGenotypeRegion <- function( as.character(gdsfmt::read.gdsn(gdsfmt::index.gdsn(gds, "sample.id"))) } -# The dosage matrix for `handle`'s `snpIdx` variants (NULL when none selected). -# snpgdsGetGeno counts the first allele (labelled A1 in .gdsSnpInfo), so no flip -# is needed; it also handles non-contiguous SNP selection. +# The sample-LD matrix of the SNPs `snpIds` (NA correlations -> 0). # @noRd -.gdsBlockGeno <- function(gds, handle, snpIdx) { - snpIds <- snpInfo(handle)$SNP[snpIdx] - geno <- SNPRelate::snpgdsGetGeno( - gds, - snp.id = snpIds, - with.id = FALSE, - verbose = FALSE - ) - if (is.null(geno) || length(geno) == 0) { - return(NULL) - } - `storage.mode<-`(geno, "double") -} - -# The sample-LD matrix for `handle`'s `snpIdx` variants (NA correlations -> 0). -# @noRd -.gdsBlockLd <- function(gds, handle, snpIdx) { +.gdsBlockLd <- function(gds, snpIds) { # snpgdsLDMat() returns variants in FILE order whatever order snp.id is - # given in, and labels nothing. Ask in ascending order and permute back, - # exactly as the block readers do, so the matrix agrees with its names. - ord <- order(.genotypeFilePos(handle, snpIdx)) - snpIds <- snpInfo(handle)$SNP[snpIdx[ord]] + # given in, and labels nothing. Ask in file order and permute back, so the + # matrix agrees with its names; the file's own ids are passed, since a + # GDS may store them as integers. + fileIds <- gdsfmt::read.gdsn(gdsfmt::index.gdsn(gds, "snp.id")) + pos <- match(as.character(snpIds), as.character(fileIds)) + ord <- order(pos) ldMat <- SNPRelate::snpgdsLDMat( gds, - snp.id = snpIds, + snp.id = fileIds[pos[ord]], method = "corr", slide = -1, verbose = FALSE ) raw <- replace(ldMat$LD, is.na(ldMat$LD), 0) inv <- order(ord) - ids <- snpInfo(handle)$SNP[snpIdx] + ids <- as.character(snpIds) `dimnames<-`(raw[inv, inv, drop = FALSE], list(ids, ids)) } @@ -1811,3 +1536,992 @@ loadGenotypeRegion <- function( A2 = a2 ) } + +# ============================================================================= +# BGEN genotype reading +# ----------------------------------------------------------------------------- +# A "bgen" GenotypeHandle reads a BGEN file lazily, without converting it: +# its variants come from the .bgi index (bgenix's SQLite database), its +# samples from the file's sample block or a .sample file beside it, and a +# block's dosages are read record by record at the offsets the index gives. +# +# Every revision is read: layout 1 (BGEN 1.1: diploid, biallelic, three +# 16-bit probabilities per sample, zlib or no compression) and layout 2 (BGEN +# 1.2 and 1.3: phased or unphased, any ploidy, any bit depth, zlib, zstd or +# no compression). A record with more than two alleles becomes one variant +# per non-first allele. The dosage is the expected count of A1, the variant's +# second (or later) allele, with the first as A2, as bgen's own tools count +# it: from 0 to the sample's ploidy. +# ============================================================================= + +# The handle of a BGEN file. Only its header, its index and its sample ids +# are read here. +# @noRd +#' @importFrom checkmate assertFileExists +.makeBgenHandle <- function(path) { + walk(c("DBI", "RSQLite"), .bgenRequirePackage) + assertFileExists(path, access = "r", .var.name = "BGEN file") + path <- normalizePath(path) + header <- .bgenReadHeader(path, withSamples = TRUE) + sampleIds <- header$sampleIds %||% .bgenSampleFileIds(path) + if (length(sampleIds) != header$nSamples) { + abort(glue( + "BGEN file {path} declares {header$nSamples} samples but ", + "{length(sampleIds)} sample ids were found." + )) + } + new( + "GenotypeHandle", + path = path, + format = "bgen", + snpInfo = .withFileIdx(.bgenSnpInfo(path)), + nSamples = as.integer(header$nSamples), + sampleIds = sampleIds, + pgenPtr = NULL + ) +} + +# @noRd +.bgenRequirePackage <- function(pkg) { + if (!requireNamespace(pkg, quietly = TRUE)) { + abort(glue( + "Package '{pkg}' is required for reading BGEN files, whose .bgi ", + "index is an SQLite database." + )) + } + invisible(NULL) +} + +# A little-endian unsigned integer from raw bytes, as a double (a uint32 can +# exceed R's integer range). +# @noRd +.bgenUint <- function(bytes) { + sum(as.numeric(bytes) * 256^(seq_along(bytes) - 1L)) +} + +# The header block: the sample count, the compression and layout flags and, +# when the file stores them and they are asked for, the sample ids. An +# unknown layout or compression is refused here, before any variant is +# read. +# @noRd +.bgenReadHeader <- function(path, withSamples = FALSE) { + con <- file(path, "rb") + on.exit(close(con)) + fixed <- readBin(con, "raw", 20L) + if (length(fixed) < 20L) { + abort(glue("{path} is too short to be a BGEN file.")) + } + headerLength <- .bgenUint(fixed[5:8]) + magic <- fixed[17:20] + if (!identical(magic, charToRaw("bgen")) && any(magic != as.raw(0L))) { + abort(glue("{path} is not a BGEN file (no 'bgen' magic number).")) + } + readBin(con, "raw", headerLength - 20) + flags <- as.integer(readBin(con, "raw", 4L)) + compression <- bitwAnd(flags[[1L]], 3L) + layout <- bitwAnd(bitwShiftR(flags[[1L]], 2L), 15L) + .bgenCheckFlags(path, compression, layout) + nSamples <- .bgenUint(fixed[13:16]) + hasIds <- bitwAnd(flags[[4L]], 128L) > 0L + list( + nSamples = nSamples, + compression = compression, + layout = layout, + sampleIds = if (withSamples && hasIds) { + .bgenReadSampleBlock(con, nSamples) + } + ) +} + +# @noRd +.bgenCheckFlags <- function(path, compression, layout) { + if (!is_in(layout, 1:2)) { + abort(glue( + "{path} has BGEN layout {layout}; the spec defines layouts 1 and 2." + )) + } + if (compression > 2L) { + abort(glue("{path} has an unknown BGEN compression ({compression}).")) + } + if (layout == 1L && compression == 2L) { + abort(glue( + "{path} is a layout-1 (BGEN 1.1) file compressed with zstd, which ", + "the spec does not allow." + )) + } + invisible(NULL) +} + +# The sample identifier block, which follows the header: its length, the +# sample count again, then each id as a 2-byte length and its bytes. +# @noRd +.bgenReadSampleBlock <- function(con, nSamples) { + blockLength <- .bgenUint(readBin(con, "raw", 4L)) + block <- readBin(con, "raw", blockLength - 4) + if (.bgenUint(block[1:4]) != nSamples) { + abort("The BGEN sample block's sample count disagrees with its header.") + } + bgenSampleIdsCpp(block[-(1:4)], as.integer(nSamples)) +} + +# The sample ids of a BGEN file that does not store them, from the .sample +# file beside it (`.sample`): the first column, after the header line +# and the line of column types. +# @noRd +#' @importFrom readr read_table cols col_character +.bgenSampleFileIds <- function(path) { + samplePath <- str_c(.genotypeStem(path), ".sample") + if (!file.exists(samplePath)) { + abort(c( + glue("{path} stores no sample ids, and {samplePath} is missing."), + i = "Put the Oxford-format .sample file beside the .bgen file." + )) + } + tbl <- read_table( + samplePath, + col_types = cols(.default = col_character()), + progress = FALSE + ) + as.character(tbl[[1L]][-1L]) +} + +# The variant table: one row per (record, non-first allele), in file order, +# with each record's offset and length from the .bgi index and the allele the +# row reads. Built column-wise for the biallelic records, which are nearly +# all of them; only a multi-allelic record is read to learn its alleles. +# @noRd +#' @importFrom dplyr arrange bind_rows +#' @importFrom purrr pmap +.bgenSnpInfo <- function(path) { + index <- .bgenReadIndex(str_c(path, ".bgi")) + multi <- index$number_of_alleles > 2L + rows <- .bgenBiallelicRows(index[!multi, ]) + if (!any(multi)) { + return(rows) + } + warn(glue( + "{sum(multi)} BGEN record(s) of {path} carry more than two alleles; ", + "each is read as one biallelic variant per non-first allele." + )) + split <- pmap( + list( + chrom = index$chromosome[multi], + pos = index$position[multi], + alleles = .bgenRecordAlleles( + path, + index$file_start_position[multi], + index$size_in_bytes[multi] + ), + offset = index$file_start_position[multi], + size = index$size_in_bytes[multi] + ), + .bgenSplitRecordRows + ) + arrange( + bind_rows(rows, list_rbind(split)), + .data$bgen_offset, + .data$bgen_allele + ) +} + +# The rows of the biallelic records: A2 the first allele, A1 the second. A +# record keeps its rsid as its id when it has one, else is named +# chr:pos:A2:A1. +# @noRd +.bgenBiallelicRows <- function(index) { + ids <- formatVariantId( + index$chromosome, + index$position, + index$allele1, + index$allele2 + ) + named <- !is.na(index$rsid) & nzchar(index$rsid) & index$rsid != "." + tibble( + SNP = if_else(named, index$rsid, ids), + CHR = canonChrom(index$chromosome), + BP = as.integer(index$position), + A1 = index$allele2, + A2 = index$allele1, + bgen_offset = as.numeric(index$file_start_position), + bgen_size = as.numeric(index$size_in_bytes), + bgen_allele = rep(2L, nrow(index)) + ) +} + +# A multi-allelic record's rows: A2 its first allele, A1 each other allele +# in turn, every row named chr:pos:A2:A1 so they stay distinct. +# @noRd +.bgenSplitRecordRows <- function(chrom, pos, alleles, offset, size) { + others <- alleles[-1L] + tibble( + SNP = formatVariantId(chrom, pos, alleles[[1L]], others), + CHR = canonChrom(chrom), + BP = as.integer(pos), + A1 = others, + A2 = alleles[[1L]], + bgen_offset = as.numeric(offset), + bgen_size = as.numeric(size), + bgen_allele = seq_along(others) + 1L + ) +} + +# The .bgi index's Variant table, in file order. +# @noRd +.bgenReadIndex <- function(bgiPath) { + if (!file.exists(bgiPath)) { + abort(c( + str_c( + "A BGEN file is read through its .bgi index: ", + bgiPath, + " is missing." + ), + i = "Build it with bgenix (`bgenix -g .bgen -index`)." + )) + } + con <- DBI::dbConnect( + RSQLite::SQLite(), + bgiPath, + flags = RSQLite::SQLITE_RO + ) + on.exit(DBI::dbDisconnect(con)) + as_tibble(DBI::dbGetQuery( + con, + str_c( + "SELECT chromosome, position, rsid, number_of_alleles, allele1, ", + "allele2, file_start_position, size_in_bytes FROM Variant ", + "ORDER BY file_start_position" + ) + )) +} + +# The alleles of the records at `offsets`, read from their identifying data: +# the .bgi index holds only the first two. +# @noRd +.bgenRecordAlleles <- function(path, offsets, sizes) { + con <- file(path, "rb") + on.exit(close(con)) + map2(offsets, sizes, .bgenReadRecordAlleles, con = con) +} + +# Only a layout-2 record can carry more than two alleles, so only one is +# parsed here. +# @noRd +.bgenReadRecordAlleles <- function(offset, size, con) { + record <- .bgenReadRecord(con, offset, size) + .bgenParseIdentifying(record, layout = 2L)$alleles +} + +# @noRd +.bgenReadRecord <- function(con, offset, size) { + seek(con, where = offset) + readBin(con, "raw", size) +} + +# A record's identifying data: its alleles, and where its genotype block +# starts (1-based). A layout-1 record opens with its sample count and has two +# alleles without saying so; a layout-2 one states its allele count. +# @noRd +.bgenParseIdentifying <- function(record, layout) { + at <- if (layout == 1L) 5L else 1L + for (field in 1:3) { + # variant id, rsid, chromosome: a 2-byte length and the bytes + at <- at + 2L + .bgenUint(record[at:(at + 1L)]) + } + at <- at + 4L + nAlleles <- 2L + if (layout == 2L) { + nAlleles <- .bgenUint(record[at:(at + 1L)]) + at <- at + 2L + } + alleles <- character(nAlleles) + for (k in seq_len(nAlleles)) { + len <- .bgenUint(record[at:(at + 3L)]) + alleles[[k]] <- rawToChar(record[(at + 4L):(at + 3L + len)]) + at <- at + 4L + len + } + list(alleles = alleles, genotypeStart = at) +} + +# The samples x variants dosages of the handle's variants at `snpIdx`: each +# record read once, its probabilities decompressed and decoded for the +# alleles its rows ask about. +# @noRd +.extractBlockBgen <- function(handle, snpIdx) { + si <- slice(snpInfo(handle), snpIdx) + path <- .genotypeReadPath(handle) + header <- .bgenReadHeader(path) + con <- file(path, "rb") + on.exit(close(con)) + offsets <- unique(si$bgen_offset) + blocks <- map( + offsets, + .bgenRecordDosage, + si = si, + con = con, + header = header, + nSamples = handle@nSamples + ) + out <- exec(cbind, !!!blocks) + # The blocks hold the rows grouped by record; put them back in `si`'s + # order. + rowOrder <- list_c(map(offsets, .bgenRowsOf, si = si)) + out[, order(rowOrder), drop = FALSE] +} + +# @noRd +.bgenRowsOf <- function(offset, si) { + which(si$bgen_offset == offset) +} + +# One record's dosage columns, for the rows of `si` that read it. +# @noRd +.bgenRecordDosage <- function(offset, si, con, header, nSamples) { + rows <- .bgenRowsOf(offset, si) + record <- .bgenReadRecord(con, offset, si$bgen_size[[rows[[1L]]]]) + if (header$layout == 1L) { + return(.bgenLayout1Dosage(record, header$compression, nSamples)) + } + probabilities <- .bgenProbabilityData(record, header$compression) + bgenDosageCpp( + probabilities, + as.integer(nSamples), + as.integer(si$bgen_allele[rows]) + ) +} + +# A layout-1 record's dosage column: per sample three 16-bit probabilities, +# of the genotypes AA, AB and BB, each out of 32768, zlib-compressed or +# stored as they are. The second allele's expected count is P(AB) + 2 P(BB); +# a sample whose three are all zero is missing. +# @noRd +.bgenLayout1Dosage <- function(record, compression, nSamples) { + at <- .bgenParseIdentifying(record, layout = 1L)$genotypeStart + nBytes <- 6 * nSamples + data <- if (compression == 0L) { + record[at:(at + nBytes - 1)] + } else { + stored <- .bgenUint(record[at:(at + 3L)]) + memDecompress(record[(at + 4L):(at + 3L + stored)], type = "gzip") + } + if (length(data) != nBytes) { + abort("A layout-1 BGEN record holds the wrong amount of data.") + } + p <- matrix( + readBin( + data, + "integer", + n = 3 * nSamples, + size = 2L, + signed = FALSE, + endian = "little" + ) / + 32768, + nrow = 3L + ) + dosage <- p[2L, ] + 2 * p[3L, ] + matrix(replace(dosage, colSums(p) == 0, NA_real_), ncol = 1L) +} + +# A layout-2 record's genotype probability data, uncompressed. +# @noRd +.bgenProbabilityData <- function(record, compression) { + at <- .bgenParseIdentifying(record, layout = 2L)$genotypeStart + stored <- .bgenUint(record[at:(at + 3L)]) + if (compression == 0L) { + return(record[(at + 4L):(at + 3L + stored)]) + } + uncompressed <- .bgenUint(record[(at + 4L):(at + 7L)]) + payload <- record[(at + 8L):(at + 3L + stored)] + .bgenUncompress(payload, compression, uncompressed) +} + +# zlib through base R; zstd through archive, since R need not be built with +# it. +# @noRd +#' @importFrom archive archive_read +.bgenUncompress <- function(payload, compression, uncompressed) { + if (compression == 1L) { + return(memDecompress(payload, type = "gzip")) + } + con <- archive_read(rawConnection(payload), format = "raw", filter = "zstd") + open(con, "rb") + on.exit(close(con)) + readBin(con, "raw", uncompressed) +} + +# ============================================================================= +# VCF, GDS and PLINK1 genotype panels +# ----------------------------------------------------------------------------- +# A VCF, GDS or PLINK1 file is read as a genotype panel directly rather than +# through a GenotypeHandle: its variants and samples are read here, its +# dosages through a GenotypeDosageSeed, a block at a time, when something +# touches them. The +# panel records where it came from (metadata(panel)$genotypeSource), which is +# what anything needing the file reads -- a stochastic sketch's .afreq +# sidecar, the per-chromosome files of a manifest. +# +# A record with several ALT alleles becomes one variant per ALT allele, with a +# warning, its dosage the count of that allele, as for BGEN; a record with no +# ALT allele holds no variant. The dosage is FORMAT/DS where the file declares +# one, else the count from the GT calls. A SNPRelate GDS stores genotype codes +# counting the first of a SNP's two alleles, which is therefore A1, as +# SNPRelate reads it. A PLINK1 .bed's codes are read through PlinkMatrix as +# the count of A1, the .bim's fifth column. +# ============================================================================= + +# @noRd +.requireReaderPackage <- function(pkg, format) { + if (!requireNamespace(pkg, quietly = TRUE)) { + abort(glue("Package '{pkg}' is required for reading {format} files.")) + } + invisible(NULL) +} + +# The panel of a VCF, GDS or PLINK1 file, without the record of a stochastic +# sketch's sidecar (.withSourceProjection() adds it, once the panel is whole). +# @noRd +.readDosagePanel <- function(path, format, vcfArgs = list()) { + switch( + format, + vcf = .vcfPanel(path, vcfArgs), + gds = .gdsPanel(path), + plink1 = .plinkBedPanel(path) + ) +} + +# A panel over a GenotypeDosageSeed: its rows' variants as rowRanges, shaped +# as .genotypeExperiment() shapes a handle's, and where it came from. +# @noRd +.dosageSeedPanel <- function(seed, rows, format, path) { + dosage <- DelayedArray::DelayedArray(seed) + SummarizedExperiment::SummarizedExperiment( + assays = list(dosage = dosage), + rowRanges = .snpInfoRanges(rows, rownames(dosage)), + colData = S4Vectors::DataFrame(row.names = colnames(dosage)), + metadata = list( + genotypeSource = list( + format = format, + path = path, + chromPaths = character(0) + ) + ) + ) +} + +# One row per (record, ALT allele), in file order: A2 the record's REF +# allele, A1 the ALT allele the row counts. A record keeps its own id when it +# has one and a single ALT allele; otherwise each of its rows is named +# chr:pos:A2:A1, so they stay distinct. +# @noRd +.recordVariantRows <- function(records, path, label) { + nAlt <- lengths(records$alt) + multi <- nAlt > 1L + if (any(multi)) { + warn(glue( + "{sum(multi)} {label} record(s) of {path} carry more than one ", + "ALT allele; each is read as one biallelic variant per ALT allele." + )) + } + record <- rep(seq_len(nrow(records)), nAlt) + a1 <- as.character(list_c(records$alt)) + a2 <- records$ref[record] + named <- !is.na(records$id) & !multi + tibble( + SNP = if_else( + named[record], + records$id[record], + formatVariantId(records$chrom[record], records$pos[record], a2, a1) + ), + CHR = canonChrom(records$chrom[record]), + BP = as.integer(records$pos[record]), + A1 = a1, + A2 = a2, + record = record, + allele = sequence(nAlt) + ) +} + +# ---- VCF ------------------------------------------------------------------- + +# A VCF's panel. Only its header and its records' fixed fields are read here. +# @noRd +.vcfPanel <- function(path, vcfArgs = list()) { + .requireReaderPackage("VariantAnnotation", "VCF") + file <- .vcfIndexedFile(path) + header <- VariantAnnotation::scanVcfHeader(file) + field <- .vcfDosageField(header, path(file)) + records <- .vcfRecords(file, vcfArgs) + rows <- .recordVariantRows(records, path(file), "VCF") + seed <- methods::new( + "GenotypeDosageSeed", + backend = file, + field = if (field == "DS") "vcfDs" else "vcfGt", + rowRecord = rows$record, + rowAllele = rows$allele, + records = GRanges( + seqnames = records$chrom, + ranges = IRanges(records$pos, records$end), + posRank = .positionRank(records$chrom, records$pos) + ), + dn = list( + normalizeVariantId(rows$SNP), + as.character(VariantAnnotation::samples(header)) + ) + ) + .dosageSeedPanel(seed, rows, "vcf", path(file)) +} + +# The VCF at `path` with its tabix index, through which its records are read +# a block at a time. A VCF without one cannot be read lazily; a BCF cannot be +# read at all, Rsamtools having its BCF support disabled. +# @noRd +#' @importFrom checkmate assertFileExists +.vcfIndexedFile <- function(path) { + assertFileExists(path, access = "r", .var.name = "VCF file") + path <- normalizePath(path) + if (str_ends(str_to_lower(path), "\\.bcf")) { + abort(c( + glue("{path} is a BCF file, which pecotmr cannot read."), + i = str_c( + "Rsamtools ships with its BCF support disabled; convert it ", + "to a bgzipped, tabix-indexed VCF." + ) + )) + } + index <- str_c(path, ".tbi") + if (!file.exists(index)) { + abort(c( + glue( + "A VCF is read a block at a time through its tabix index, ", + "and {index} is missing." + ), + i = str_c( + "Compress the VCF with bgzip and index it, e.g. with ", + "Rsamtools::bgzip() and Rsamtools::indexTabix(format = 'vcf')." + ) + )) + } + VariantAnnotation::VcfFile(path, index = index) +} + +# The FORMAT field the dosages are read from: DS when the header declares +# it, with one value per ALT allele (Number=A) or one per record (Number=1), +# else GT. +# @noRd +.vcfDosageField <- function(header, path) { + declared <- VariantAnnotation::geno(header) + fields <- rownames(declared) + if (is_in("DS", fields)) { + number <- declared$Number[match("DS", fields)] + if (!is_in(number, c("A", "1"))) { + abort(glue( + "{path} declares FORMAT/DS with Number={number}; a dosage ", + "has one value per ALT allele (Number=A) or one per record ", + "(Number=1)." + )) + } + return("DS") + } + if (is_in("GT", fields)) { + return("GT") + } + abort(glue( + "{path} declares neither FORMAT/DS nor FORMAT/GT, so it holds no ", + "genotypes to read." + )) +} + +# One row per record of the VCF, in file order: its position and the end of +# its REF allele, its own ID (NA where it has none), its REF allele and its +# ALT alleles. +# @noRd +.vcfRecords <- function(file, vcfArgs) { + vcf <- exec( + VariantAnnotation::readVcf, + file, + param = VariantAnnotation::ScanVcfParam( + fixed = "ALT", + info = NA, + geno = NA + ), + !!!vcfArgs + ) + rr <- SummarizedExperiment::rowRanges(vcf) + chrom <- as.character(seqnames(rr)) + pos <- as.integer(start(rr)) + ref <- as.character(rr$REF) + alt <- as.list(methods::as(rr$ALT, "CharacterList")) + tibble( + chrom = chrom, + pos = pos, + end = as.integer(end(rr)), + id = .vcfRecordIds(names(rr), chrom, pos, ref, alt), + ref = ref, + alt = map(alt, .vcfAltAlleles) + ) +} + +# Each record's own ID, or NA where it has none. VariantAnnotation names a +# record whose ID is "." from the line's own text, CHROM:POS_REF/ALT, and +# that name is no ID of the record's. +# @noRd +.vcfRecordIds <- function(ids, chrom, pos, ref, alt) { + if (is.null(ids)) { + return(rep(NA_character_, length(chrom))) + } + altText <- map_chr(alt, str_flatten, collapse = ",") + synthetic <- str_c( + chrom, + ":", + pos, + "_", + ref, + "/", + if_else(altText == "", ".", altText) + ) + if_else(ids == synthetic | ids == ".", NA_character_, ids) +} + +# A record's ALT alleles; a record whose ALT is "." has none. +# @noRd +.vcfAltAlleles <- function(alt) { + alt[!is_in(alt, c("", "."))] +} + +# Each record's rank among the records starting at its position, in file +# order: 1 for nearly all of them. +# @noRd +.positionRank <- function(chrom, pos) { + tibble(chrom = chrom, pos = pos) |> + mutate(rank = row_number(), .by = all_of(c("chrom", "pos"))) |> + pull("rank") +} + +# ---- GDS ------------------------------------------------------------------- + +# A GDS file's panel: a SNPRelate (SNP_ARRAY) or SeqArray (SEQ_ARRAY) file, +# told apart by the format its root node records. +# @noRd +#' @importFrom checkmate assertFileExists +.gdsPanel <- function(path) { + walk(c("gdsfmt", "GDSArray"), .requireReaderPackage, format = "GDS") + assertFileExists(path, access = "r", .var.name = "GDS file") + path <- normalizePath(path) + kind <- .withGdsFile(path, .gdsFileFormat) + switch( + kind, + SNP_ARRAY = .snpGdsPanel(path), + SEQ_ARRAY = .seqArrayPanel(path), + abort(glue( + "{path} is a GDS file of format '{kind}'; genotypes are read ", + "from SNPRelate (SNP_ARRAY) and SeqArray (SEQ_ARRAY) files." + )) + ) +} + +# Resource bracket: open the GDS at `path` read-only with gdsfmt, close it on +# exit, and run `fn(gds, ...)`. A file GDSArray already holds open in this +# session is opened again rather than refused. +# @noRd +.withGdsFile <- function(path, fn, fnArgs = list()) { + gds <- gdsfmt::openfn.gds(path, readonly = TRUE, allow.duplicate = TRUE) + on.exit(gdsfmt::closefn.gds(gds)) + exec(fn, gds, !!!fnArgs) +} + +# The kind of GDS file an open GDS is, as its root node records it. +# @noRd +.gdsFileFormat <- function(gds) { + as.character(gdsfmt::get.attr.gdsn(gds$root)$FileFormat %||% "unknown") +} + +# A node of an open GDS, read whole. +# @noRd +.gdsRead <- function(gds, name) { + gdsfmt::read.gdsn(gdsfmt::index.gdsn(gds, name)) +} + +# A SNPRelate GDS's panel. +# @noRd +.snpGdsPanel <- function(path) { + meta <- .withGdsFile(path, .snpGdsMeta) + rows <- mutate( + meta$snpInfo, + SNP = as.character(.data$SNP), + CHR = canonChrom(.data$CHR), + record = row_number(), + allele = 1L + ) + backend <- DelayedArray::seed(GDSArray::GDSArray(path, "genotype")) + stored <- c(nrow(rows), length(meta$sampleIds)) + expected <- if (meta$snpFirst) stored else rev(stored) + if (!identical(as.integer(dim(backend)), as.integer(expected))) { + abort(glue( + "The genotype matrix of {path} is ", + "{str_flatten(dim(backend), ' x ')}, but the file lists ", + "{nrow(rows)} SNPs and {length(meta$sampleIds)} samples." + )) + } + seed <- methods::new( + "GenotypeDosageSeed", + backend = backend, + field = if (meta$snpFirst) "snpGdsSnpOrder" else "snpGdsSampleOrder", + rowRecord = rows$record, + rowAllele = rows$allele, + records = IRanges(rows$record, width = 1L), + dn = list(normalizeVariantId(rows$SNP), meta$sampleIds) + ) + .dosageSeedPanel(seed, rows, "gds", path) +} + +# An open SNPRelate GDS's SNPs, its samples, and how its genotype matrix is +# stored: SNP by SNP unless its node says sample.order, as SNPRelate reads +# it. +# @noRd +.snpGdsMeta <- function(gds) { + node <- gdsfmt::index.gdsn(gds, "genotype") + list( + snpInfo = .gdsReadSnpInfo(gds), + sampleIds = .gdsReadSampleIds(gds), + snpFirst = !is_in("sample.order", names(gdsfmt::get.attr.gdsn(node))) + ) +} + +# A SeqArray GDS's panel. +# @noRd +.seqArrayPanel <- function(path) { + meta <- .withGdsFile(path, .seqArrayMeta, fnArgs = list(path = path)) + rows <- .recordVariantRows(meta$records, path, "SeqArray GDS") + counts <- as.integer(meta$counts) + seed <- methods::new( + "GenotypeDosageSeed", + backend = DelayedArray::seed(GDSArray::GDSArray(path, meta$node)), + field = meta$field, + rowRecord = rows$record, + rowAllele = rows$allele, + records = IRanges(start = cumsum(counts) - counts + 1L, width = counts), + dn = list(normalizeVariantId(rows$SNP), meta$sampleIds) + ) + .dosageSeedPanel(seed, rows, "gds", path) +} + +# An open SeqArray GDS's samples and records, and the node its dosages are +# read from, with how many of that node's columns (or 2-bit planes) each +# record takes. +# @noRd +.seqArrayMeta <- function(gds, path) { + alleles <- str_split(as.character(.gdsRead(gds, "allele")), ",") + c( + list( + sampleIds = as.character(.gdsRead(gds, "sample.id")), + records = tibble( + chrom = as.character(.gdsRead(gds, "chromosome")), + pos = as.integer(.gdsRead(gds, "position")), + id = .seqArrayIds(gds, length(alleles)), + ref = map_chr(alleles, 1L), + alt = map(alleles, .seqArrayAltAlleles) + ) + ), + .seqArrayDosageNode(gds, path) + ) +} + +# Each record's own ID, or NA where it has none or the file stores none. +# @noRd +.seqArrayIds <- function(gds, nRecords) { + node <- gdsfmt::index.gdsn(gds, "annotation/id", silent = TRUE) + if (is.null(node)) { + return(rep(NA_character_, nRecords)) + } + ids <- as.character(gdsfmt::read.gdsn(node)) + if_else(is_in(ids, c("", ".")), NA_character_, ids) +} + +# A record's ALT alleles: every allele after its first, the REF. +# @noRd +.seqArrayAltAlleles <- function(alleles) { + .vcfAltAlleles(alleles[-1L]) +} + +# Where a SeqArray GDS's dosages are: its FORMAT/DS when it stores one, with +# one value per ALT allele or one per record, else its genotype calls. +# @noRd +.seqArrayDosageNode <- function(gds, path) { + ds <- gdsfmt::index.gdsn(gds, "annotation/format/DS", silent = TRUE) + if (!is.null(ds)) { + number <- as.character(gdsfmt::get.attr.gdsn(ds)$Number %||% "") + if (!is_in(number, c("A", "1"))) { + abort(glue( + "{path} stores FORMAT/DS with Number={number}; a dosage has ", + "one value per ALT allele (Number=A) or one per record ", + "(Number=1)." + )) + } + return(list( + node = "annotation/format/DS/data", + field = "seqArrayDs", + counts = .gdsRead(gds, "annotation/format/DS/@data") + )) + } + .seqArrayCheckGenotypes(gds, path) + list( + node = "genotype/data", + field = "seqArrayGt", + counts = .gdsRead(gds, "genotype/@data") + ) +} + +# The genotype calls are read from their 2-bit planes, so a file storing +# them otherwise, or storing calls beyond its ploidy apart from them, is +# refused rather than misread. +# @noRd +.seqArrayCheckGenotypes <- function(gds, path) { + data <- gdsfmt::objdesp.gdsn(gdsfmt::index.gdsn(gds, "genotype/data")) + if (!identical(data$trait, "Bit2")) { + abort(glue( + "{path} stores its genotype calls as {data$trait}; SeqArray ", + "stores them in 2-bit planes, which is what is read." + )) + } + extra <- gdsfmt::index.gdsn(gds, "genotype/extra.index", silent = TRUE) + nExtra <- if (is.null(extra)) 0L else gdsfmt::objdesp.gdsn(extra)$dim[[2L]] + if (nExtra > 0L) { + abort(glue( + "{path} stores {nExtra} genotype call(s) beyond its ploidy, ", + "which are not read." + )) + } + invisible(NULL) +} + +# ---- PLINK1 ---------------------------------------------------------------- + +# A PLINK1 fileset's panel: its .bim and .fam are read here, its genotypes a +# block at a time through PlinkMatrix (.plinkBedSeedRead()). The stem is +# recorded absolute, as a VCF's or GDS file's path is, so the panel still +# reads from another working directory. +# @noRd +.plinkBedPanel <- function(path) { + .requireReaderPackage("PlinkMatrix", "PLINK1") + named <- .plink1RequireFiles(path) + stem <- file.path(normalizePath(dirname(named)), basename(named)) + meta <- .plink1ReadMeta(stem) + nVariants <- nrow(meta$snpInfo) + seed <- methods::new( + "GenotypeDosageSeed", + backend = stem, + field = "plinkBed", + rowRecord = seq_len(nVariants), + rowAllele = rep(1L, nVariants), + records = IRanges(seq_len(nVariants), width = 1L), + dn = list(normalizeVariantId(meta$snpInfo$SNP), meta$sampleIds) + ) + .dosageSeedPanel(seed, meta$snpInfo, "plink1", stem) +} + +# Resolve the plink1 stem and assert its .bed/.bim/.fam all exist. +# @noRd +#' @importFrom checkmate assertFileExists +.plink1RequireFiles <- function(path) { + stem <- .genotypeStem(path) + assertFileExists( + str_c(stem, c(".bed", ".bim", ".fam")), + access = "r", + .var.name = "Plink file" + ) + stem +} + +# Read the .bim / .fam sidecars into snpInfo + sample metadata. Returns +# list(snpInfo, sampleIds, nSamples). +# @noRd +#' @importFrom readr read_table cols col_character +.plink1ReadMeta <- function(stem) { + # col_types all-character so readr never coerces a column: a bim whose + # allele column is uniformly "T"/"F" (all-A/T SNPs) would otherwise become + # logical, silently corrupting A1/A2 (BP is cast to integer below). + bim <- read_table( + str_c(stem, ".bim"), + col_names = c("CHR", "SNP", "CM", "BP", "A1", "A2"), + col_types = cols(.default = col_character()) + ) + fam <- read_table( + str_c(stem, ".fam"), + col_names = FALSE, + col_types = cols(.default = col_character()) + ) + # plink1 bim: col5 = A1 (minor/effect), col6 = A2 (major/ref); matches the + # pecotmr convention directly. + snpInfo <- tibble( + SNP = bim$SNP, + CHR = as.character(bim$CHR), + BP = as.integer(bim$BP), + A1 = bim$A1, + A2 = bim$A2 + ) + list( + snpInfo = snpInfo, + sampleIds = as.character(fam[[2]]), + nSamples = nrow(fam) + ) +} + +# ---- Per-chromosome VCF / GDS / PLINK1 sets -------------------------------- + +# The panel of a per-chromosome VCF, GDS or PLINK1 set: the shards' panels +# stacked -- DelayedArray binds their assays, so nothing is read -- recording +# each chromosome's file as a sharded GenotypeHandle does. Every shard must +# hold the same samples, in the same order. +# @noRd +.stackDosagePanels <- function(panels, format, metaPath, paths) { + .stackCheckSamples(panels, paths) + rowRangesList <- unname(map(panels, SummarizedExperiment::rowRanges)) + SummarizedExperiment::SummarizedExperiment( + assays = list( + dosage = exec( + BiocGenerics::rbind, + !!!unname(map(panels, .seDosage)) + ) + ), + rowRanges = suppressWarnings(exec(c, !!!rowRangesList)), + colData = SummarizedExperiment::colData(panels[[1L]]), + metadata = list( + genotypeSource = list( + format = format, + path = metaPath, + chromPaths = .panelChromPaths(panels) + ) + ) + ) +} + +# @noRd +.stackCheckSamples <- function(panels, paths) { + first <- colnames(panels[[1L]]) + same <- map_lgl(map(panels, colnames), identical, first) + if (all(same)) { + return(invisible(NULL)) + } + abort(glue( + "genoMeta: all per-chromosome files must have ", + "identical sample IDs in the same order (mismatch at ", + "'{paths[[which(!same)[[1L]]]]}')." + )) +} + +# Each chromosome's file, named by chromosome; a chromosome found in two +# files is refused, as for a sharded GenotypeHandle. +# @noRd +.panelChromPaths <- function(panels) { + chromPaths <- list_c(map(panels, .panelShardPaths)) + counts <- table(names(chromPaths)) + walk2(names(counts), as.integer(counts), .checkChromNotSplit) + chromPaths +} + +# One shard panel's chromosomes, each mapped to its file. +# @noRd +.panelShardPaths <- function(panel) { + chroms <- unique(.ldRefChrom(panel)) + set_names(rep(.ldRefSource(panel)$path, length(chroms)), chroms) +} diff --git a/R/gwasSumStats.R b/R/gwasSumStats.R index 013773065..9e488c0ca 100644 --- a/R/gwasSumStats.R +++ b/R/gwasSumStats.R @@ -4,7 +4,7 @@ # DFrame-subclass collection keyed by the identity tuple (study). Each # row holds a per-study GRanges of GWAS summary statistics covering a # single LD block; build a separate collection per block when sweeping -# the genome. Class-level slots ldSketch + genome + qcInfo apply +# the genome. Class-level slots ldReference + genome + qcInfo apply # uniformly across rows. # ============================================================================= @@ -18,7 +18,7 @@ NULL #' block when sweeping the genome. #' @details Required column: \code{study}, unique across rows. The class-level #' slots inherited from \code{\linkS4class{SumStatsBase}} -- -#' \code{ldSketch}, \code{genome} and \code{qcInfo} -- apply uniformly to +#' \code{ldReference}, \code{genome} and \code{qcInfo} -- apply uniformly to #' every row. #' @seealso \code{\link{GwasSumStats}} for the constructor and #' \code{\linkS4class{QtlSumStats}} for the QTL counterpart. @@ -28,7 +28,7 @@ setClass( "GwasSumStats", contains = "SumStatsBase", validity = function(object) { - # The ldSketch slot's class union enforces its type. + # The ldReference slot's class union enforces its type. coll <- makeAssertCollection() assertNames( colnames(mcols(object)) %||% character(0), @@ -65,13 +65,13 @@ setMethod("show", "GwasSumStats", function(object) { "genome build {GenomeInfoDb::genome(object)}\n", .trim = FALSE )) - ld <- object@ldSketch + ld <- object@ldReference ldSrc <- if (is.null(ld)) { "none (LD-free)" } else { - .ldSketchLabel(ld) + .ldRefLabel(ld) } - cat(glue(" LD sketch: {ldSrc}\n", .trim = FALSE)) + cat(glue(" LD reference: {ldSrc}\n", .trim = FALSE)) }) @@ -107,7 +107,7 @@ NULL #' @title Create a GwasSumStats Collection Object #' @description Construct a \code{GwasSumStats} S4 DFrame-subclass collection #' from per-study tuple vectors and a list of \code{GRanges} entries (one per -#' study), plus a single LD sketch handle and a single genome build that apply +#' study), plus a single LD reference and a single genome build that apply #' to the whole collection. #' #' Each \code{GRanges} entry must carry per-variant statistics in its mcols (at @@ -118,9 +118,10 @@ NULL #' study. #' @param genome Single character string giving the genome build (e.g., #' \code{"hg19"}, \code{"hg38"}). Uniform across the collection because all -#' entries share the same LD sketch. -#' @param ldSketch A genotype panel (see \code{\link{readGenotypes}}) -#' carrying the LD reference. +#' entries share the same LD reference. +#' @param ldReference The LD reference: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. #' @param varY Optional numeric vector of per-study phenotype variances #' (\code{NA_real_} entries allowed). Used by the sufficient-statistic #' interface; z-score RSS analyses should leave entries as NA. @@ -137,10 +138,9 @@ NULL #' no per-variant \code{N} column and no case/control counts. Named #' \code{nSample} to avoid clashing with \code{nSamples()} (the LD-panel #' sample size). -#' @param ldBlocks Optional LD-block specification: an \code{LdBlocks}, a -#' \code{GRanges}, a data.frame with \code{chrom}/\code{start}/\code{end} -#' (plus an optional \code{blockId}), or a path to such a table. When -#' supplied, each +#' @param ldBlocks Optional LD-block specification: a \code{GRanges}, a +#' data.frame with \code{chrom}/\code{start}/\code{end} (plus an optional +#' \code{blockId}), or a path to such a table. When supplied, each #' study's variants are split into one element per block rather than one per #' chromosome, and \code{blockId} takes the block's key (its \code{names}, #' else a \code{blockId} metadata column, else its coordinates). cTWAS needs @@ -170,13 +170,13 @@ NULL #' S4Vectors::mcols(gr) <- S4Vectors::DataFrame(SNP = paste0("rs", 1:3), #' A1 = "A", A2 = "G", Z = rnorm(3), N = 100L) #' GwasSumStats(studyName = "t1", entry = list(gr), genome = "hg38", -#' ldSketch = panel) +#' ldReference = panel) #' @export GwasSumStats <- function( studyName, entry, genome, - ldSketch = NULL, + ldReference = NULL, varY = NA_real_, nCase = NULL, nControl = NULL, @@ -217,7 +217,7 @@ GwasSumStats <- function( GenomicRanges::GRangesList(split$entry), value = md ) - .sumStatsNewValidated("GwasSumStats", grl, ldSketch, genome, qcInfo) + .sumStatsNewValidated("GwasSumStats", grl, ldReference, genome, qcInfo) } # Build and validate a SumStatsBase subclass from its GRangesList plus the @@ -225,7 +225,7 @@ GwasSumStats <- function( # rather than duplicated so the two constructors cannot drift in how they # coerce those slots. Used by qtlSumStats.R too. # @noRd -.sumStatsNewValidated <- function(Class, grl, ldSketch, genome, qcInfo) { +.sumStatsNewValidated <- function(Class, grl, ldReference, genome, qcInfo) { # The build goes into seqinfo, which is where a GRangesList keeps it and # where every Bioconductor consumer reads it from. Assigned before new() # so validity sees the finished object. @@ -233,7 +233,7 @@ GwasSumStats <- function( obj <- methods::new( Class, built, - ldSketch = .asLdSketch(ldSketch), + ldReference = .asLdRef(ldReference), qcInfo = as.list(qcInfo) ) methods::validObject(obj) @@ -302,7 +302,7 @@ GwasSumStats <- function( if (length(genome) != 1L) { msg <- glue( "`genome` must be a single character string (one build per ", - "collection, because all entries share the LD sketch)." + "collection, because all entries share the LD reference)." ) abort(msg) } @@ -525,15 +525,18 @@ setMethod("varY", "GwasSumStats", function(x, studyName = NULL) { #' across the inputs (a mismatch is an error, not a silent first-wins), the #' per-element \code{qcInfo$entryAudit} concatenates in element order so #' \code{\link{qcDiagnostics}} keeps addressing the right element, and the -#' LD sketches union into one panel over the shared genotype handle. That last -#' one matters: block-parallel pipelines narrow each piece's panel to its own -#' block, so keeping only the first would leave a multi-block collection whose -#' LD reference covers one block. +#' LD references combine: genotype panels union into one panel over the shared +#' genotype handle. That last one matters: block-parallel pipelines narrow each +#' piece's panel to its own block, so keeping only the first would leave a +#' multi-block collection whose LD reference covers one block. An +#' \code{LdMatrix} or a \code{GenotypeMixture} cannot be unioned that way; +#' one of the inputs' references must already cover the others, or the +#' combined one is passed as \code{ldReference}. #' #' @param ... Two or more \code{GwasSumStats} objects, or a single \code{list} #' of them. -#' @param ldSketch Optional genotype panel (see \code{\link{readGenotypes}}) to -#' attach to the combined collection, overriding the unioned one. Default +#' @param ldReference Optional genotype panel (see \code{\link{readGenotypes}}) +#' to attach to the combined collection, overriding the unioned one. Default #' \code{NULL}. #' @return A single combined \code{GwasSumStats}. #' @seealso \code{\link{combineQtlSumStats}}, @@ -542,7 +545,7 @@ setMethod("varY", "GwasSumStats", function(x, studyName = NULL) { #' data(gwasSumStatsS4Example) #' combineGwasSumStats(gwasSumStatsS4Example) #' @export -combineGwasSumStats <- function(..., ldSketch = NULL) { +combineGwasSumStats <- function(..., ldReference = NULL) { parts <- .asCombineList( list(...), "GwasSumStats", @@ -551,5 +554,5 @@ combineGwasSumStats <- function(..., ldSketch = NULL) { if (length(parts) == 1L) { return(parts[[1L]]) } - .rbindSumStats(parts, ldSketch, "combineGwasSumStats") + .rbindSumStats(parts, ldReference, "combineGwasSumStats") } diff --git a/R/h2Annotations.R b/R/h2Annotations.R index 72179c116..1a23f69bb 100644 --- a/R/h2Annotations.R +++ b/R/h2Annotations.R @@ -102,8 +102,9 @@ setMethod( #' @title Detect Annotation File Format #' @description Detect annotation file format from extension. This is separate -#' from \code{.h2DetectFormat} because BED annotation files (genomic intervals -#' for rtracklayer) must be distinguished from plink BED files. +#' from the genotype format detection (\code{.genotypePathFormat}) because +#' BED annotation files (genomic intervals for rtracklayer) must be +#' distinguished from plink BED files. #' @param path Character, file path. #' @return Character, one of "bigwig", "ldsc_annot", or "bed". #' @keywords internal diff --git a/R/h2EstimationWrappers.R b/R/h2EstimationWrappers.R index 6d7bfb1c7..a35a813bd 100644 --- a/R/h2EstimationWrappers.R +++ b/R/h2EstimationWrappers.R @@ -8,10 +8,10 @@ # * HDL/sHDL (High-Definition Likelihood, Ning et al. 2020 + Zhao 2023) # * LDER (LD Eigenvalue Regression, Song et al. 2022) # -# Plus shared utilities (block ops, WLS, jackknife SE, enrichment, LD -# shrinkage, genome-build checks, tauStar standardization, meta-analysis) -# and the converter that bridges H2Estimate results into the sLDSC -# postprocessing pipeline. +# Plus shared utilities (block ops, WLS, jackknife SE, enrichment, +# genome-build checks, tauStar standardization, meta-analysis) and the +# converter that bridges H2Estimate results into the sLDSC postprocessing +# pipeline. # # The H2Estimate result class itself lives in R/h2Estimate.R. # ============================================================================= @@ -22,7 +22,6 @@ #' @name pecotmr-h2-utils #' @keywords internal #' @importFrom GenomicRanges GRanges -#' @importFrom BiocParallel bplapply bpparam #' @importFrom IRanges findOverlaps #' @importFrom S4Vectors queryHits subjectHits NULL @@ -48,45 +47,6 @@ snpsPerBlock <- function(snpInfo, ldBlocks) { split(queryHits(hits), subjectHits(hits)) } -#' @title Apply Function Per Block with BiocParallel -#' @description Apply a function to each LD block in parallel. -#' @param blockIndices List of SNP index vectors per block. -#' @param FUN Function to apply to each block's indices. -#' @param BPPARAM BiocParallel parameter object. -#' @param ... Additional arguments passed to FUN. -#' @return A list of results, one per block. -#' @keywords internal -bplapplyBlocks <- function(blockIndices, FUN, BPPARAM = NULL, ...) { - if (is.null(BPPARAM)) { - BPPARAM <- bpparam() - } - bplapply(blockIndices, FUN, BPPARAM = BPPARAM, ...) -} - -# ============================================================================= -# Regression utilities -# ============================================================================= - -#' @title Jackknife Standard Errors by Block -#' @description Compute jackknife SE estimates using leave-one-block-out. -#' @param estimatesFull Numeric vector, full-sample parameter estimates. -#' @param estimatesLoo A matrix (nBlocks x nParams), leave-one-out estimates. -#' @return Numeric vector of jackknife SEs. -#' @keywords internal -jackknifeSe <- function(estimatesFull, estimatesLoo) { - nBlocks <- nrow(estimatesLoo) - pseudoVals <- nBlocks * - matrix( - estimatesFull, - nrow = nBlocks, - ncol = length(estimatesFull), - byrow = TRUE - ) - - (nBlocks - 1) * estimatesLoo - jkVar <- apply(pseudoVals, 2, var) / nBlocks - sqrt(jkVar) -} - # ============================================================================= # Baseline enrichment computation # ============================================================================= @@ -165,42 +125,6 @@ computeBaselineEnrichment <- function( ) } -# ============================================================================= -# LD shrinkage -# ============================================================================= - -#' @title Apply LD Shrinkage -#' @description Apply shrinkage to sample LD matrix to reduce noise from finite -#' reference panel size, following Wen & Stephens (2010). -#' @param R Numeric matrix, sample LD correlation matrix. -#' @param nSamples Integer, reference panel sample size. -#' @param shrinkageType Character, one of "wen_stephens", "constant". -#' @param geneticMap Numeric vector, genetic map positions for SNPs in R. -#' @return Shrunk LD correlation matrix. -#' @keywords internal -shrinkLd <- function( - R, - nSamples, - shrinkageType = "wen_stephens", - geneticMap = NULL -) { - if (shrinkageType == "wen_stephens" && !is.null(geneticMap)) { - # Wen & Stephens (2010) shrinkage based on genetic distance - p <- nrow(R) - theta <- 2 * nSamples / (22 * nSamples + 16) # effective recombination - distCm <- abs(outer(geneticMap, geneticMap, "-")) - shrinkFactor <- exp( - -4 * nSamples * distCm / (100 * (2 * nSamples + 16)) - ) - RShrunk <- `diag<-`(R * shrinkFactor, 1) - } else { - # Simple constant shrinkage - lambda <- 1 / sqrt(nSamples) - RShrunk <- (1 - lambda) * R + lambda * diag(nrow(R)) - } - RShrunk -} - # ============================================================================= # Genome build utilities # ============================================================================= @@ -209,9 +133,9 @@ shrinkLd <- function( #' @description Check that genome builds match between objects. Each object #' contributes a single genome build (from its \code{genome} slot). An #' object that records no build is skipped rather than treated as a -#' mismatch: \code{loadLdMatrix()} leaves the build unset, so an LD -#' reference built from it names no build, and an unknown build cannot -#' contradict a known one. +#' mismatch: an LD reference often names no build (a genotype panel's +#' variants rarely carry one), and an unknown build cannot contradict a +#' known one. #' @param ... Objects with a \code{genome} slot. #' @return TRUE if all match, error otherwise. #' @keywords internal @@ -235,10 +159,10 @@ checkGenomeBuild <- function(...) { } # genome() is per-seqlevel, so flatten across objects before uniquing: one # object naming two builds and two objects disagreeing are the same error. - # An object naming none contributes NA and is skipped, because - # loadLdMatrix() leaves the build unset and demanding one here would - # reject every LD reference the package's own loader produces. The - # coordinate check in .h2CheckPositions() is the stronger guard regardless. + # An object naming none contributes NA and is skipped, because an LD + # reference often names no build and demanding one here would reject most + # of them. The coordinate check in .h2CheckPositions() is the stronger + # guard regardless. builds <- unname(discard( unique(list_c(map(objects, GenomeInfoDb::genome))), is.na @@ -304,24 +228,9 @@ standardizeTauStar <- function( # DerSimonian-Laird random-effects meta-analysis # ============================================================================= -#' @title Random-Effects Meta-Analysis (metafor wrapper) -#' @description Thin convenience wrapper around \code{metafor::rma()}. pecotmr -#' does not implement its own meta-analysis engine -- this helper only guards -#' the degenerate 0- and 1-study cases (where \code{rma} is undefined) and -#' reshapes the \code{metafor} fit into a small list. The between-study -#' variance estimator is chosen by \code{method} and passed straight through. -#' @param means Numeric vector of study-level point estimates. -#' @param ses Numeric vector of study-level standard errors (must be positive -#' and finite). -#' @param method Between-study variance estimator forwarded to -#' \code{metafor::rma(method = )} (e.g. \code{"DL"} (default), \code{"REML"}, -#' \code{"ML"}, \code{"EB"}). -#' @return A list with \code{mean}, \code{se}, \code{tau2}, \code{I2} (a [0, 1] -#' proportion) and \code{Q} (Cochran's Q). -#' @importFrom metafor rma -#' @noRd # Degenerate meta-analysis inputs: k = 0 (all NA) or k = 1 (pass-through), plus # positivity validation. Returns NULL when the full estimator should run. +# @noRd .rmaMetaEarly <- function(means, ses, k) { if (k == 0L) { return(list( @@ -405,6 +314,24 @@ RmaOptions <- function(...) { ) } +#' @title Random-Effects Meta-Analysis (metafor wrapper) +#' @description Thin convenience wrapper around \code{metafor::rma()}. pecotmr +#' does not implement its own meta-analysis engine -- this helper only guards +#' the degenerate 0- and 1-study cases (where \code{rma} is undefined) and +#' reshapes the \code{metafor} fit into a small list. The between-study +#' variance estimator is chosen by \code{method} and passed straight through. +#' @param means Numeric vector of study-level point estimates. +#' @param ses Numeric vector of study-level standard errors (must be positive +#' and finite). +#' @param method Between-study variance estimator forwarded to +#' \code{metafor::rma(method = )} (e.g. \code{"DL"} (default), \code{"REML"}, +#' \code{"ML"}, \code{"EB"}). +#' @param metaArgs Extra \code{metafor::rma()} arguments, an +#' \code{\link{RmaOptions}} record. +#' @return A list with \code{mean}, \code{se}, \code{tau2}, \code{I2} (a [0, 1] +#' proportion) and \code{Q} (Cochran's Q). +#' @importFrom metafor rma +#' @noRd #' @importFrom rlang try_fetch .rmaMeta <- function(means, ses, method = "DL", metaArgs = list()) { k <- length(means) @@ -506,18 +433,48 @@ NULL # Per-block whitened statistics x_i = (u_i'z)/sqrt(lam_i) and stratified # eigenvalue scores l_{ia} = sum_j A_{ja} V_{ji}^2 (all-ones column when -# unstratified). Near-null directions (lam < 1e-6) are zeroed (as upstream). -.lderBlockStats <- function(block, z, baselineMat) { - V <- block$vectors - lam <- block$values - raw <- as.vector(crossprod(V, z[block$snpIdx]) / sqrt(pmax(lam, 1e-12))) - x <- replace(raw, lam < 1e-6, 0) - ldAnnot <- if (is.null(baselineMat)) { - matrix(1, length(lam), 1) - } else { - crossprod(V^2, baselineMat[block$snpIdx, , drop = FALSE]) +# unstratified), plus the candidate annotations' scores, from the block's +# decomposition. Near-null directions (lam < 1e-6) are zeroed, as in the +# original LDER implementation, which keeps every direction of the block: +# the zero-eigenvalue directions a decomposition from a panel's factor does +# not list are added as rows with x = 0 and lam = 0. +.lderBlockStats <- function(block, eigenRef, z, baselineMat, candMat) { + dec <- .ldEigenBlockAt(block, eigenRef) + lam <- dec$values + raw <- as.vector( + crossprod(dec$vectors, z[dec$snpIdx]) / sqrt(pmax(lam, 1e-12)) + ) + nAll <- length(lam) + dec$nNull + list( + x = c(replace(raw, lam < 1e-6, 0), numeric(dec$nNull)), + lam = c(lam, numeric(dec$nNull)), + ldAnnot = .h2EigenAnnot(dec, baselineMat) %||% matrix(1, nAll, 1), + ldCand = .h2EigenAnnot(dec, candMat) + ) +} + +# One block's eigenvalue scores sum_k V_ki^2 a_k for each column of +# `annotMat`, with a zero row for each zero-eigenvalue direction the +# decomposition does not list: every use multiplies a direction's row by its +# eigenvalue. NULL without annotations. +.h2EigenAnnot <- function(dec, annotMat) { + if (is.null(annotMat)) { + return(NULL) + } + listed <- crossprod( + dec$vectors^2, + annotMat[dec$snpIdx, , drop = FALSE] + ) + rbind(listed, matrix(0, dec$nNull, ncol(annotMat))) +} + +# The candidate (tested) annotations, or NULL when there are none to score. +.h2CandidateMat <- function(annotations) { + if (is.null(annotations)) { + return(NULL) } - list(x = x, lam = lam, ldAnnot = ldAnnot) + candMat <- assay(testedAnnotations(annotations), "annotations") + if (ncol(candMat) == 0L) NULL else candMat } # Concatenate per-block stats into the genome-wide regression design. M_a is @@ -645,15 +602,23 @@ NULL aLoo <- map_dbl(loo, "a") tauList <- map(loo, "tau") jkCoefs <- exec(rbind, !!!tauList) - jkSe <- function(v) sqrt((nB - 1) / nB * sum((v - mean(v))^2)) list( - h2Se = jkSe(h2Loo), - aSe = jkSe(aLoo), - tauSe = apply(jkCoefs, 2, jkSe), + h2Se = .jackknifeSeFromLoo(h2Loo), + aSe = .jackknifeSeFromLoo(aLoo), + tauSe = apply(jkCoefs, 2, .jackknifeSeFromLoo), annotationJackknifeCoefs = jkCoefs ) } +# Delete-one-block jackknife standard error from the n leave-one-out +# estimates `v`: sqrt((n - 1) / n * sum((v - mean(v))^2)). The LDER, gLDSC, +# HDL and S-LDSC jackknifes all report this quantity. +# @noRd +.jackknifeSeFromLoo <- function(v) { + n <- length(v) + sqrt((n - 1) / n * sum((v - mean(v))^2)) +} + # tau / tauSe / annotationJackknifeCoefs for the enrichment framework # (univariate: tau = h2). .lderEnrichParts <- function(fit, jk, baselineMat) { @@ -735,7 +700,7 @@ NULL } # LDER candidate-annotation score statistics against the fitted baseline. -.lderScoreStats <- function(eigenRef, annotations, design, fit, N) { +.lderScoreStats <- function(blockStats, annotations, design, fit, N) { candidate <- testedAnnotations(annotations) candMat <- assay(candidate, "annotations") nCand <- ncol(candMat) @@ -749,12 +714,7 @@ NULL ) w <- 1 / (2 * mu^2) resid <- design$x^2 - mu - candCross <- map( - eigenList(eigenRef), - .h2EigenCandCrossprod, - candMat = candMat - ) - ldCand <- exec(rbind, !!!candCross) + ldCand <- exec(rbind, !!!map(blockStats, "ldCand")) keepAll <- rep(TRUE, length(lam)) scoreZ <- map_dbl( seq_len(nCand), @@ -779,11 +739,15 @@ lderUnivariate <- function( ) { rough <- !inSample(eigenRef) baselineMat <- .h2BaselineMat(annotations) + # One pass over the blocks, each decomposed only while its statistics are + # taken, so a reference read from a panel holds one block at a time. blockStats <- map( - eigenList(eigenRef), + .ldEigenBlocks(eigenRef), .lderBlockStats, + eigenRef = eigenRef, z = z, - baselineMat = baselineMat + baselineMat = baselineMat, + candMat = .h2CandidateMat(annotations) ) design <- .lderDesign(blockStats, baselineMat) M <- length(design$x) @@ -800,24 +764,31 @@ lderUnivariate <- function( jk <- .lderJackknife(blockStats, baselineMat, n, M, design$M_a, rough, TRUE) ep <- .lderEnrichParts(fit, jk, baselineMat) localDf <- if (local) .lderLocal(blockStats, n, fit$a) else NULL - scoreStats <- if (is.null(annotations)) { - NULL - } else { - .lderScoreStats(eigenRef, annotations, design, fit, n)$scoreStats - } - jkRes <- list( + .h2JackknifeResult( + h2 = fit$h2, + intercept = fit$a, se = c(jk$h2Se, jk$aSe), - tauSe = ep$tauSe, - annotationJackknifeCoefs = ep$annotationJackknifeCoefs - ) - enrichmentDf <- .h2Enrichment( - baselineMat, - ep$tau, - ep$tauSe, - ep$annotationJackknifeCoefs, - fit$h2 + ep = ep, + baselineMat = baselineMat, + localDf = localDf, + scoreStats = .lderCandidateScores( + blockStats, + annotations, + design, + fit, + n + ) ) - .h2Result(fit$h2, jkRes, fit$a, ep$tau, localDf, enrichmentDf, scoreStats) +} + +# LDER's score statistics for the candidate annotations; NULL when there are +# none to test. +# @noRd +.lderCandidateScores <- function(blockStats, annotations, design, fit, n) { + if (is.null(annotations)) { + return(NULL) + } + .lderScoreStats(blockStats, annotations, design, fit, n)$scoreStats } @@ -860,6 +831,35 @@ lderUnivariate <- function( ) } +# The result of an estimator jackknifed over its blocks (LDER, HDL): `se` +# holds the standard errors of h2 and of the intercept, and `ep` the +# enrichment parts (tau, tauSe, annotationJackknifeCoefs) that the +# per-annotation enrichment table is built from. +# @noRd +.h2JackknifeResult <- function( + h2, + intercept, + se, + ep, + baselineMat, + localDf, + scoreStats +) { + jkRes <- list( + se = se, + tauSe = ep$tauSe, + annotationJackknifeCoefs = ep$annotationJackknifeCoefs + ) + enrichmentDf <- .h2Enrichment( + baselineMat, + ep$tau, + ep$tauSe, + ep$annotationJackknifeCoefs, + h2 + ) + .h2Result(h2, jkRes, intercept, ep$tau, localDf, enrichmentDf, scoreStats) +} + # Per-annotation enrichment table via the shared tau-based framework (used by # HDL and LDER; g-LDSC builds its own from partitioned h2). NULL when # unstratified. @@ -892,10 +892,15 @@ lderUnivariate <- function( #' @description Estimate heritability and functional enrichment by feasible #' generalized least squares (FGLS) on LD scores (Xiong et al. 2024). The #' response \eqn{Z_j^2 - 1} is regressed on stratified LD scores -#' \eqn{\ell_{ja} = \sum_k R^2_{jk} A_{ka}} (with a confounding intercept) -#' using the full residual covariance +#' \eqn{\ell_{ja} = \sum_k R^2_{jk} A_{ka}} (with a confounding intercept; +#' the raw sample \eqn{R^2}, as the original gLDSC implementation forms +#' them, not the unbiased \eqn{r^2} of the S-LDSC scores) using the full +#' residual covariance #' \eqn{\Omega = (\hat{N\tau}\, R D_a R + R)^2} (elementwise), inverted per LD -#' block via a 99\%-eigenvalue-mass truncation. \eqn{\hat{N\tau} = \sum(Z^2-1) +#' block via a 99\%-eigenvalue-mass truncation. Each block's \eqn{\Omega} +#' is formed, decomposed and dropped in turn; for a reference read from a +#' genotype panel it is formed from the panel's factor, never from the +#' block's \eqn{R}. \eqn{\hat{N\tau} = \sum(Z^2-1) #' / \sum \ell} is a method-of-moments plug-in; #' \eqn{D_a = diag(\sum_a A_{ja})}. #' Coefficients are \eqn{\tau = (X'\Omega^{-1}X)^{-1} X'\Omega^{-1} y / N} and @@ -919,32 +924,89 @@ NULL if (is.null(baselineMat)) matrix(1, M, 1) else cbind(base = 1, baselineMat) } -# Per-block quantities: LD matrix R, block annotations, stratified LD scores -# l = (R^2) A, and response y = Z^2 - 1. -.gldscBlockPrep <- function(block, z, A) { - R <- block$R - idx <- block$snpIdx +# Per-block design: the block's annotations, its stratified LD scores +# l = (R o R) A, the candidate annotations' scores (R o R) C, and the +# response y = Z^2 - 1. The one LD score here that stays on the raw r^2: the +# original gLDSC implementation squares its LD matrix as is (middle_fun.R +# LDSCmatrix), and the GLS design is reproduced from it. A block read from a +# genotype panel gets its scores from the panel's factor a stripe of rows at +# a time (.ldSquaredProduct()). +.gldscBlockDesign <- function(block, panel, z, A, candMat) { + idx <- block$idx Ab <- A[idx, , drop = FALSE] - list(R = R, Ab = Ab, ldsc = (R^2) %*% Ab, y = z[idx]^2 - 1, snpIdx = idx) + Cb <- if (!is.null(candMat)) candMat[idx, , drop = FALSE] + scores <- .ldSquaredProduct(.ldStatBlockLd(block, panel), cbind(Ab, Cb)) + nA <- ncol(Ab) + list( + Ab = Ab, + ldsc = scores[, seq_len(nA), drop = FALSE], + ldCand = if (!is.null(Cb)) scores[, -seq_len(nA), drop = FALSE], + y = z[idx]^2 - 1 + ) } -# 99%-mass truncated inverse of Omega = (rawNtau * R Da R + R)^2 (upstream -# eigen.cut). Da = diag(rowSums(A)) so R Da R = crossprod(R sqrt(Da)). -.gldscOmegaInv <- function(R, Ab, rawNtau, cutpoint = 0.99) { - Omega <- (rawNtau * crossprod(R * sqrt(rowSums(Ab))) + R)^2 - ei <- eigen(Omega, symmetric = TRUE) +# Omega = S o S, with S = rawNtau * R Da R + R and Da = diag(rowSums(A)), +# reduced to its leading eigenpairs carrying `cutpoint` of the eigenvalue +# mass (eigen.cut in the original gLDSC implementation): Omega^-1 is applied +# as V diag(1 / values) V' and never formed. +.gldscOmegaEigen <- function(ld, Ab, rawNtau, cutpoint = 0.99) { + S <- .gldscS(ld, rowSums(Ab), rawNtau) + ei <- eigen(S^2, symmetric = TRUE) k <- which(cumsum(ei$values) / sum(ei$values) >= cutpoint)[1] - Vc <- ei$vectors[, seq_len(k), drop = FALSE] - Vc %*% (t(Vc) / ei$values[seq_len(k)]) + list( + vectors = ei$vectors[, seq_len(k), drop = FALSE], + values = ei$values[seq_len(k)] + ) +} + +# S = rawNtau * R Da R + R for a block's LD source, with Da = diag(d). From a +# stored R, R Da R = crossprod(R sqrt(Da)). From a factor F, with +# R = F'F / (m - 1), S = F' (rawNtau / (m - 1) * (F Da F') F + F) / (m - 1): +# only S is formed, which Omega needs whole, never the block's R. +.gldscS <- function(ld, d, rawNtau) { + if (!is.null(ld$R)) { + return(rawNtau * crossprod(ld$R * sqrt(d)) + ld$R) + } + F <- ld$F + m1 <- nrow(F) - 1 + gram <- tcrossprod(sweep(F, 2L, sqrt(d), "*")) + crossprod(F, rawNtau / m1 * (gram %*% F) + F) / m1 +} + +# One block's contribution to the GLS normal equations, L = X'Omega^-1 X and +# R = X'Omega^-1 y with X = [1 | l], plus its candidate-score pieces. Omega +# is formed for the block, decomposed and dropped, so only one block's is +# ever in memory. +.gldscBlockContrib <- function(block, design, panel, rawNtau) { + om <- .gldscOmegaEigen(.ldStatBlockLd(block, panel), design$Ab, rawNtau) + X <- cbind(1, design$ldsc) + P <- crossprod(om$vectors, X) + Py <- crossprod(om$vectors, design$y) + Pw <- P / om$values + list( + L = crossprod(Pw, P), + R = as.vector(crossprod(Pw, Py)), + p = length(block$idx), + cand = .gldscCandidatePieces(om, design$ldCand, P, Py) + ) } -# Block contribution to the GLS normal equations: L = X'Omega^-1 X, -# R = X'Omega^-1 y, with X = [1 | l]. -.gldscBlockGls <- function(prep, rawNtau) { - OmInv <- .gldscOmegaInv(prep$R, prep$Ab, rawNtau) - X <- cbind(1, prep$ldsc) - XtOi <- crossprod(X, OmInv) - list(L = XtOi %*% X, R = as.vector(XtOi %*% prep$y)) +# A block's candidate-score pieces. The score is taken against the FITTED +# baseline residual, U_c = l_c' Omega^-1 (y - X coef), which is linear in +# coef: keeping l_c' Omega^-1 y and l_c' Omega^-1 X lets the block's score be +# formed once the fit is known, without revisiting the block. Alongside, +# I_c = l_c' Omega^-1 l_c. NULL without candidates. +.gldscCandidatePieces <- function(om, ldCand, P, Py) { + if (is.null(ldCand)) { + return(NULL) + } + Q <- crossprod(om$vectors, ldCand) + Qw <- Q / om$values + list( + Uy = as.vector(crossprod(Qw, Py)), + UX = crossprod(Qw, P), + I = colSums(Q * Qw) + ) } # GLS estimate from accumulated normal equations: tau = solve(L, R)/N (the @@ -968,7 +1030,6 @@ NULL # from the accumulated normal equations (so folds stay GLS-weighted). Jackknife # variance = var * (nB-1)^2 / nB (Xiong et al.). .gldscJackknife <- function(contrib, left, right, A, N) { - nB <- length(contrib) loo <- map( contrib, .gldscLooEstimate, @@ -982,11 +1043,10 @@ NULL tauList <- map(loo, "tau") jkCoefs <- exec(rbind, !!!tauList) intLoo <- map_dbl(loo, "intercept") - jkSe <- function(v) sqrt(var(v) * (nB - 1)^2 / nB) list( - h2Se = jkSe(estHBlocks[, 1]), - intSe = jkSe(intLoo), - tauSe = apply(jkCoefs, 2, jkSe), + h2Se = .jackknifeSeFromLoo(estHBlocks[, 1]), + intSe = .jackknifeSeFromLoo(intLoo), + tauSe = apply(jkCoefs, 2, .jackknifeSeFromLoo), annotationJackknifeCoefs = jkCoefs, estHBlocks = estHBlocks ) @@ -1022,64 +1082,48 @@ NULL ) } -# Local heritability for one LD block: a per-block GLS on [1 | base LD score]; +# Local heritability for one LD block: a per-block GLS on [1 | base LD score], +# whose normal equations are the leading 2 x 2 of the block's own; # h2Local = tau_base * (block SNPs), SE from the GLS coefficient covariance. -.gldscLocalBlock <- function(prep, b, rawNtau, N) { - OmInv <- .gldscOmegaInv(prep$R, prep$Ab, rawNtau) - X <- cbind(1, prep$ldsc[, 1]) - XtOi <- crossprod(X, OmInv) - L <- XtOi %*% X - coef <- solve(L, as.vector(XtOi %*% prep$y)) - p <- nrow(prep$R) +.gldscLocalBlock <- function(cb, b, N) { + L <- cb$L[1:2, 1:2] + coef <- solve(L, cb$R[1:2]) covTau <- (solve(L) / N^2)[2, 2] tibble( blockId = b, - h2Local = coef[2] / N * p, - h2LocalSe = sqrt(max(covTau, 0)) * p + h2Local = coef[2] / N * cb$p, + h2LocalSe = sqrt(max(covTau, 0)) * cb$p ) } # Per-block local heritability (NULL unless requested). -.gldscLocal <- function(preps, rawNtau, N) { +.gldscLocal <- function(contrib, N) { bind_rows( map( - seq_along(preps), + seq_along(contrib), .gldscLocalRow, - preps = preps, - rawNtau = rawNtau, + contrib = contrib, N = N ) ) } -# Candidate-annotation score against the FITTED baseline GLS residual -# r = y - X coef: U_c = sum_b l_c' Omega^-1 r, I_c = sum_b l_c' Omega^-1 l_c, -# scoreZ = U_c / sqrt(I_c). Uses the real fitted coefficients (not a refit). -.gldscCandidateBlock <- function(prep, candMat, rawNtau, coef) { - OmInv <- .gldscOmegaInv(prep$R, prep$Ab, rawNtau) - r <- prep$y - as.vector(cbind(1, prep$ldsc) %*% coef) - ldCand <- (prep$R^2) %*% candMat[prep$snpIdx, , drop = FALSE] - ct <- crossprod(ldCand, OmInv) - list(U = as.vector(ct %*% r), I = diag(ct %*% ldCand)) +# One block's candidate scores at the fitted coefficients, against the +# fitted baseline GLS residual r = y - X coef: U_c = l_c' Omega^-1 r and +# I_c = l_c' Omega^-1 l_c. +.gldscCandidateBlock <- function(cb, coef) { + list(U = cb$cand$Uy - as.vector(cb$cand$UX %*% coef), I = cb$cand$I) } -# sHDL-style candidate score statistics + jackknife score correlation. -.gldscScoreStats <- function(preps, candMat, rawNtau, coef) { - nCand <- ncol(candMat) - if (nCand == 0) { - return(NULL) - } - per <- map( - preps, - .gldscCandidateBlock, - candMat = candMat, - rawNtau = rawNtau, - coef = coef - ) +# sHDL-style candidate score statistics + jackknife score correlation: +# scoreZ = sum_b U_c / sqrt(sum_b I_c), at the real fitted coefficients (not +# a refit). +.gldscScoreStats <- function(contrib, coef) { + per <- map(contrib, .gldscCandidateBlock, coef = coef) U <- reduce(map(per, "U"), `+`) I <- reduce(map(per, "I"), `+`) scoreZ <- U / sqrt(pmax(I, 1e-10)) - R <- if (nCand == 1) { + R <- if (length(U) == 1L) { matrix(1, 1, 1) } else { looRows <- map( @@ -1089,8 +1133,7 @@ NULL U = U, I = I ) - looZ <- exec(rbind, !!!looRows) - Rc <- cor(looZ) + Rc <- cor(exec(rbind, !!!looRows)) replace(Rc, is.na(Rc), 0) } list(scoreZ = scoreZ, R = R) @@ -1103,39 +1146,53 @@ gldscUnivariate <- function( annotations = NULL, local = FALSE ) { - ldMatrixList <- ldMatrixList(ldRef) - if (length(ldMatrixList) == 0L) { - msg <- glue( - "g-LDSC requires full per-block LD matrices (ldMatrixList). ", - "Read the LD reference with the LD matrices retained." - ) - abort(msg) + if (!.ldScoreHasLd(ldRef)) { + abort(c( + "g-LDSC needs each LD block's LD, and this LdScore carries none.", + i = str_c( + "Build it from a genotype panel, or from LdMatrix blocks ", + "with `keepLdMatrices = TRUE`." + ) + )) } M <- length(ldRef) A <- .gldscAnnotMatrix(annotations, M) - preps <- map(ldMatrixList, .gldscBlockPrep, z = z, A = A) - rawNtau <- sum(list_c(map(preps, "y"))) / - sum(list_c(map(preps, "ldsc"))) - contrib <- map(preps, .gldscBlockGls, rawNtau = rawNtau) + panel <- ldReference(ldRef) + blocks <- .ldScoreBlocks(ldRef) + # Two passes over the blocks: Omega depends on rawNtau, which needs every + # block's design first. A block read from a panel is read once per pass. + designs <- map( + blocks, + .gldscBlockDesign, + panel = panel, + z = z, + A = A, + candMat = .h2CandidateMat(annotations) + ) + rawNtau <- sum(list_c(map(designs, "y"))) / + sum(list_c(map(designs, "ldsc"))) + contrib <- map2( + blocks, + designs, + .gldscBlockContrib, + panel = panel, + rawNtau = rawNtau + ) left <- reduce(map(contrib, "L"), `+`) right <- reduce(map(contrib, "R"), `+`) fit <- .gldscEstimate(left, right, A, n) jk <- .gldscJackknife(contrib, left, right, A, n) - .gldscResult(fit, jk, annotations, preps, rawNtau, local, n, M) + .gldscResult(fit, jk, annotations, contrib, local, n, M) } -# The candidate-annotation score statistics, when candidates were supplied and -# the score test could be formed for them. NULL otherwise. +# The candidate-annotation score statistics, when candidates were supplied. +# NULL otherwise. # @noRd -.gldscCandidateScores <- function(annotations, preps, rawNtau, fit) { - if (is.null(annotations)) { - return(NULL) - } - candMat <- assay(testedAnnotations(annotations), "annotations") - sc <- .gldscScoreStats(preps, candMat, rawNtau, fit$coef) - if (is.null(sc)) { +.gldscCandidateScores <- function(annotations, contrib, fit) { + if (is.null(.h2CandidateMat(annotations))) { return(NULL) } + sc <- .gldscScoreStats(contrib, fit$coef) list( z = sc$scoreZ, R = sc$R, @@ -1146,9 +1203,9 @@ gldscUnivariate <- function( } # Assemble the gldscUnivariate return (enrichment, local, candidate scores). -.gldscResult <- function(fit, jk, annotations, preps, rawNtau, local, n, M) { +.gldscResult <- function(fit, jk, annotations, contrib, local, n, M) { baselineMat <- .h2BaselineMat(annotations) - localDf <- if (local) .gldscLocal(preps, rawNtau, n) else NULL + localDf <- if (local) .gldscLocal(contrib, n) else NULL enrichmentDf <- if (is.null(baselineMat)) { NULL } else { @@ -1162,7 +1219,7 @@ gldscUnivariate <- function( M ) } - scoreStats <- .gldscCandidateScores(annotations, preps, rawNtau, fit) + scoreStats <- .gldscCandidateScores(annotations, contrib, fit) tau <- if (is.null(baselineMat)) fit$h2 else fit$tau[-1] tauSe <- if (is.null(baselineMat)) jk$h2Se else jk$tauSe[-1] jkRes <- list( @@ -1216,9 +1273,9 @@ NULL # variance of rotated coordinate i is # lamh2_i = s_i lam_i^2 - lam_i h2Total / nRef + int lam_i / n, # s_i = (ldAnnotScaled %*% h2a)_i and h2Total = sum(h2a). With a single 1/M -# score column this is exactly upstream HDL's llfun (Ning et al. 2020). -# param = c(h2_1, .., h2_nTau, int). -# `lim` is upstream HDL's floor on the modelled per-direction variance +# score column this is exactly llfun in the original HDL implementation +# (Ning et al. 2020). param = c(h2_1, .., h2_nTau, int). +# `lim` is the original HDL's floor on the modelled per-direction variance # (llfun.R uses exp(-10)). It has to match: with 40% of a real reference's # eigen-directions near zero the floor is active for most of them, and the # `bstar^2 / lamh2` term it feeds dominates the likelihood -- a smaller floor @@ -1247,23 +1304,34 @@ NULL } # Per-block HDL quantities: eigenvalues, rotated N-normalized effects -# bstar = V'(z/sqrt(n)), and stratified eigenvalue scores l_{ia} (NULL when -# unstratified). -.hdlBlockData <- function(block, z, n, baselineMat) { - idx <- block$snpIdx - V <- block$vectors - ldAnnot <- if (is.null(baselineMat)) { - NULL - } else { - crossprod(V^2, baselineMat[idx, , drop = FALSE]) - } +# bstar = V'(z/sqrt(n)), stratified eigenvalue scores l_{ia} (NULL when +# unstratified) and the candidate annotations' scores, from the block's +# decomposition. The zero-eigenvalue directions a decomposition from a +# panel's factor does not list are added with lam = 0. +.hdlBlockData <- function(block, eigenRef, z, n, baselineMat, candMat) { + dec <- .ldEigenBlockAt(block, eigenRef) + zb <- z[dec$snpIdx] / sqrt(n) + bstar <- as.vector(crossprod(dec$vectors, zb)) list( - lam = block$values, - bstar = as.vector(crossprod(V, z[idx] / sqrt(n))), - ldAnnot = ldAnnot + lam = c(dec$values, numeric(dec$nNull)), + bstar = c(bstar, .hdlNullBstar(zb, bstar, dec$nNull)), + ldAnnot = .h2EigenAnnot(dec, baselineMat), + ldCand = .h2EigenAnnot(dec, candMat) ) } +# The rotated effects along a block's unlisted zero-eigenvalue directions. +# HDL gives every such direction the same modelled variance, its floor, so +# the likelihood sees only their total sum of squares: the part of z outside +# the listed directions, spread evenly over them, leaves every estimate as a +# full decomposition would give it. +.hdlNullBstar <- function(zb, bstar, nNull) { + if (nNull == 0L) { + return(numeric()) + } + rep(sqrt(max(sum(zb^2) - sum(bstar^2), 0) / nNull), nNull) +} + # Genome-wide design: eigenvalues, rotated effects, block ids, the M_a-scaled # eigenvalue-score matrix, and M_a. Univariate uses a single all-ones score # column (l_{i,base} = 1) scaled by total M. @@ -1395,11 +1463,10 @@ NULL h2aList <- map(loo, "h2a") h2aBlocks <- exec(rbind, !!!h2aList) intLoo <- map_dbl(loo, "int") - jkSe <- function(x) sqrt(mean((x - mean(x))^2) * (nBlocks - 1)) list( - h2aSe = apply(h2aBlocks, 2, jkSe), - h2Se = jkSe(rowSums(h2aBlocks)), - intSe = jkSe(intLoo), + h2aSe = apply(h2aBlocks, 2, .jackknifeSeFromLoo), + h2Se = .jackknifeSeFromLoo(rowSums(h2aBlocks)), + intSe = .jackknifeSeFromLoo(intLoo), h2aBlocks = h2aBlocks ) } @@ -1429,15 +1496,19 @@ NULL # intercept fixed at the global fit; SE from the Gaussian-variance Fisher # information sum((dlamh2/dh2)^2 / (2 lamh2^2)). .hdlLocalBlock <- function(bd, b, n, nSamples, int, M, lim = exp(-10)) { - varOf <- function(h2) { - pmax( - h2 / M * bd$lam^2 - h2 * bd$lam / nSamples + int * bd$lam / n, - lim - ) - } - nll <- function(h2) sum(log(varOf(h2)) + bd$bstar^2 / varOf(h2)) - h2 <- optimize(nll, c(0, 1))$minimum - lamh2 <- varOf(h2) + # `intercept`, not `int`: optimize() would partially match `int` to its + # own `interval` argument. + h2 <- optimize( + .hdlLocalNll, + c(0, 1), + bd = bd, + M = M, + nSamples = nSamples, + intercept = int, + n = n, + lim = lim + )$minimum + lamh2 <- .hdlLocalVar(h2, bd, M, nSamples, int, n, lim) g <- bd$lam^2 / M - bd$lam / nSamples info <- sum(g^2 / (2 * lamh2^2)) tibble( @@ -1447,6 +1518,24 @@ NULL ) } +# Modeled variance of one block's bstar^2 at local heritability `h2`, the +# intercept fixed at the global fit; floored at `lim`. +# @noRd +.hdlLocalVar <- function(h2, bd, M, nSamples, intercept, n, lim) { + pmax( + h2 / M * bd$lam^2 - h2 * bd$lam / nSamples + intercept * bd$lam / n, + lim + ) +} + +# Gaussian negative log-likelihood (up to a constant) of one block's bstar at +# local heritability `h2` -- what .hdlLocalBlock() minimizes. +# @noRd +.hdlLocalNll <- function(h2, bd, M, nSamples, intercept, n, lim) { + lamh2 <- .hdlLocalVar(h2, bd, M, nSamples, intercept, n, lim) + sum(log(lamh2) + bd$bstar^2 / lamh2) +} + # Per-block local heritability (NULL unless requested). .hdlLocal <- function(blockData, n, nSamples, int, M) { bind_rows( @@ -1480,21 +1569,15 @@ NULL } # sHDL candidate-annotation score statistics + jackknife score correlation. -.shdlStratified <- function(eigenRef, annotations, design, lamh2) { +.shdlStratified <- function(blockData, nPanel, annotations, design, lamh2) { candidate <- testedAnnotations(annotations) candMat <- assay(candidate, "annotations") nCand <- ncol(candMat) if (nCand == 0) { return(list(enrichment = NULL, scoreStats = NULL)) } - nPanel <- ldPanelNSamples(eigenRef) M_c <- colSums(candMat) - candCross <- map( - eigenList(eigenRef), - .h2EigenCandCrossprod, - candMat = candMat - ) - ldCand <- exec(rbind, !!!candCross) + ldCand <- exec(rbind, !!!map(blockData, "ldCand")) scoreResid <- design$bstar^2 / lamh2^2 - 1 / lamh2 keepAll <- rep(TRUE, length(lamh2)) scoreZ <- map_dbl( @@ -1554,41 +1637,51 @@ hdlUnivariate <- function( annotations = NULL, local = FALSE ) { - eigenList <- eigenList(eigenRef) M <- length(eigenRef) nPanel <- ldPanelNSamples(eigenRef) baselineMat <- .h2BaselineMat(annotations) + # One pass over the blocks, each decomposed only while its data are taken. blockData <- map( - eigenList, + .ldEigenBlocks(eigenRef), .hdlBlockData, + eigenRef = eigenRef, z = z, n = n, - baselineMat = baselineMat + baselineMat = baselineMat, + candMat = .h2CandidateMat(annotations) ) design <- .hdlDesign(blockData, M, baselineMat) ft <- .hdlFit(design, n, nPanel) jk <- .hdlJackknife(blockData, M, baselineMat, n, nPanel) ep <- .hdlEnrichParts(ft, jk, design, baselineMat) localDf <- if (local) .hdlLocal(blockData, n, nPanel, ft$int, M) else NULL - scoreStats <- if (is.null(annotations)) { - NULL - } else { - lamh2 <- .hdlBaselineVar(design, ft, n, nPanel) - .shdlStratified(eigenRef, annotations, design, lamh2)$scoreStats - } - jkRes <- list( + .h2JackknifeResult( + h2 = ft$h2, + intercept = ft$int, se = c(jk$h2Se, jk$intSe), - tauSe = ep$tauSe, - annotationJackknifeCoefs = ep$annotationJackknifeCoefs - ) - enrichmentDf <- .h2Enrichment( - baselineMat, - ep$tau, - ep$tauSe, - ep$annotationJackknifeCoefs, - ft$h2 + ep = ep, + baselineMat = baselineMat, + localDf = localDf, + scoreStats = .hdlCandidateScores( + blockData, + nPanel, + annotations, + design, + ft, + n + ) ) - .h2Result(ft$h2, jkRes, ft$int, ep$tau, localDf, enrichmentDf, scoreStats) +} + +# sHDL's score statistics for the candidate annotations, under the variances +# the baseline fit implies; NULL when there are none to test. +# @noRd +.hdlCandidateScores <- function(blockData, nPanel, annotations, design, ft, n) { + if (is.null(annotations)) { + return(NULL) + } + lamh2 <- .hdlBaselineVar(design, ft, n, nPanel) + .shdlStratified(blockData, nPanel, annotations, design, lamh2)$scoreStats } @@ -1622,8 +1715,10 @@ NULL #' least squares on LD scores (Finucane et al. 2015) -- the original method #' the other three estimators descend from. Per-variant \eqn{\chi^2_j} is #' regressed on annotation-stratified LD scores -#' \eqn{\ell_{jC} = \sum_{k \in C} r^2_{jk}} with a confounding -#' intercept, fitting +#' \eqn{\ell_{jC} = \sum_{k \in C} r^2_{jk}}, each \eqn{r^2} being ldsc's +#' unbiased estimate \code{r^2 - (1 - r^2) / (n - 2)} at the reference +#' panel size \code{n} (see \code{\link{buildLdScore}}), with a +#' confounding intercept, fitting #' \eqn{E[\chi^2_j] = N \sum_C \tau_C \ell_{jC} + Na + 1}. #' #' Weights are LDSC's two multiplied factors, both floored at 1: the @@ -1638,8 +1733,9 @@ NULL #' \item{unstratified}{the two-step estimator: the intercept is fit on #' the variants with \eqn{\chi^2 < 30}, then held fixed while the #' slope is fit over all of them.} -#' \item{stratified}{no two-step -- upstream refuses one on a -#' partitioned design. Variants with \eqn{\chi^2 >} \code{max(0.001 +#' \item{stratified}{no two-step -- the original \code{ldsc} refuses +#' one on a partitioned design. Variants with \eqn{\chi^2 >} +#' \code{max(0.001 #' N, 80)} are dropped, the design is weighted once, and a single #' weighted least-squares fit follows.} #' } @@ -1649,15 +1745,17 @@ NULL #' than an error to clamp away, and clamping would move the heritability #' too. #' -#' Estimates agree with upstream \code{ldsc} to roughly 1e-6 relative; -#' the residual is a small ridge in the weighted solve, which upstream has -#' no equivalent of and which exists so an exactly singular design returns -#' a value rather than \code{NA}. +#' Estimates agree with the original \code{ldsc} implementation to +#' roughly 1e-6 relative; the residual is a small ridge in the weighted +#' solve, which the original has no equivalent of and which exists so an +#' exactly singular design returns a value rather than \code{NA}. #' #' Unlike \code{\link{pecotmr-h2-gldsc}} this needs only the per-variant #' scores when unstratified, so it runs on a scores-only #' \code{\link{LdScore}}; the stratified fit forms per-annotation scores -#' from the per-block LD matrices and therefore requires them. +#' from each block's LD, so it needs an \code{LdScore} that carries it: +#' one built from a genotype panel, or from \code{LdMatrix} blocks with +#' their matrices kept. #' #' \strong{Scale.} LD score regression is a genome-scale method -- real #' analyses regress over roughly a million variants. On a reference of a @@ -1698,62 +1796,60 @@ sldscUnivariate <- function( } # The design's LD-score columns: the base score first, then one stratified -# score per baseline annotation. Stratified scores come from the per-block LD -# matrices, the same source g-LDSC uses. +# score per baseline annotation. Stratified scores come from each block's LD, +# the same source g-LDSC uses, and take ldsc's unbiased r^2 as the stored +# base score does: ldsc applies it to every annotation column. # @noRd .sldscScoreMatrix <- function(ldRef, baselineMat, M) { base <- as.vector(mcols(ldRef)$ldScores[, 1L]) if (is.null(baselineMat)) { return(matrix(base, ncol = 1L, dimnames = list(NULL, "base_l2"))) } - ldMatrixList <- ldMatrixList(ldRef) - if (length(ldMatrixList) == 0L) { - msg <- glue( - "stratified S-LDSC requires full per-block LD matrices ", - "(ldMatrixList) to form the per-annotation LD scores. Build the ", - "reference with `buildLdScore(keepLdMatrices = TRUE)`." - ) - abort(msg) + if (!.ldScoreHasLd(ldRef)) { + abort(str_c( + "stratified S-LDSC needs each LD block's LD to form the ", + "per-annotation LD scores. Build the reference from a genotype ", + "panel, or with `buildLdScore(keepLdMatrices = TRUE)`." + )) } - strat <- .h2ScatterBlockRows( - matrix(0, M, ncol(baselineMat)), - map(ldMatrixList, .h2BlockAnnotScores, baselineMat = baselineMat) - ) - cbind(base_l2 = base, strat) + cbind(base_l2 = base, .h2StratifiedScores(ldRef, baselineMat)) } -# One block's per-annotation LD scores from its full LD matrix. +# Annotation-stratified LD scores sum_k r^2_jk a_k under ldsc's unbiased r^2, +# one column per annotation, from each block's LD: stored, or read from the +# genotype panel a block at a time. # @noRd -.h2BlockAnnotScores <- function(block, baselineMat) { - idx <- block$snpIdx - list( - idx = idx, - values = (block$R^2) %*% baselineMat[idx, , drop = FALSE] +.h2StratifiedScores <- function(ldRef, annotMat) { + .h2ScatterBlockRows( + matrix(0, length(ldRef), ncol(annotMat)), + map( + .ldScoreBlocks(ldRef), + .h2BlockAnnotScores, + panel = ldReference(ldRef), + annotMat = annotMat, + nRef = .ldscPanelN(ldRef) + ) ) } -# Per-block variant indices, from the LD matrices when present and otherwise -# by overlapping the reference's variants with its LD blocks. The jackknife -# leaves one of these out at a time. +# One block's per-annotation LD scores from its LD, summing ldsc's unbiased +# r^2 by its summed form. +# @noRd +.h2BlockAnnotScores <- function(block, panel, annotMat, nRef) { + idx <- block$idx + Ab <- annotMat[idx, , drop = FALSE] + raw <- .ldSquaredProduct(.ldStatBlockLd(block, panel), Ab) + list(idx = idx, values = .ldscUnbiasedSum(raw, colSums(Ab), nRef)) +} + +# Per-block variant indices: the LD blocks the estimators walk, which the +# jackknife leaves out one at a time. # @noRd .sldscBlockIndex <- function(ldRef) { - ldMatrixList <- ldMatrixList(ldRef) - if (length(ldMatrixList) > 0L) { - return(map(ldMatrixList, "snpIdx")) - } - snpInfo <- tibble( - CHR = as.character(seqnames(ldRef)), - BP = start(ldRef) - ) - idx <- snpsPerBlock(snpInfo, ldBlocks(ldRef)) - keep <- idx[map_int(idx, length) > 0L] - if (length(keep) == 0L) { - abort("S-LDSC: no LD block covers any reference variant.") - } - keep + map(.ldScoreBlocks(ldRef), "idx") } -# Upstream `Hsq.weights` verbatim (ldsc/ldscore/regressions.py): +# `Hsq.weights` from the original ldsc, verbatim (ldscore/regressions.py): # # hsq = clamp(hsq, 0, 1) # ld = max(ld, 1); w_ld = max(w_ld, 1) @@ -1763,8 +1859,8 @@ sldscUnivariate <- function( # # `ld` is the LD score entering the model and `w_ld` the one computed over # just the regression variants. pecotmr carries a single score, so both read -# the same column -- also the usual case upstream, where --w-ld-chr names the -# same file as --ref-ld-chr. +# the same column -- also the usual case with the original ldsc, where +# --w-ld-chr names the same file as --ref-ld-chr. # @noRd .sldscWeights <- function(ld, wLd, n, M, hsq, intercept) { hsq <- min(max(hsq, 0), 1) @@ -1781,8 +1877,8 @@ sldscUnivariate <- function( M * (mean(chi2) - intercept) / mean(n * baseScore) } -# Upstream takes two different routes and the split is on annotation count, -# not on preference (ldscore/sumstats.py): +# The original ldsc takes two different routes and the split is on annotation +# count, not on preference (ldscore/sumstats.py): # # univariate -- two-step estimator, chi2 < 30 selects step 1, IRWLS twice # partitioned -- NO two-step; it raises rather than attempt one. Variants @@ -1807,7 +1903,8 @@ sldscUnivariate <- function( capped <- chi2 < 30 keep <- if (sum(capped) < 4L) rep(TRUE, length(chi2)) else capped # One set of initial weights, formed on the full data, reused by both - # steps -- upstream computes `initial_w` once and subsets it for step 1. + # steps -- the original ldsc computes `initial_w` once and subsets it for + # step 1. xTot <- rowSums(scores) aggH2 <- .sldscAggregateH2(chi2, xTot, n, M) w0 <- .sldscWeights(xTot, baseScore, n, M, aggH2, 1) @@ -1821,8 +1918,8 @@ sldscUnivariate <- function( freeIntercept = TRUE, w0 = w0[keep] ) - # Not floored at 1. The model implies an intercept of at least 1, but - # upstream reports whatever step 1 estimates -- a value below 1 is + # Not floored at 1. The model implies an intercept of at least 1, but the + # original ldsc reports whatever step 1 estimates -- a value below 1 is # informative (it says the data are inconsistent with the model, often # deflation) and clamping it silently would change the h2 as well. intercept <- step1$intercept @@ -1854,15 +1951,16 @@ sldscUnivariate <- function( chi2 <- chi2[keep] scores <- scores[keep, , drop = FALSE] baseScore <- baseScore[keep] - # `x_tot` upstream: the row-sum of the design's annotation columns, which - # coincides with the base score only when there is a single column. + # `x_tot` in the original ldsc: the row-sum of the design's annotation + # columns, which coincides with the base score only when there is a + # single column. xTot <- rowSums(scores) hsq <- .sldscAggregateH2(chi2, xTot, n, M) w <- .sldscWeights(xTot, baseScore, n, M, hsq, 1) .sldscCoefToFit(.sldscWlsCoef(cbind(scores, 1), chi2, w), n, A) } -# Two IRWLS passes, matching upstream's `for i in range(2)`. +# Two IRWLS passes, matching the original ldsc's `for i in range(2)`. # @noRd .sldscIrwls <- function( y, @@ -1885,19 +1983,19 @@ sldscUnivariate <- function( coef <- NULL for (i in seq_len(max(1L, nIter))) { coef <- .sldscWlsCoef(design, y, w) - # Upstream builds the weight from the FIRST coefficient and `x_tot` - # alone -- a univariate approximation even for a partitioned design -- - # rather than from the full fitted values. + # The original ldsc builds the weight from the FIRST coefficient and + # `x_tot` alone -- a univariate approximation even for a partitioned + # design -- rather than from the full fitted values. hsq <- M * coef[[1L]] / n if (freeIntercept) { intercept <- max(coef[[length(coef)]], 0) } w <- .sldscWeights(xTot, baseScore, n, M, hsq, intercept) } - # Upstream's loop updates the weights `nIter` times and then solves once - # more with the converged weights -- three solves for its two passes, not - # two. Stopping at the loop's last solve leaves the final weight update - # unused and shifts the estimate by around a percent. + # The original ldsc's loop updates the weights `nIter` times and then + # solves once more with the converged weights -- three solves for its two + # passes, not two. Stopping at the loop's last solve leaves the final + # weight update unused and shifts the estimate by around a percent. coef <- .sldscWlsCoef(design, y, w) list( coef = coef[seq_len(ncol(scores))], @@ -1955,12 +2053,10 @@ sldscUnivariate <- function( jkCoefs <- exec(rbind, !!!map(loo, "tau")) estHBlocks <- exec(rbind, !!!map(loo, "estH")) intLoo <- map_dbl(loo, "intercept") - nB <- length(loo) - jkSe <- function(v) sqrt(var(v) * (nB - 1)^2 / nB) list( - h2Se = jkSe(estHBlocks[, 1L]), - intSe = jkSe(intLoo), - tauSe = apply(jkCoefs, 2, jkSe), + h2Se = .jackknifeSeFromLoo(estHBlocks[, 1L]), + intSe = .jackknifeSeFromLoo(intLoo), + tauSe = apply(jkCoefs, 2, .jackknifeSeFromLoo), annotationJackknifeCoefs = jkCoefs, estHBlocks = estHBlocks ) @@ -2065,8 +2161,9 @@ sldscUnivariate <- function( # trailing arguments (`nIter` for sldsc; the others take none today), so # an unknown name should fail here rather than at whichever estimator the # method token happens to select. lder/gldsc/hdl used to advertise a - # ridge `lambda` here that nothing read -- upstream LDER and HDL have no - # such penalty, and the function behind it had no callers. + # ridge `lambda` here that nothing read -- the original LDER and HDL + # implementations have no such penalty, and the function behind it had no + # callers. base <- list(z, n, ldRef, annotations, local) fn <- switch( method, @@ -2249,50 +2346,83 @@ setMethod( base } -# One block's base LD scores. +# One block's base LD scores. sum_k r^2_jk is sum_i (V[j,i] d[i])^2 because +# R = V D V' with orthonormal V, so the raw sum needs no R; the summed form of +# ldsc's unbiased r^2 then corrects it exactly, the block size being the +# weight sum. # @noRd -.h2BlockBaseScores <- function(block) { +.h2BlockBaseScores <- function(block, nRef) { Vd <- sweep(block$vectors, 2, block$values, "*") - list(idx = block$snpIdx, values = rowSums(Vd^2)) + list( + idx = block$snpIdx, + values = .ldscUnbiasedSum( + cbind(rowSums(Vd^2)), + length(block$snpIdx), + nRef + ) + ) } -# Base LD scores l2[j] = sum_i (V[j,i] * d[i])^2 (since R = V D V'). -.computeLdScoresBase <- function(eigenList, nSnps) { +# Base LD scores: ldsc's unbiased r^2 summed over each variant's block. +.computeLdScoresBase <- function(ldRef, nRef) { .h2ScatterBlockRows( - matrix(0, nrow = nSnps, ncol = 1, dimnames = list(NULL, "base_l2")), - map(eigenList, .h2BlockBaseScores) + matrix( + 0, + nrow = length(ldRef), + ncol = 1, + dimnames = list(NULL, "base_l2") + ), + map( + .ldEigenBlocks(ldRef), + .h2BlockBaseScoresAt, + ldRef = ldRef, + nRef = nRef + ) ) } +# @noRd +.h2BlockBaseScoresAt <- function(block, ldRef, nRef) { + .h2BlockBaseScores(.ldEigenBlockAt(block, ldRef), nRef) +} + # Base + annotation-stratified LD scores. Stratified column a: -# l2_a[j] = sum_i (V[j,i] d[i])^2 * (sum_k V[k,i]^2 annot[k,a]). -.computeLdScoresStratified <- function(eigenList, annotations, nSnps) { +# l2_a[j] = sum_k r^2_jk annot[k, a], under ldsc's unbiased r^2. +.computeLdScoresStratified <- function(ldRef, annotations, nRef) { annotMat <- assay(annotations, "annotations") annotNames <- SummarizedExperiment::colData(annotations)$name .h2ScatterBlockRows( matrix( 0, - nrow = nSnps, + nrow = length(ldRef), ncol = 1 + ncol(annotMat), dimnames = list(NULL, c("base_l2", annotNames)) ), - map(eigenList, .h2BlockStratScores, annotMat = annotMat) + map( + .ldEigenBlocks(ldRef), + .h2BlockStratScores, + ldRef = ldRef, + annotMat = annotMat, + nRef = nRef + ) ) } -# One block's base + annotation-stratified scores. All annotations are taken -# in a single matrix product rather than one column at a time. +# One block's base + annotation-stratified scores. The weighted sums need R's +# entries: the eigen shortcut sum_i (V[j,i] d[i])^2 * sum_k V[k,i]^2 +# annot[k,a] drops the cross terms between eigenvectors, which cancel only +# when every annot[k, a] is 1, so it is not the stratified score. R's rows +# are rebuilt from the decomposition a stripe at a time, and the summed form +# of ldsc's unbiased r^2 corrects every column at once. # @noRd -.h2BlockStratScores <- function(block, annotMat) { - idx <- block$snpIdx - V <- block$vectors - Vd2 <- sweep(V, 2, block$values, "*")^2 +.h2BlockStratScores <- function(block, ldRef, annotMat, nRef) { + dec <- .ldEigenBlockAt(block, ldRef) + idx <- dec$snpIdx + Ab <- annotMat[idx, , drop = FALSE] + raw <- .ldSquaredProduct(dec[c("vectors", "values")], cbind(1, Ab)) list( idx = idx, - values = cbind( - rowSums(Vd2), - Vd2 %*% crossprod(V^2, annotMat[idx, , drop = FALSE]) - ) + values = .ldscUnbiasedSum(raw, c(length(idx), colSums(Ab)), nRef) ) } @@ -2302,14 +2432,13 @@ setMethod( "computeLdScores", signature(ldRef = "LdEigen"), function(ldRef, annotations = NULL) { - # Reconstruct LD scores from eigendecompositions - # l2[j] = sum_k r^2_{jk} = sum_b sum_{eigenvalues in b} V[j,.]^2 * d - nSnps <- length(ldRef) - eigenList <- eigenList(ldRef) + # LD scores from the eigendecompositions, under ldsc's unbiased r^2, + # so they match buildLdScore() on the same reference. + nRef <- .ldscPanelN(ldRef) if (is.null(annotations)) { - return(.computeLdScoresBase(eigenList, nSnps)) + return(.computeLdScoresBase(ldRef, nRef)) } - .computeLdScoresStratified(eigenList, annotations, nSnps) + .computeLdScoresStratified(ldRef, annotations, nRef) } ) @@ -2322,25 +2451,20 @@ setMethod( if (is.null(annotations)) { return(mcols(ldRef)$ldScores) } - - # Compute annotation-stratified LD scores using LD matrices - ldMatrixList <- ldMatrixList(ldRef) - if (length(ldMatrixList) == 0) { - msg <- glue( - "Annotation-stratified LD scores require ldMatrixList in ", - "LdScore. ", - "Recompute the LD reference with full LD matrices." - ) - abort(msg) + if (!.ldScoreHasLd(ldRef)) { + abort(str_c( + "Annotation-stratified LD scores need each LD block's LD, ", + "and this LdScore carries none. Build it from a genotype ", + "panel, or from LdMatrix blocks with `keepLdMatrices = TRUE`." + )) } - - annotMat <- assay(annotations, "annotations") - # The stratified columns l2_a[j] = sum_k R^2_{jk} annot[k, a] are - # scattered per block; the base column comes from the reference and - # is joined on afterwards, so the scatter never touches it. - strat <- .h2ScatterBlockRows( - matrix(0, nrow = length(ldRef), ncol = ncol(annotMat)), - map(ldMatrixList, .h2BlockAnnotScores, baselineMat = annotMat) + # The stratified columns l2_a[j] = sum_k r^2_jk annot[k, a], under + # ldsc's unbiased r^2, are scattered per block; the base column comes + # from the reference and is joined on afterwards, so the scatter + # never touches it. + strat <- .h2StratifiedScores( + ldRef, + assay(annotations, "annotations") ) `colnames<-`( cbind(mcols(ldRef)$ldScores[, 1], strat), @@ -2423,14 +2547,6 @@ h2EstimateToSldscTrait <- function(h2Est) { # ---- map/apply helpers (lambda-free callbacks) --------------------------- -# crossprod of one eigen block's squared vectors against its -# candidate-annotation -# rows. Shared by the LDER and sHDL candidate-score assemblies. -# @noRd -.h2EigenCandCrossprod <- function(bk, candMat) { - crossprod(bk$vectors^2, candMat[bk$snpIdx, , drop = FALSE]) -} - # One LDER delete-one-block jackknife fold: design and fit with block `b` left # out. # @noRd @@ -2486,8 +2602,8 @@ h2EstimateToSldscTrait <- function(h2Est) { # One g-LDSC local-heritability row for block `b`. # @noRd -.gldscLocalRow <- function(b, preps, rawNtau, N) { - .gldscLocalBlock(preps[[b]], b, rawNtau, N) +.gldscLocalRow <- function(b, contrib, N) { + .gldscLocalBlock(contrib[[b]], b, N) } # g-LDSC leave-one-block-out standardized candidate score vector for block `b`. diff --git a/R/jointEngine.R b/R/jointEngine.R index fce1a4eaa..8ba305283 100644 --- a/R/jointEngine.R +++ b/R/jointEngine.R @@ -328,6 +328,28 @@ setMethod( # fsusie joint fit (functional SuSiE over the trait domain; individual-level, # cross-trait). Keep the first post-processed entry for this joint fit. +# +# The credible-set arguments supply what .fmOwnedEngineArgs("fsusieR::susiF") +# says is supplied: these are refused in FsusieOptions, so the pipeline is +# their only source. They are fitFsusie() formals, so .splitMethodArgs() +# routes them past the constructor that refuses them. +# +# The functional fit is collapsed to a variants x features weight matrix now +# (trimming later drops fitted_wc) and stored on $coef, so a trimmed fit can +# still yield TWAS weights. +# +# Returns ONE entry for the whole joint fit, unlike mvsusie's +# one-per-condition. mvsusie's posteriors carry a conditions axis, so +# .fmSliceCond() gives each condition its own 2-D slice and N rows partition +# the fit at no cost. An fsusie fit has no such axis: each trait is a POSITION +# on a shared functional grid, so `fitted_func` / `cred_band` / `outing_grid` +# describe all traits at once and cannot be sliced per trait. One row per +# trait would therefore store N copies of the same curve. +# +# The trait list itself is not lost -- every row carries the full joint +# membership in its `jointTraits` column. Only the grid POSITIONS are +# unrecoverable from that, and they are what maps a curve position back to a +# trait, so they ride on the fit. # @noRd .jointFitFsusie <- function(group, Xc, Yc, nCond, cfg, args) { if (length(.jgTraitPos(group)) != nCond) { @@ -338,10 +360,31 @@ setMethod( abort(msg) } verbose <- if (is.null(cfg$verbose)) 1 else cfg$verbose - # Supply what .fmOwnedEngineArgs("fsusieR::susiF") says is supplied: - # these are refused in FsusieOptions, so the pipeline is their only - # source. They are fitFsusie() formals, so .splitMethodArgs() routes them - # past the constructor that refuses them. + fit <- if (.jointFitsFullData(cfg)) { + .jointFsusieFullFit(group, Xc, Yc, cfg, args) + } else { + NULL + } + cvM <- .jointFsusieCv(Xc, Yc, group, cfg, args, verbose) + entry <- .jointFsusieEntry( + r = 1L, + fit = fit, + cvM = cvM, + Xc = Xc, + cfg = cfg, + af = args$af, + region = args$region + ) + if (!is.null(fit)) { + entry@susieFit$trait_positions <- .jgTraitPos(group) + } + list(entry) +} + +# The full-data fSuSiE fit of one joint group, with its functional fit +# collapsed to a variants x features weight matrix on $coef. +# @noRd +.jointFsusieFullFit <- function(group, Xc, Yc, cfg, args) { csp <- cfg$credibleSetParam fitArgs <- .fmMergeUserArgs( list( @@ -356,10 +399,7 @@ setMethod( args$methodArgs[["fsusie"]] ) raw <- exec(fitFsusie, !!!.splitMethodArgs(fitFsusie, fitArgs)) - # Collapse the functional fit to a variants x features weight matrix now - # (trimming later drops fitted_wc); store on $coef so a trimmed fit - # can still yield TWAS weights. - fit <- list_assign( + list_assign( raw, coef = try_fetch( fsusieWeights(fsusieFit = raw, variantIds = colnames(Xc)), @@ -367,36 +407,12 @@ setMethod( ) ) |> .setFinemappingFitClass("fsusie") - cvM <- .jointFsusieCv(Xc, Yc, group, cfg, args, verbose) - entry <- .jointFsusieEntry( - r = 1L, - fit = fit, - cvM = cvM, - Xc = Xc, - cfg = cfg, - af = args$af, - region = args$region - ) - # ONE entry for the whole joint fit, unlike mvsusie's one-per-condition. - # mvsusie's posteriors carry a conditions axis, so .fmSliceCond() gives - # each condition its own 2-D slice and N rows partition the fit at no - # cost. An fsusie fit has no such axis: each trait is a POSITION on a - # shared functional grid, so `fitted_func` / `cred_band` / `outing_grid` - # describe all traits at once and cannot be sliced per trait. One row per - # trait would therefore store N copies of the same curve. - # - # The trait list itself is not lost -- every row carries the full joint - # membership in its `jointTraits` column. Only the grid POSITIONS are - # unrecoverable from that, and they are what maps a curve position back - # to a trait, so they ride on the fit. - entry@susieFit$trait_positions <- .jgTraitPos(group) - list(entry) } # Per-fold fsusie CV slice, or NULL when CV is disabled. # @noRd .jointFsusieCv <- function(Xc, Yc, group, cfg, args, verbose) { - cv <- cfg$crossValidationArgs %||% list() + cv <- cfg$crossValidationParam %||% list() cvFolds <- cv$folds %||% 0L if (cvFolds <= 1L) { return(NULL) @@ -407,11 +423,11 @@ setMethod( "fsusie", args$methodArgs, cvFolds, - samplePartition = cfg$crossValidationArgs$samplePartition, + samplePartition = cfg$crossValidationParam$samplePartition, coverage = cfg$credibleSetParam$coverage, pos = .jgTraitPos(group), verbose = verbose, - numThreads = cv$threads %||% 1L, + numThreads = cv$numThreads %||% 1L, seed = cfg$seed ) .fmSliceCv(cv, "fsusie") @@ -430,18 +446,22 @@ setMethod( af = NULL, region = NULL ) { - bare <- .fmPostprocessOne( - fit = fit, - method = "fsusie", - dataX = Xc, - dataY = NULL, - af = af, - region = region, - conditionIdx = r, - csInput = "fsusie", - credibleSetParam = cfg$credibleSetParam, - fitRetention = cfg$fitRetention - ) + bare <- if (is.null(fit)) { + .fmCvOnlyRow() + } else { + .fmPostprocessOne( + fit = fit, + method = "fsusie", + dataX = Xc, + dataY = NULL, + af = af, + region = region, + conditionIdx = r, + csInput = "fsusie", + credibleSetParam = cfg$credibleSetParam, + fitRetention = cfg$fitRetention + ) + } e <- if (is.null(cvM)) { bare } else { @@ -531,6 +551,28 @@ setMethod( context = key$context ) mvPrior <- .buildMvsusieReweightedPrior(mvFitParts, colnames(Ys), ddCut) + fit <- if (.jointFitsFullData(cfg)) { + .jointMvFullFit(Xc, Ys, cfg, args, mvPrior) + } else { + NULL + } + cvM <- .jointMvCv( + Xc, + Ys, + cfg, + args, + mvPrior, + mvFitParts, + mvCv, + ddCut, + verbose + ) + list(fit = fit, cvM = cvM) +} + +# The full-data mvSuSiE fit of one joint group, under the reweighted prior. +# @noRd +.jointMvFullFit <- function(Xc, Ys, cfg, args, mvPrior) { mvBaseArgs <- c( list( X = Xc, @@ -545,20 +587,15 @@ setMethod( "mvsusie", args$methodArgs[["mvsusie"]] ) - fit <- exec(fitMvsusie, !!!.splitMethodArgs(fitMvsusie, fitArgs)) |> + exec(fitMvsusie, !!!.splitMethodArgs(fitMvsusie, fitArgs)) |> .setFinemappingFitClass("mvsusie") - cvM <- .jointMvCv( - Xc, - Ys, - cfg, - args, - mvPrior, - mvFitParts, - mvCv, - ddCut, - verbose - ) - list(fit = fit, cvM = cvM) +} + +# Whether a joint run fits the full data as well as cross-validating, from the +# CrossValidationParam's fitFullData (TRUE when unset). +# @noRd +.jointFitsFullData <- function(cfg) { + isTRUE(cfg$crossValidationParam$fitFullData %||% TRUE) } # Per-fold mvsusie CV slice (reusing the mr.mash prior per fold), or NULL. @@ -574,12 +611,12 @@ setMethod( ddCut, verbose ) { - cv <- cfg$crossValidationArgs %||% list() + cv <- cfg$crossValidationParam %||% list() cvFolds <- cv$folds %||% 0L if (cvFolds <= 1L) { return(NULL) } - sp <- cfg$crossValidationArgs$samplePartition %||% mvCv$samplePartition + sp <- cfg$crossValidationParam$samplePartition %||% mvCv$samplePartition mvPriorCv <- .fmBuildMvsusiePriorCv(mvCv, mvFitParts, colnames(Ys), ddCut) cv <- .fmWeightsCv( Xc, @@ -592,7 +629,7 @@ setMethod( verbose = verbose, mvPrior = mvPrior, mvPriorCv = mvPriorCv, - numThreads = cv$threads %||% 1L, + numThreads = cv$numThreads %||% 1L, seed = cfg$seed ) .fmSliceCv(cv, "mvsusie") @@ -617,18 +654,22 @@ setMethod( return(NULL) } r <- match(i, survivors) - bare <- .fmPostprocessOne( - fit = fitted$fit, - method = "mvsusie", - dataX = Xc, - dataY = NULL, - af = af, - region = region, - conditionIdx = r, - csInput = "X", - credibleSetParam = cfg$credibleSetParam, - fitRetention = cfg$fitRetention - ) + bare <- if (is.null(fitted$fit)) { + .fmCvOnlyRow() + } else { + .fmPostprocessOne( + fit = fitted$fit, + method = "mvsusie", + dataX = Xc, + dataY = NULL, + af = af, + region = region, + conditionIdx = r, + csInput = "X", + credibleSetParam = cfg$credibleSetParam, + fitRetention = cfg$fitRetention + ) + } e <- if (is.null(fitted$cvM)) { bare } else { @@ -715,10 +756,10 @@ setMethod( } # One RSS per-condition FineMappingRow (csInput = "Xcorr"). -# @noRd -# `credibleSet` / `fitRetention` come off `cfg`, which is what the caller +# `credibleSetParam` / `fitRetention` come off `cfg`, which is what the caller # supplies; they were also declared as formals with no defaults, so nothing # could ever have passed them without erroring. `group` was unused too. +# @noRd .jointRssEntry <- function(r, fit, ldMat, cfg, region = NULL) { .fmPostprocessOne( fit = fit, @@ -960,8 +1001,30 @@ setMethod( if (identical(level, "none")) "slim" else level } +# The mr.mash RSS fit of one summary-statistics joint group, with the caller's +# per-method options split across the wrapper's own formals and the engine's +# arguments exactly as the per-tuple path splits them (.twasWeightCallArgs()). +# @noRd +.jointMrmashRssWeights <- function(group, cfg, args) { + callArgs <- .twasWeightCallArgs( + "mrmashRssWeights", + list( + stat = list( + z = .jgZ(group), + n = .jgN(group), + variantNames = .jgVariantIds(group) + ), + ldReference = .jgLdRef(group), + fitRetention = .jointRetentionLevel(cfg) + ), + .twasUserArgs(args$methodArgs, "mrmash") + ) + exec(mrmashRssWeights, !!!callArgs) +} + # Full-data TWAS weight fit for a joint group. Returns list(W, fitParts, vids); -# W is NULL (a CV-only run) when fitFullData is FALSE. +# W is NULL (a CV-only run) when the CrossValidationParam's fitFullData is +# FALSE. # @noRd .jointTwasFitFull <- function( Xc, @@ -973,12 +1036,7 @@ setMethod( stdz, nCond ) { - fitFullData <- if (is.null(cfg$fitFullData)) { - TRUE - } else { - isTRUE(cfg$fitFullData) - } - if (!fitFullData) { + if (!.jointFitsFullData(cfg)) { return(list(W = NULL, fitParts = NULL, vids = colnames(Xc))) } retention <- .jointRetentionLevel(cfg) @@ -1053,13 +1111,12 @@ setMethod( is_in(token, c(canonical[!is.na(canonical)], bare)) } -# @noRd # Whether this token is barred from cross-validation: not requested, or its # weights are all zero so there is nothing to validate. A fine-mapping method # with no recoverable per-fold fits is an error rather than a silent skip. # @noRd .jointTwasCvBlocked <- function(W, cfg, token) { - if (!.jointTwasCvRequested(cfg$crossValidationArgs$weightMethods, token)) { + if (!.jointTwasCvRequested(cfg$crossValidationParam$weightMethods, token)) { return(TRUE) } if (!is.null(W) && all(W == 0)) { @@ -1091,7 +1148,7 @@ setMethod( # per-call args first and the pipeline config as the fallback. # @noRd .jointTwasRunCv <- function(Xc, Yc, wm, args, cfg, cvFolds) { - cv <- cfg$crossValidationArgs %||% list() + cv <- cfg$crossValidationParam %||% list() maxCv <- if ((cv$maxVariants %||% -1) <= 0) { Inf } else { @@ -1105,15 +1162,16 @@ setMethod( weightMethods = wm, fitRetention = "slim", maxNumVariants = maxCv, - numThreads = cfg$crossValidationArgs$threads %||% 1, + numThreads = cfg$crossValidationParam$numThreads %||% 1, dataDrivenPriorMatricesCv = args$dataDrivenPriorMatricesCv, verbose = cfg$verbose %||% 1, seed = cfg$seed ) } +# @noRd .jointTwasCv <- function(Xc, Yc, wm, ma, W, args, cfg, token) { - cv <- cfg$crossValidationArgs %||% list() + cv <- cfg$crossValidationParam %||% list() cvFolds <- cv$folds %||% 0L if (cvFolds <= 1L) { return(NULL) @@ -1168,11 +1226,7 @@ setMethod( signature("SumStatsJointGroup", "TwasJointPipeline"), function(group, pipeline, token, args) { cfg <- .jpConfig(pipeline) - weights <- mrmashRssWeights( - stat = list(z = .jgZ(group), n = .jgN(group)), - LD = .jgLdMatrix(group), - fitRetention = .jointRetentionLevel(cfg) - ) + weights <- .jointMrmashRssWeights(group, cfg, args) vids <- rownames(weights) %||% rownames(.jgZ(group)) fitParts <- attr(weights, "fit") # A single-condition fit comes back as a bare vector; the per-condition @@ -1206,29 +1260,30 @@ setMethod( # columns for axes that actually vary are attached. # Fold the accumulator's per-row records into one collection: build each record # into a 1-row collection via `constructor`, then union them with the generic -# .rbindCollections (which aligns optional columns and sets the ldSketch slot). -# The only per-row transforms are: wrap `entry` in a list, and drop an NA joint* -# value so the 1-row collection omits that column (the union re-adds it, padded, -# only when some row is joint). Every other field -- study/context/trait/method, -# region, and any future column -- flows through untouched. -.buildJointResult <- function(constructor, records, ldSketch = NULL) { +# .rbindCollections (which aligns optional columns and sets the ldReference +# slot). The only per-row transforms are: wrap `entry` in a list, and drop an NA +# joint* value so the 1-row collection omits that column (the union re-adds it, +# padded, only when some row is joint). Every other field -- +# study/context/trait/method, region, and any future column -- flows through +# untouched. +.buildJointResult <- function(constructor, records, ldReference = NULL) { if (length(records) == 0L) { return(NULL) } parts <- map(records, .jointBuildRecordPart, constructor = constructor) - .rbindCollections(parts, ldSketch = ldSketch) + .rbindCollections(parts, ldReference = ldReference) } setMethod("construct", "FmJointPipeline", function(pipeline, records) { .buildJointResult( QtlFineMappingResult, records, - .jpConfig(pipeline)$ldSketch + .jpConfig(pipeline)$ldReference ) }) setMethod("construct", "TwasJointPipeline", function(pipeline, records) { - .buildJointResult(TwasWeights, records, .jpConfig(pipeline)$ldSketch) + .buildJointResult(TwasWeights, records, .jpConfig(pipeline)$ldReference) }) # ---- enumerators (pattern x dataForm -> list) -------------------- @@ -1275,7 +1330,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { verbose, label = "jointCrossContext", region = args$region, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam ) if (is.null(xy)) { return(NULL) @@ -1294,7 +1349,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { # cross-context / sumstats. .enumCrossContextSumstats <- function(data, scope, args = list()) { - ldSketch <- ldSketch(data) + ldReference <- ldReference(data) studyCol <- as.character(data$study) contextCol <- as.character(data$context) traitCol <- as.character(data$trait) @@ -1304,7 +1359,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { data = data, scope = scope, args = args, - ldSketch = ldSketch, + ldReference = ldReference, studyCol = studyCol, contextCol = contextCol, traitCol = traitCol @@ -1327,7 +1382,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { data, scope, args, - ldSketch, + ldReference, studyCol, contextCol, traitCol @@ -1343,7 +1398,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { s = s, scopedContexts = scopedContexts, args = args, - ldSketch = ldSketch, + ldReference = ldReference, studyCol = studyCol, contextCol = contextCol, traitCol = traitCol @@ -1359,7 +1414,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { s, scopedContexts, args, - ldSketch, + ldReference, studyCol, contextCol, traitCol @@ -1376,14 +1431,14 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { tupleRows, ctxNames, errorLabel = "jointCrossContext (QtlSumStats)", - ldSketch = ldSketch, + ldReference = ldReference, cutoffs = args$cutoffs ) new( "SumStatsJointGroup", conditions = tibble(study = s, context = ctxNames, trait = tid), Z = jz$Z, - ldSketch = ldSketch, + ldReference = ldReference, N = jz$nVec ) } @@ -1428,7 +1483,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { label = "jointCrossTrait", studyName = studyName, region = args$region, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam ) if (is.null(xy)) { return(NULL) @@ -1455,7 +1510,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { # cross-trait / sumstats. .enumCrossTraitSumstats <- function(data, scope, args = list()) { - ldSketch <- ldSketch(data) + ldReference <- ldReference(data) studyCol <- as.character(data$study) contextCol <- as.character(data$context) traitCol <- as.character(data$trait) @@ -1465,7 +1520,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { data = data, scope = scope, args = args, - ldSketch = ldSketch, + ldReference = ldReference, studyCol = studyCol, contextCol = contextCol, traitCol = traitCol @@ -1478,7 +1533,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { data, scope, args, - ldSketch, + ldReference, studyCol, contextCol, traitCol @@ -1490,7 +1545,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { s = s, scopedTraits = scope$traits[[s]], args = args, - ldSketch = ldSketch, + ldReference = ldReference, studyCol = studyCol, contextCol = contextCol, traitCol = traitCol @@ -1506,7 +1561,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { s, scopedTraits, args, - ldSketch, + ldReference, studyCol, contextCol, traitCol @@ -1523,14 +1578,14 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { tupleRows, trNames, errorLabel = "jointCrossTrait (QtlSumStats)", - ldSketch = ldSketch, + ldReference = ldReference, cutoffs = args$cutoffs ) new( "SumStatsJointGroup", conditions = tibble(study = s, context = cx, trait = trNames), Z = jz$Z, - ldSketch = ldSketch, + ldReference = ldReference, N = jz$nVec ) } @@ -1539,7 +1594,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { # disjoint samples). One group per (context, trait) present in >= 2 scoped # studies; the study axis varies -> "joint" + jointStudies. .enumCrossStudySumstats <- function(data, scope, args = list()) { - ldSketch <- ldSketch(data) + ldReference <- ldReference(data) cols <- list( study = as.character(data$study), context = as.character(data$context), @@ -1555,7 +1610,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { args = args, cols = cols, allTrs = allTrs, - ldSketch = ldSketch + ldReference = ldReference )) } @@ -1568,7 +1623,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { args, cols, allTrs, - ldSketch + ldReference ) { compact(map( allTrs, @@ -1578,7 +1633,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { args = args, cols = cols, cx = cx, - ldSketch = ldSketch + ldReference = ldReference )) } @@ -1591,16 +1646,24 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { args, cols, cx, - ldSketch + ldReference ) { - .enumCrossStudyGroup(data, scope, args, cols, cx, tid, ldSketch) + .enumCrossStudyGroup(data, scope, args, cols, cx, tid, ldReference) } # One cross-study joint group for a (context, trait) cell, or NULL when fewer # than two studies survive the scope filter -- a "joint" fit over one study is # just that study, so the cell contributes nothing. # @noRd -.enumCrossStudyGroup <- function(data, scope, args, cols, cx, tid, ldSketch) { +.enumCrossStudyGroup <- function( + data, + scope, + args, + cols, + cx, + tid, + ldReference +) { candidates <- which( cols$context == cx & cols$trait == tid & @@ -1624,7 +1687,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { tupleRows, stNames, errorLabel = "jointCrossStudy", - ldSketch = ldSketch, + ldReference = ldReference, cutoffs = args$cutoffs ) new( @@ -1635,7 +1698,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { trait = tid ), Z = jz$Z, - ldSketch = ldSketch, + ldReference = ldReference, N = jz$nVec ) } @@ -1658,7 +1721,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { verbose, label = "composed", region = args$region, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam ) if (is.null(xy)) { return(list()) @@ -1707,8 +1770,15 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { naAction ) { se <- molecularTraits(data, contexts = cx) - compact(map( + traits <- keep( intersect(scope$traits[[studyName]], rownames(se)), + .traitSelectedAt, + traitId = scope$traitSelection, + s = studyName, + cx = cx + ) + compact(map( + traits, .enumUnivariateGroupFor, data = data, args = args, @@ -1727,7 +1797,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { contexts = cx, traitId = tid, naAction = naAction, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam ) X <- if (is.null(args$region)) { .fmResidGeno( @@ -1735,14 +1805,14 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { contexts = cx, traitId = tid, cisWindow = args$cisWindow, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam ) } else { .fmResidGeno( data, contexts = cx, region = args$region, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam ) } common <- intersect(rownames(X), rownames(Y)) @@ -1762,7 +1832,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { # (fixed) axes form one group each. Reuses .enumerateComposedSumstatGroups. .enumComposedSumstats <- function(data, scope, args = list()) { axes <- args$axes %||% c("context", "trait") - ldSketch <- ldSketch(data) + ldReference <- ldReference(data) gi <- .enumerateComposedSumstatGroups(list(axes = axes), data, scope) if (is.null(gi)) { return(list()) @@ -1776,14 +1846,14 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { data = data, gi = gi, args = args, - ldSketch = ldSketch + ldReference = ldReference ))) } # One composed cell's group, or NULL when it spans fewer than two tuples -- # a "joint" fit over one tuple is just that tuple. # @noRd -.enumComposedGroupFor <- function(gIdx, data, gi, args, ldSketch) { +.enumComposedGroupFor <- function(gIdx, data, gi, args, ldReference) { if (length(gIdx) < 2L) { return(NULL) } @@ -1792,7 +1862,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { gIdx, map_chr(gIdx, .jointGroupColLabel, gi = gi), errorLabel = "composed (QtlSumStats)", - ldSketch = ldSketch, + ldReference = ldReference, cutoffs = args$cutoffs ) new( @@ -1803,7 +1873,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { trait = gi$traitCol[gIdx] ), Z = jz$Z, - ldSketch = ldSketch, + ldReference = ldReference, N = jz$nVec ) } @@ -1821,7 +1891,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { cfg <- .jpConfig(pipeline) cond <- .jgConditions(g) out <- list_assign(args, !!!.twasGroupFmArgs(args, cond)) - cv <- cfg$crossValidationArgs %||% list() + cv <- cfg$crossValidationParam %||% list() cvF <- cv$folds %||% 0L if (cvF <= 1L || !is(g, "IndividualJointGroup")) { return(out) @@ -1950,13 +2020,22 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { # -- one method -> per-condition entries; the SR-TWAS ensemble is a layer ON TOP # of that. .runJointCell <- function(cell, pipeline, data, scope, tokens, args = list()) { + # PC groups are labelled by component, not by trait: their enumerator + # applies the selection itself, to the traits the components come from. + selection <- if (identical(.jcPattern(cell), "topPc")) { + NULL + } else { + scope$traitSelection + } groups <- .jcEnumerate(cell)(data, scope, args) |> + map(.jointGroupSelect, traitId = selection) |> + compact() |> keep(.jointGroupMeetsMin, minGroup = .jcMinGroup(cell)) if (length(groups) == 0L) { return(NULL) } doEnsemble <- is(pipeline, "TwasJointPipeline") && - isTRUE(.jpConfig(pipeline)$ensembleArgs$enabled) + isTRUE(.jpConfig(pipeline)$ensembleParam$enabled) records <- list_flatten(map( groups, .jointCellGroup, @@ -1969,30 +2048,63 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { construct(pipeline, records) } +# The group less any (context, trait) condition a per-context `traitId` +# selection leaves out, or NULL when it leaves out every one. A NULL +# selection keeps the group whole. +# @noRd +.jointGroupSelect <- function(g, traitId) { + if (is.null(traitId)) { + return(g) + } + cond <- .jgConditions(g) + .jgKeepConditions( + g, + map_lgl( + seq_len(nrow(cond)), + .jointConditionSelectedAt, + cond = cond, + traitId = traitId + ) + ) +} + +# Whether the selection keeps condition `i` of a group's conditions table. +# @noRd +.jointConditionSelectedAt <- function(i, cond, traitId) { + .traitSelectedAt( + cond$trait[[i]], + traitId, + cond$study[[i]], + cond$context[[i]] + ) +} + # Fit every token for one joint group -> records, plus the optional SR-TWAS # ensemble layer (built ON TOP of the shared per-method fits, when >= 2 methods # produced entries). +# +# `grp` carries provenance: the ";"-joined members of each varying axis +# (identical on every per-context row of this group), and the window this +# group was analysed over -- the caller's region when one was named, else the +# group's own cis window (the union span of its traits +/- cisWindow). The +# window is recorded because the fit only ever sees whichever variants +# survived QC; it is read back by genomicRegion(). +# +# `fitArgs$af` / `fitArgs$region` are provenance the fitters cannot recover for +# themselves: `data` lives here but not inside fitJointGroup(), and the +# requested window is only ever known to the caller. Both are read back as +# args$af / args$region where the entries are post-processed. # @noRd .jointCellGroup <- function(g, pipeline, data, tokens, args, doEnsemble) { cond <- .jgConditions(g) - # Provenance: the ";"-joined members of each varying axis (identical on - # every per-context row of this group). grp <- list( cond = cond, js = .jointAxisMembers(cond, "study"), jc = .jointAxisMembers(cond, "context"), jt = .jointAxisMembers(cond, "trait"), - # The window this group was analysed over: the caller's region when - # one was named, else the group's own cis window (the union span of - # its traits +/- cisWindow). Recorded because the fit only ever sees - # whichever variants survived QC; read back by genomicRegion(). region = .jointGroupWindow(data, cond, args) ) fitArgs <- .twasGroupArgs(g, pipeline, args) - # Provenance the fitters cannot recover for themselves: `data` lives here - # but not inside fitJointGroup(), and the requested window is only ever - # known to the caller. Both are read back as args$af / args$region where - # the entries are post-processed. fitArgs$af <- .jointAfForGroup(data, g) fitArgs$region <- args$region perTokenEntries <- compact(set_names( @@ -2095,17 +2207,21 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { .twasEnsembleLayer <- function(group, perTokenEntries, cfg) { tokens <- names(perTokenEntries) Y <- .jgY(group) - r2Cut <- if (is.null(cfg$ensembleArgs$r2Threshold)) { + r2Cut <- if (is.null(cfg$ensembleParam$r2Threshold)) { 0.01 } else { - cfg$ensembleArgs$r2Threshold + cfg$ensembleParam$r2Threshold } - solver <- if (is.null(cfg$ensembleArgs$solver)) { + solver <- if (is.null(cfg$ensembleParam$solver)) { "quadprog" } else { - cfg$ensembleArgs$solver + cfg$ensembleParam$solver + } + alpha <- if (is.null(cfg$ensembleParam$alpha)) { + 1 + } else { + cfg$ensembleParam$alpha } - alpha <- if (is.null(cfg$ensembleArgs$alpha)) 1 else cfg$ensembleArgs$alpha stdz <- cfg$standardized %||% FALSE map( seq_len(nrow(.jgConditions(group))), @@ -2121,9 +2237,6 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { ) } -# Per-condition CV predictions + weights + R^2 across the group's methods. -# Returns list(preds, wts, rsq), skipping methods with no CV / weights. -# @noRd # One token's ensemble inputs for condition `r`, or NULL when it has no # cross-validated predictions or no weights to contribute. # @noRd @@ -2158,6 +2271,9 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { ) } +# Per-condition CV predictions + weights + R^2 across the group's methods. +# Returns list(preds, wts, rsq), skipping methods with no CV / weights. +# @noRd .twasEnsembleCollect <- function(r, perTokenEntries, tokens) { parts <- compact(map( tokens, @@ -2259,7 +2375,13 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { # @noRd .enumTopPcForContext <- function(cx, data, scope, args, studyName, naAction) { se <- molecularTraits(data, contexts = cx) - traits <- intersect(scope$traits[[studyName]], rownames(se)) + traits <- keep( + intersect(scope$traits[[studyName]], rownames(se)), + .traitSelectedAt, + traitId = scope$traitSelection, + s = studyName, + cx = cx + ) # PCA is undefined for a single trait, matching the fine-mapping rule. if (length(traits) < 2L) { return(list()) @@ -2269,7 +2391,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { contexts = cx, traitId = traits, naAction = naAction, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam ) scores <- .fmTopPcScores(Y, args$nPCs %||% 10L) if (is.null(scores)) { @@ -2298,14 +2420,14 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { contexts = cx, traitId = traits, cisWindow = args$cisWindow, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam )) } .fmResidGeno( data, contexts = cx, region = args$region, - residualizationArgs = args$residualizationArgs + residualizationParam = args$residualizationParam ) } @@ -2422,7 +2544,7 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { if (length(jointMethods) == 0L || length(parsedJointSpec) == 0L) { return(NULL) } - ldSketch <- .jpConfig(pipeline)$ldSketch + ldReference <- .jpConfig(pipeline)$ldReference isFm <- is(pipeline, "FmJointPipeline") results <- compact(map( parsedJointSpec, @@ -2441,9 +2563,9 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { rbindFn <- if (isFm) .rbindFineMappingResult else .rbindTwasWeights reduce( results, - .jointRbindWithSketch, + .jointRbindWithLdRef, rbindFn = rbindFn, - ldSketch = ldSketch + ldReference = ldReference ) } @@ -2472,8 +2594,8 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { } # @noRd -.jointRbindWithSketch <- function(acc, res, rbindFn, ldSketch) { - rbindFn(acc, res, ldSketch = ldSketch) +.jointRbindWithLdRef <- function(acc, res, rbindFn, ldReference) { + rbindFn(acc, res, ldReference = ldReference) } # Run all joint methods for ONE spec: resolve its scope (optionally @@ -2511,10 +2633,6 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { .runJointCell(cell, pipeline, data, scope, jointMethods, spArgs) } -# Region mode without an explicit traitId: restrict each study's scoped traits -# to the genes overlapping the locus (matches fineMappingPipeline). Gene coords -# are context-independent, so the first scoped context's SE supplies them. -# @noRd # One study's traits narrowed to those inside `region`; left alone when the # study has no context to read a phenotype from. # @noRd @@ -2531,6 +2649,10 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { ) } +# Region mode without an explicit traitId: restrict each study's scoped traits +# to the genes overlapping the locus (matches fineMappingPipeline). Gene coords +# are context-independent, so the first scoped context's SE supplies them. +# @noRd .jointRestrictRegionTraits <- function(scope, data, region) { studies <- names(scope$traits) list_assign( @@ -2596,9 +2718,6 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { ) } -# Build one record into a 1-row collection: wrap `entry` in a list and drop any -# NA joint* axis (so the union re-adds it, padded, only when some row is joint). -# @noRd # A joint-axis column holding a lone NA means "not a joint axis", so it is # dropped rather than carried as a column of NA. # @noRd @@ -2606,6 +2725,9 @@ setMethod("construct", "TwasJointPipeline", function(pipeline, records) { !is.null(value) && length(value) == 1L && is.na(value) } +# Build one record into a 1-row collection: wrap `entry` in a list and drop any +# NA joint* axis (so the union re-adds it, padded, only when some row is joint). +# @noRd .jointBuildRecordPart <- function(rec, constructor) { jointCols <- c("jointStudies", "jointContexts", "jointTraits") absent <- jointCols[map_lgl(rec[jointCols], .jointAxisIsAbsent)] diff --git a/R/jointSpecification.R b/R/jointSpecification.R index 3030c80e9..1d3e38f63 100644 --- a/R/jointSpecification.R +++ b/R/jointSpecification.R @@ -1,7 +1,7 @@ # ============================================================================= # Joint-specification grammar and ragged input-argument parsing for the -# fineMapping / twasWeights pipelines. Pure validation + normalization; -# no pipeline dispatch or fits live here. +# fineMapping / twasWeights pipelines, plus the joint-specification +# dispatchers and multi-region / multi-study merges they share. # ============================================================================= # ----------------------------------------------------------------------------- @@ -76,9 +76,6 @@ abort(msg) } -# Return character vector of contexts in `data` (across all studies when -# `study = NULL`, or for one study otherwise). -# @noRd # Concatenate a list of pieces into one, the way `c()` did, but safe on the # empty case (`list_c()` has no zero-length identity to return). # @noRd @@ -89,6 +86,9 @@ list_c(pieces) } +# Return character vector of contexts in `data` (across all studies when +# `study = NULL`, or for one study otherwise). +# @noRd .spListContexts <- function(data, studyName = NULL) { if (is(data, "QtlDataset")) { if (!is.null(studyName) && !identical(studyName, studyName(data))) { @@ -134,9 +134,6 @@ abort(msg) } -# Return character vector of traits in `data` (filtered by study and/or -# context when supplied). -# @noRd # The trait ids present in one context of a QtlDataset. # @noRd .spTraitsInContext <- function(context, data) { @@ -193,6 +190,9 @@ character(0) } +# Return character vector of traits in `data` (filtered by study and/or +# context when supplied). +# @noRd .spListTraits <- function(data, studyName = NULL, context = NULL) { if (is(data, "QtlDataset")) { return(.spListTraitsQtlDataset(data, studyName, context)) @@ -230,7 +230,6 @@ .spValidJointAxes <- c("study", "context", "trait") -# @noRd # --- parseJointSpecification helpers ---------------------------------------- # Extract (axes, scope) from a spec (character vector or named list). @@ -341,6 +340,7 @@ list(axes = axes, scope = scope) } +# @noRd parseJointSpecification <- function(jointSpecification, data) { if (is.null(jointSpecification)) { return(list()) @@ -371,10 +371,12 @@ parseJointSpecification <- function(jointSpecification, data) { # of selected contexts. NULL input is preserved as NULL ("all contexts"). # ----------------------------------------------------------------------------- -# @noRd # --- parseContexts helpers -------------------------------------------------- -# Vector form: apply uniformly to every study, filtering to availability. +# Vector form: apply uniformly to every study, filtering to availability. A +# context no study has is a mistake rather than a gap -- with one study it +# is the only way to name a missing context -- so that is an error; one that +# only some studies lack is dropped for those, with a warning. .parseContextsVec <- function(contexts, studies, data) { if (length(contexts) == 0L) { msg <- glue( @@ -383,6 +385,12 @@ parseJointSpecification <- function(jointSpecification, data) { ) abort(msg) } + unknown <- setdiff(contexts, .spListContexts(data)) + if (length(unknown) > 0L) { + unknownStr <- str_flatten(unknown, ", ") + msg <- glue("`contexts` names context(s) no study has: {unknownStr}") + abort(msg) + } set_names( map(studies, .parseContextsAvail, data = data, contexts = contexts), studies @@ -454,6 +462,7 @@ parseJointSpecification <- function(jointSpecification, data) { ) } +# @noRd parseContexts <- function(contexts, data) { if (is.null(contexts)) { return(NULL) @@ -482,7 +491,6 @@ parseContexts <- function(contexts, data) { # available traits). Validates IDs against `.spListTraits` lookups. # ----------------------------------------------------------------------------- -# @noRd # --- parseTraitIds helpers -------------------------------------------------- # Validate a per-(study, context) trait vector against the data. @@ -561,6 +569,7 @@ parseContexts <- function(contexts, data) { abort(msg) } +# @noRd parseTraitIds <- function(traitId, data) { if (is.null(traitId)) { return(NULL) @@ -614,379 +623,100 @@ parseTraitIds <- function(traitId, data) { } # ----------------------------------------------------------------------------- -# parseMethods -- normalize and validate the `methods` argument with optional -# `sumStatsMethods` / `qtlDatasetMethods` overrides. Validates: -# * mutual exclusivity (methods XOR split-by-data-form) -# * nested list structure (vector OR named list at each level; never both) -# * method names against the capability table -# * multi-axis methods may NOT appear at per-context or per-trait levels -# * mr.mash and mvsusie pipeline scope -# -# `caps` is the capability table for the pipeline (see -# `.fineMappingMethodCapabilities` and `.twasMethodCapabilities`). -# `multivariateMethods` is the subset of tokens whose `multivariate = TRUE`; -# used for per-context / per-trait placement rejection. -# `rejectedAtUser` is a character vector of tokens forbidden as user-requested -# methods on this pipeline (e.g. "mrmash" in fineMapping, "mvsusie" in twas). -# -# Returns a list with components: -# methods (NULL if not given) -# sumStatsMethods (NULL if not given) -# qtlDatasetMethods (NULL if not given) -# shape "primary" if `methods` was given, "split" otherwise +# Reading a parsed selection. The pipelines normalize `contexts` / `traitId` +# once, at entry, with parseContexts() / parseTraitIds(); these answer "what +# does it select here?" for one study, context or row. A study or context the +# selection does not mention keeps everything it has. # ----------------------------------------------------------------------------- -# Walk a (possibly nested) methods spec and return the depth at which leaf -# vectors live: 1 = top-level vector, 2 = per-study, 3 = per-(study,context), -# 4 = per-(study,context,trait). Returns a tibble-like list of (path, vec). -# `levelNames` is c("study", "context", "trait"); the leaf level is where -# the vector lives. -# @noRd -# Validate a named-list method node before recursing into it. -.spWalkValidate <- function(spec, label, depth, maxDepth) { - if (!is.list(spec)) { - cls <- class(spec)[[1L]] - msg <- glue( - "{label}: every node must be a character vector or a named ", - "list (got class '{cls}')" - ) - abort(msg) - } - if (depth >= maxDepth) { - msg <- glue( - "{label}: cannot nest below the trait level (depth ", - "{maxDepth} is the deepest a vector may appear at)." - ) - abort(msg) - } - specNm <- names(spec) - if (is.null(specNm) || any(str_length(specNm) == 0L, na.rm = TRUE)) { - d <- depth + 1L - msg <- glue( - "{label}: named-list nodes must have non-empty names at ", - "depth {d}" - ) - abort(msg) - } - if (length(spec) == 0L) { - d <- depth + 1L - msg <- glue("{label}: empty named list at depth {d}") - abort(msg) - } -} - -.spWalkMethods <- function( - spec, - label = "methods", - depth = 0L, - maxDepth = 3L, - path = character(0) -) { - if (is.character(spec)) { - return(list(list(depth = depth, path = path, methods = unique(spec)))) - } - .spWalkValidate(spec, label, depth, maxDepth) - out <- .spConcat(map( - names(spec), - .spWalkMethodsAt, - spec = spec, - label = label, - depth = depth, - maxDepth = maxDepth, - path = path - )) - out -} - -# Walk one named branch of a methods spec. +# The contexts selected for study `s` (possibly none), or NULL for all. # @noRd -.spWalkMethodsAt <- function(nm, spec, label, depth, maxDepth, path) { - .spWalkMethods( - spec[[nm]], - label = label, - depth = depth + 1L, - maxDepth = maxDepth, - path = c(path, nm) - ) +.selectedContexts <- function(contexts, s) { + if (is.null(contexts)) NULL else contexts[[s]] } -# Validate one leaf method vector: non-empty character, all tokens known (in -# `caps`), and none in `rejectedAtUser`. +# The traits selected for study `s` in context `cx`, or NULL for all. # @noRd -.jointValidateLeafVec <- function(vec, label, caps, rejectedAtUser) { - if (!is.character(vec) || length(vec) == 0L) { - msg <- glue( - "{label}: method vector must be a non-empty character vector" - ) - abort(msg) - } - bad <- setdiff(vec, names(caps)) - if (length(bad) > 0L) { - badStr <- str_flatten(bad, ", ") - knownStr <- str_flatten(names(caps), ", ") - msg <- glue( - "{label}: unknown method token(s): {badStr}. ", - "Known tokens: {knownStr}" - ) - abort(msg) - } - rejected <- intersect(vec, rejectedAtUser) - if (length(rejected) > 0L) { - rejectedStr <- str_flatten(rejected, ", ") - msg <- glue( - "{label}: method(s) cannot be user-requested on this ", - "pipeline: {rejectedStr}" - ) - abort(msg) +.selectedTraits <- function(traitId, s, cx) { + if (!is.list(traitId)) { + return(traitId) } - invisible(NULL) + entry <- traitId[[s]] + if (is.list(entry)) entry[[cx]] else entry } +# TRUE when trait `tid` is selected for study `s` in context `cx`. # @noRd -# --- parseMethods helpers --------------------------------------------------- - -# Validate mutual exclusivity of primary vs split method specs. -#' @importFrom checkmate assertCharacter -.parseMethodsValidateArgs <- function( - primaryGiven, - splitGiven, - sumStatsMethods, - qtlDatasetMethods -) { - if (primaryGiven && splitGiven) { - msg <- glue( - "Use either `methods` or (`sumStatsMethods` + ", - "`qtlDatasetMethods`), not both." - ) - abort(msg) - } - if (!primaryGiven && !splitGiven) { - msg <- glue( - "Specify `methods`, or both `sumStatsMethods` and ", - "`qtlDatasetMethods`." - ) - abort(msg) - } - if (splitGiven) { - if (is.null(sumStatsMethods) || is.null(qtlDatasetMethods)) { - msg <- glue( - "`sumStatsMethods` and `qtlDatasetMethods` must be given ", - "together." - ) - abort(msg) - } - assertCharacter(sumStatsMethods, min.len = 1L) - assertCharacter(qtlDatasetMethods, min.len = 1L) - } -} - -# Multi-axis methods may not appear below the per-study level. -.parseMethodsCheckMultiAxis <- function(leaf, lab, multivariateMethods) { - if (leaf$depth < 2L) { - return(invisible(NULL)) - } - bad <- intersect(leaf$methods, multivariateMethods) - if (length(bad) > 0L) { - badStr <- str_flatten(bad, ", ") - levelName <- c("per-study", "per-context", "per-trait")[[leaf$depth]] - msg <- glue( - "{lab}: multi-axis method(s) {badStr} cannot be assigned at ", - "the {levelName} level (multi-axis methods operate across ", - "axes)." - ) - abort(msg) - } +.traitSelectedAt <- function(tid, traitId, s, cx) { + sel <- .selectedTraits(traitId, s, cx) + is.null(sel) || is_in(tid, sel) } -# Study/context/trait keys of a method leaf must reference valid entities. -.parseMethodsCheckPath <- function(leaf, lab, data, studyNames) { - # The path is (study, context, trait); each level is only checked once the - # leaf is deep enough to name it -- a shallower leaf has no such entry to - # read, so the depth guard has to come before the extraction. - if (leaf$depth < 1L) { - return(invisible(NULL)) - } - s <- leaf$path[[1L]] - if (!is_in(s, studyNames)) { - msg <- glue("{lab}: unknown study '{s}'") - abort(msg) - } - if (leaf$depth < 2L) { - return(invisible(NULL)) - } - cx <- leaf$path[[2L]] - if (!is_in(cx, .spListContexts(data, s))) { - msg <- glue("{lab}: unknown context '{cx}' for study '{s}'") - abort(msg) - } - if (leaf$depth < 3L) { - return(invisible(NULL)) +# The traits study `s` takes part with across `studyContexts`, the contexts in +# play for it -- what a fit spanning those contexts draws on: the union of +# their selections, or NULL when any of them keeps all of its traits. +# @noRd +.selectedStudyTraits <- function(traitId, s, studyContexts) { + entry <- if (is.list(traitId)) traitId[[s]] else traitId + if (!is.list(entry)) { + return(entry) } - tr <- leaf$path[[3L]] - if (!is_in(tr, .spListTraits(data, studyName = s, context = cx))) { - msg <- glue( - "{lab}: unknown trait '{tr}' for (study '{s}', ", - "context '{cx}')" - ) - abort(msg) + if (!all(is_in(studyContexts, names(entry)))) { + return(NULL) } + unique(as.character(list_c(unname(entry[studyContexts])))) } -# Validate one method leaf (leaf vector + multi-axis + path references). -.parseMethodsCheckLeaf <- function( - leaf, - data, - caps, - multivariateMethods, - rejectedAtUser, - studyNames -) { - lab <- if (length(leaf$path) == 0L) { - "methods" - } else { - pathStr <- str_flatten(str_c("'", leaf$path, "'"), "$") - glue("methods[[{pathStr}]]") - } - .jointValidateLeafVec(leaf$methods, lab, caps, rejectedAtUser) - .parseMethodsCheckMultiAxis(leaf, lab, multivariateMethods) - .parseMethodsCheckPath(leaf, lab, data, studyNames) +# TRUE when some study's traits are chosen context by context. Only then can +# a (context, trait) pair inside a study's selected traits still be left out. +# @noRd +.hasNestedTraitSelection <- function(traitId) { + is.list(traitId) && any(map_lgl(traitId, is.list)) } -# Walk a primary `methods` tree and validate every leaf. -.parseMethodsWalked <- function( - methods, - data, - caps, - multivariateMethods, - rejectedAtUser -) { - walked <- .spWalkMethods(methods, label = "methods", maxDepth = 3L) - studyNames <- .spListStudies(data) - for (leaf in walked) { - .parseMethodsCheckLeaf( - leaf, - data, - caps, - multivariateMethods, - rejectedAtUser, - studyNames - ) - } +# Which rows of a summary-statistics collection the parsed `contexts` / +# `traitId` selection keeps. +# @noRd +.sumStatsRowsSelected <- function(data, contexts, traitId) { + map_lgl( + seq_len(nrow(data)), + .sumStatsRowSelectedAt, + studyCol = as.character(data$study), + contextCol = as.character(data$context), + traitCol = as.character(data$trait), + contexts = contexts, + traitId = traitId + ) } -parseMethods <- function( - methods, - sumStatsMethods = NULL, - qtlDatasetMethods = NULL, - data, - caps, - multivariateMethods, - rejectedAtUser = character(0) +# Whether the selection keeps row `i`. +# @noRd +.sumStatsRowSelectedAt <- function( + i, + studyCol, + contextCol, + traitCol, + contexts, + traitId ) { - primaryGiven <- !is.null(methods) - splitGiven <- !is.null(sumStatsMethods) || !is.null(qtlDatasetMethods) - .parseMethodsValidateArgs( - primaryGiven, - splitGiven, - sumStatsMethods, - qtlDatasetMethods - ) - if (splitGiven) { - .jointValidateLeafVec( - sumStatsMethods, - "sumStatsMethods", - caps, - rejectedAtUser - ) - .jointValidateLeafVec( - qtlDatasetMethods, - "qtlDatasetMethods", - caps, - rejectedAtUser - ) - } else { - .parseMethodsWalked( - methods, - data, - caps, - multivariateMethods, - rejectedAtUser - ) - } - list( - methods = methods, - sumStatsMethods = sumStatsMethods, - qtlDatasetMethods = qtlDatasetMethods, - shape = if (primaryGiven) "primary" else "split" - ) + s <- studyCol[[i]] + cx <- contextCol[[i]] + ctxSel <- .selectedContexts(contexts, s) + (is.null(ctxSel) || is_in(cx, ctxSel)) && + .traitSelectedAt(traitCol[[i]], traitId, s, cx) } - -# ----------------------------------------------------------------------------- -# validateMethodsVsJointSpec -- cross-validation. A per-axis method assignment -# at or below the axis being jointed in any spec contradicts user intent. -# E.g. axes = "context" + per-context methods = contradiction. Joint flags -# operate on axes that haven't been pinned to per-axis methods. -# -# The rule: for each jointSpec, every axis in `axes` must NOT be a level at -# which the methods list nests. Concretely: -# - "study" in axes -> methods must not be a named list keyed by study -# (i.e. methods must be a top-level vector OR the -# split form). Per-study methods would mean different -# methods per study, incompatible with cross-study -# joints. -# - "context" in axes -> no per-(study, context) nesting at any study. -# - "trait" in axes -> no per-(study, context, trait) nesting at any -# (study, context). -# ----------------------------------------------------------------------------- - +# Study `s`'s share of a parsed `traitId`, in a form the single-study entry +# point parses back to the same selection: unchanged when it is one vector +# for every study, else that study's entry, keyed by the study when it is +# chosen context by context. NULL when the study keeps all of its traits. # @noRd -validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { - # Split-form methods are flat per-data-form vectors -- nothing to check. - if (methodsParsed$shape == "split") { - return(invisible(NULL)) - } - if (length(jointSpecParsed) == 0L) { - return(invisible(NULL)) - } - methods <- methodsParsed$methods - if (is.character(methods)) { - return(invisible(NULL)) - } # top-level vector OK - - walked <- .spWalkMethods(methods, label = "methods", maxDepth = 3L) - # depth observed at leaves; max depth in the spec reflects nesting level. - maxDepth <- max(map_int(walked, "depth")) - - for (i in seq_along(jointSpecParsed)) { - axes <- jointSpecParsed[[i]]$axes - lab <- glue("jointSpecification[[{i}]]") - if (is_in("study", axes) && maxDepth >= 1L) { - msg <- glue( - "{lab}: `axes` includes 'study' but `methods` nests ", - "per-study; remove per-study method assignment when ", - "joining over studies." - ) - abort(msg) - } - if (is_in("context", axes) && maxDepth >= 2L) { - msg <- glue( - "{lab}: `axes` includes 'context' but `methods` nests ", - "per-context; remove per-context method assignment when ", - "joining over contexts." - ) - abort(msg) - } - if (is_in("trait", axes) && maxDepth >= 3L) { - msg <- glue( - "{lab}: `axes` includes 'trait' but `methods` nests ", - "per-trait; remove per-trait method assignment when ", - "joining over traits." - ) - abort(msg) - } +.studyTraitIdArg <- function(traitId, s) { + if (!is.list(traitId)) { + return(traitId) } - invisible(NULL) + entry <- traitId[[s]] + if (is.list(entry)) set_names(list(entry), s) else entry } # ============================================================================= @@ -1019,7 +749,6 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { avail = .spListContexts, scopeLimit = scope$context, userSpec = contexts, - requireCharacter = FALSE, data = data ), studies @@ -1027,16 +756,23 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { traitsOut <- set_names( map( studies, - .fmScopeAxis, - avail = .fmScopeTraits, + .fmScopeStudyTraits, scopeLimit = scope$trait, - userSpec = traitIds, - requireCharacter = TRUE, + traitIds = traitIds, + contextsOut = contextsOut, data = data ), studies ) - list(studies = studies, contexts = contextsOut, traits = traitsOut) + # A joint fit spans a study's contexts, so its traits are the union of + # the per-context selections; when the selection is per context, the + # cell runner then drops each (context, trait) pair it leaves out. + list( + studies = studies, + contexts = contextsOut, + traits = traitsOut, + traitSelection = if (.hasNestedTraitSelection(traitIds)) traitIds + ) } # `.spListTraits` takes the study by name, so give it the same (data, s) @@ -1046,41 +782,32 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { .spListTraits(data, studyName = s) } -# What a pipeline-level `contexts` / `traitIds` argument narrows study `s` to, -# or NULL when it says nothing about it. `requireCharacter` mirrors the trait -# axis, which ignores a non-character per-study entry. +# What a pipeline-level `contexts` argument narrows study `s` to, or NULL when +# it says nothing about it: one vector for every study, or parseContexts()'s +# per-study list. # @noRd -.fmScopeUserLimit <- function(spec, s, requireCharacter) { - if (is.null(spec)) { - return(NULL) - } - if (is.character(spec)) { - return(spec) - } - if (!is.list(spec) || !is_in(s, names(spec))) { - return(NULL) - } - entry <- spec[[s]] - if (!requireCharacter || is.character(entry)) entry else NULL +.fmScopeUserLimit <- function(spec, s) { + if (is.list(spec)) .selectedContexts(spec, s) else spec } # One axis for one study: everything it has, narrowed by the spec's scope and # then by the caller's argument. Each narrowing is an intersect, so the chain # is a fold rather than a variable rewritten in place. # @noRd -.fmScopeAxis <- function( - s, - avail, - scopeLimit, - userSpec, - requireCharacter, - data -) { +.fmScopeAxis <- function(s, avail, scopeLimit, userSpec, data) { + limits <- compact(list(scopeLimit, .fmScopeUserLimit(userSpec, s))) + reduce(limits, intersect, .init = avail(data, s)) +} + +# Study `s`'s traits: everything it has, narrowed by the spec's scope and by +# the caller's `traitId` over the contexts in play for it. +# @noRd +.fmScopeStudyTraits <- function(s, scopeLimit, traitIds, contextsOut, data) { limits <- compact(list( scopeLimit, - .fmScopeUserLimit(userSpec, s, requireCharacter) + .selectedStudyTraits(traitIds, s, contextsOut[[s]]) )) - reduce(limits, intersect, .init = avail(data, s)) + reduce(limits, intersect, .init = .fmScopeTraits(data, s)) } @@ -1094,7 +821,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { tupleRows, colLabels, errorLabel, - ldSketch = NULL, + ldReference = NULL, cutoffs = NULL ) { cols <- list( @@ -1113,7 +840,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { # entry's ids filters the group: Z is built against this vector and each # entry is checked against it. firstDf <- allDf[ - .panelKeepMask(allDf$variant_id, ldSketch, cutoffs, errorLabel), + .panelKeepMask(allDf$variant_id, ldReference, cutoffs, errorLabel), , drop = FALSE ] @@ -1196,20 +923,16 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { } -# Build a multi-context Y matrix for a single (study, trait) from an -# individual-level QtlDataset. Returns list(X, Y, perTraitContexts) or -# NULL when fewer than 2 contexts carry `tid` or the sample / complete-Y -# subset is too small to fit. -# @noRd # --- .buildIndividualCrossContextXy helpers --------------------------------- -# Scoped contexts in which a trait is present; NULL if fewer than 2. # Does context `cx` carry trait `tid`? # @noRd .ccContextHasTrait <- function(cx, data, tid) { is_in(tid, rownames(molecularTraits(data, contexts = cx))) } +# Scoped contexts in which a trait is present; NULL if fewer than 2. +# @noRd .crossContextPerTrait <- function(data, tid, scopedContexts, verbose, label) { perTraitContexts <- keep( scopedContexts, @@ -1283,6 +1006,11 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { ) } +# Build a multi-context Y matrix for a single (study, trait) from an +# individual-level QtlDataset. Returns list(X, Y, perTraitContexts) or +# NULL when fewer than 2 contexts carry `tid` or the sample / complete-Y +# subset is too small to fit. +# @noRd .buildIndividualCrossContextXy <- function( data, tid, @@ -1291,7 +1019,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { verbose, label, region = NULL, - residualizationArgs + residualizationParam ) { perTraitContexts <- .crossContextPerTrait( data, @@ -1309,13 +1037,13 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { tid, cisWindow, region, - residualizationArgs + residualizationParam ) Yres <- .fmResidPheno( data, contexts = perTraitContexts, traitId = tid, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) .crossContextAssemble(X, Yres, perTraitContexts, verbose, label, tid) } @@ -1335,12 +1063,8 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { traits[IRanges::overlapsAny(rr[traits], region)] } -# Build a multi-trait Y matrix for a single (study, context) from an -# individual-level QtlDataset. Returns list(X, Y, traitsHere, se) or NULL -# when fewer than 2 traits live in the context or the sample / complete-Y -# subset is too small. -# @noRd # Warn + signal skip when a context has fewer than 2 scoped traits. +# @noRd .crossTraitTooFew <- function(traitsHere, cx, studyName, verbose, label) { if (length(traitsHere) >= 2L) { return(FALSE) @@ -1356,6 +1080,11 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { TRUE } +# Build a multi-trait Y matrix for a single (study, context) from an +# individual-level QtlDataset. Returns list(X, Y, traitsHere, se) or NULL +# when fewer than 2 traits live in the context or the sample / complete-Y +# subset is too small. +# @noRd .buildIndividualCrossTraitXy <- function( data, cx, @@ -1365,7 +1094,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { label, studyName, region = NULL, - residualizationArgs + residualizationParam ) { se <- molecularTraits(data, contexts = cx) # scopedTraits is already region-restricted upstream (.runJointSpecs) when @@ -1380,13 +1109,13 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { traitsHere, cisWindow, region, - residualizationArgs + residualizationParam ) allY <- .fmResidPheno( data, contexts = cx, traitId = traitsHere, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) common <- intersect(rownames(allX), rownames(allY)) if (length(common) < 2L) { @@ -1407,18 +1136,16 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { } -# Build a composed-axes (context, trait) X/Y for individual-level -# QtlDataset. Returns list(X, Y, tuples) or NULL. -# @noRd # Residualized genotype for the cross-* / composed builders (cis-window or an # explicit region). Shared by the individual multi-axis Xy builders. +# @noRd .buildResidGeno <- function( data, contexts, traitId, cisWindow, region, - residualizationArgs + residualizationParam ) { if (is.null(region)) { .fmResidGeno( @@ -1426,14 +1153,14 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { contexts = contexts, traitId = traitId, cisWindow = cisWindow, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) } else { .fmResidGeno( data, contexts = contexts, region = region, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) } } @@ -1515,7 +1242,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { allTraits, cisWindow, region, - residualizationArgs + residualizationParam ) { # Genotypes first, as the inline version did: reading them is what # fails on a missing panel file, and that error must stay the one the @@ -1526,13 +1253,13 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { allTraits, cisWindow, region, - residualizationArgs + residualizationParam ) resid <- .fmResidPheno( data, contexts = allContexts, traitId = allTraits, - residualizationArgs = residualizationArgs + residualizationParam = residualizationParam ) list( X = allX, @@ -1545,6 +1272,9 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { ) } +# Build a composed-axes (context, trait) X/Y for individual-level +# QtlDataset. Returns list(X, Y, tuples) or NULL. +# @noRd .buildComposedIndividualXy <- function( data, scope, @@ -1553,7 +1283,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { verbose, label, region = NULL, - residualizationArgs + residualizationParam ) { tuples <- .composedTuples(data, scope, studyName, verbose, label) if (is.null(tuples)) { @@ -1567,7 +1297,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { allTraits, cisWindow, region, - residualizationArgs + residualizationParam ) allX <- sides$X YresList <- sides$YresList @@ -1662,8 +1392,6 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { ) } -# Top-level joint dispatcher for fineMappingPipeline(QtlDataset). -# @noRd # Merge per-region QtlFineMappingResult collections (same keys across regions) # into one by merging each (study, context, trait, method) row's entry via # .fmMergeEntries (per-region susieFit list + renumbered credible sets). @@ -1690,7 +1418,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { entry = mergedEntries ), .jointCols(base), - list(ldSketch = NULL) + list(ldReference = NULL) ) exec(QtlFineMappingResult, !!!qfmrArgs) } @@ -1726,22 +1454,12 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { ) } -# Shared tail of the MultiStudyQtlDataset fineMapping / twasWeights pipeline -# methods: recurse into each embedded QtlDataset then the embedded QtlSumStats, -# row-bind the per-study results, build the per-tuple result object, and combine -# it with any joint-specification result. `perStudyFn` / `sumStatsFn` are the -# (method-specific) single-study recursions; `rbindFn` / `resultCtor` / -# `pipelineName` supply the per-pipeline pieces. Each method keeps its own -# (divergent) method-gating / joint-dispatch preamble and passes the computed -# `jointResult` in. -# @noRd # --- .multiStudyPipelineDriver helpers -------------------------------------- -# Accumulate per-study + sumstats results (tracking the embedded LD sketch). # rbind two results with no LD sketch -- the per-study combine. # @noRd .msRbindNoLd <- function(acc, x, rbindFn) { - rbindFn(acc, x, ldSketch = NULL) + rbindFn(acc, x, ldReference = NULL) } # Fold per-study results together, skipping studies that produced nothing. @@ -1755,6 +1473,8 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { reduce(kept, .msRbindNoLd, rbindFn = rbindFn) } +# Accumulate per-study + sumstats results (tracking the embedded LD sketch). +# @noRd .msDriverAccumulate <- function( qtlDatasets, sumStats, @@ -1771,19 +1491,19 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { if (is.null(ssRes)) { return(list(out = fromStudies, embeddedLd = NULL)) } - embeddedLd <- ldSketch(ssRes) + embeddedLd <- ldReference(ssRes) out <- if (is.null(fromStudies)) { ssRes } else { - rbindFn(fromStudies, ssRes, ldSketch = embeddedLd) + rbindFn(fromStudies, ssRes, ldReference = embeddedLd) } list(out = out, embeddedLd = embeddedLd) } # Reconstruct the per-tuple result object from the accumulated rows. .msDriverPerTuple <- function(out, resultCtor, embeddedLd) { - # ldSketch: NULL if all studies were individual-level; the embedded - # sumStats's ldSketch otherwise. + # ldReference: NULL if all studies were individual-level; the embedded + # sumStats's ldReference otherwise. ctorArgs <- c( list( study = as.character(out$study), @@ -1793,11 +1513,20 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { entry = .collectionEntries(out) ), .jointCols(out), - list(ldSketch = embeddedLd) + list(ldReference = embeddedLd) ) exec(resultCtor, !!!ctorArgs) } +# Shared tail of the MultiStudyQtlDataset fineMapping / twasWeights pipeline +# methods: recurse into each embedded QtlDataset then the embedded QtlSumStats, +# row-bind the per-study results, build the per-tuple result object, and combine +# it with any joint-specification result. `perStudyFn` / `sumStatsFn` are the +# (method-specific) single-study recursions; `rbindFn` / `resultCtor` / +# `pipelineName` supply the per-pipeline pieces. Each method keeps its own +# (divergent) method-gating / joint-dispatch preamble and passes the computed +# `jointResult` in. +# @noRd .multiStudyPipelineDriver <- function( data, jointResult, @@ -1837,7 +1566,7 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { if (is.null(perTupleResult)) { return(jointResult) } - rbindFn(perTupleResult, jointResult, ldSketch = embeddedLd) + rbindFn(perTupleResult, jointResult, ldReference = embeddedLd) } # Synthesize a jointSpecification for the AUTO-DETECTION path (no explicit @@ -1859,366 +1588,201 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { list() } } - - +# ---- fine-mapping joint dispatch -------------------------------------------- +# +# Every fine-mapping joint dispatch hands the engine a pipeline config and an +# `args` list built from ONE record: the pipeline's own joint record +# (.fmIndividualJointCfg() / .fmQssConfigs()), or, for a component of a +# MultiStudyQtlDataset, that record with the component's data in it. +# Re-listing the settings at each dispatch layer is how they went missing -- +# the per-study dispatch dropped cross-validation, residualization, the +# screen, fitRetention and the seed, and passed the rest positionally into +# the wrong slots. + +# Run joint specs on a QtlDataset, once per region block, merging per (study, +# context, trait, method) across the blocks. A study axis has no meaning on +# individual-level data, and rejecting it up front keeps the error ahead of +# the fit. +# @noRd .fmDispatchJointSpecsQtlDataset <- function( parsedJointSpec, - data, - methods, - contexts, - traitIds, - cisWindow, - credibleSetParam = CredibleSetParam(includeAllCs = FALSE), - verbose, - methodArgs = list(), - xRegions = list(NULL), - mrmashPrior = NULL, - dataDrivenPriorWeightsCutoff = 1e-10, - crossValidationArgs = CrossValidationParam(), - residualizationArgs = ResidualizationParam(), - pipCutoffToSkip = 0, - fineMappingResult = NULL, - fitRetention = c("slim", "none", "full"), - seed = NULL + tokens, + methodArgs, + cfg ) { - fitRetention <- arg_match(fitRetention) - # Run the joint dispatch once per region block, then merge per - # (study, context, trait, method) across regions. A single block (cis or - # jointRegions=TRUE concatenated) returns its result directly. - # xRegions is deliberately absent: each region is supplied per call. - perRegion <- map( - xRegions, + .jointRejectStudyOnIndividual(parsedJointSpec) + perRegion <- compact(map( + cfg$xRegions, .fmDispatchJointSpecRegion, parsedJointSpec = parsedJointSpec, - data = data, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - credibleSetParam = credibleSetParam, - verbose = verbose, + tokens = tokens, methodArgs = methodArgs, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - crossValidationArgs = crossValidationArgs, - residualizationArgs = residualizationArgs, - pipCutoffToSkip = pipCutoffToSkip, - fineMappingResult = fineMappingResult, - fitRetention = fitRetention, - seed = seed - ) |> - compact() + cfg = cfg + )) .fmCombineRegionResults(perRegion) } -# Merge the per-region joint results by (study, context, trait, method). A -# single block -- cis, or jointRegions = TRUE concatenated -- is returned -# directly rather than merged with itself. +# One region block of .fmDispatchJointSpecsQtlDataset(). # @noRd -.fmCombineRegionResults <- function(perRegion) { - if (length(perRegion) == 0L) { - return(NULL) - } - if (length(perRegion) == 1L) { - return(perRegion[[1L]]) - } - .fmMergeResultsByKey(perRegion) +.fmDispatchJointSpecRegion <- function( + region, + parsedJointSpec, + tokens, + methodArgs, + cfg +) { + .runJointSpecs( + parsedJointSpec, + cfg$data, + dataForm = "individual", + pipeline = .fmJointPipeline(cfg, cfg$crossValidationParam, NULL), + jointMethods = intersect(tokens, c("mvsusie", "fsusie")), + contexts = cfg$contexts, + traitIds = cfg$traitId, + args = .fmJointIndividualArgs(cfg, methodArgs, region) + ) } -# FmJointPipeline for individual-level fine-mapping, built from the call params. -.fmJointPipeline <- function( - credibleSetParam = CredibleSetParam(includeAllCs = FALSE), - dataDrivenPriorWeightsCutoff, - crossValidationArgs, - residualizationArgs, - verbose, - fitRetention = c("slim", "none", "full"), - seed -) { - fitRetention <- arg_match(fitRetention) +# The FmJointPipeline a joint run fits under: the settings the fitters read +# off the pipeline. Summary statistics pass the no-CV record (there are no +# samples to hold out) and the collection's LD sketch; individual-level data +# pass the caller's resolved CV record and no sketch. +# @noRd +.fmJointPipeline <- function(cfg, crossValidationParam, ldReference) { new( "FmJointPipeline", config = list( - credibleSetParam = credibleSetParam, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - crossValidationArgs = crossValidationArgs, - residualizationArgs = residualizationArgs, - verbose = verbose, - fitRetention = fitRetention, - seed = seed, - ldSketch = NULL + credibleSetParam = cfg$credibleSetParam, + dataDrivenPriorWeightsCutoff = cfg$dataDrivenPriorWeightsCutoff, + crossValidationParam = crossValidationParam, + verbose = cfg$verbose, + fitRetention = cfg$fitRetention, + seed = cfg$seed, + ldReference = ldReference ) ) } -# The joint pipeline record, once the spec has been checked against the -# individual-level form -- a study axis has no meaning there, and building -# a pipeline for a spec that names one would defer the error past the fit. +# The engine `args` of an individual-level joint run over one region block. +# Residualization rides here rather than on the pipeline config: the group +# enumerators read it while extracting X and Y. # @noRd -.fmJointPipelineChecked <- function( - parsedJointSpec, - credibleSetParam, - dataDrivenPriorWeightsCutoff, - crossValidationArgs, - residualizationArgs, - verbose, - fitRetention, - seed -) { - .jointRejectStudyOnIndividual(parsedJointSpec) - .fmJointPipeline( - credibleSetParam = credibleSetParam, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - crossValidationArgs = crossValidationArgs, - residualizationArgs = residualizationArgs, - verbose = verbose, - fitRetention = fitRetention, - seed = seed - ) -} - -# Engine routing (jointEngine.R) for one region block; the caller loops the -# regions. -# @noRd -.fmDispatchJointSpecsQtlDatasetOneRegion <- function( - parsedJointSpec, - data, - methods, - contexts, - traitIds, - cisWindow, - verbose, - methodArgs = list(), - region = NULL, - mrmashPrior = NULL, - dataDrivenPriorWeightsCutoff = 1e-10, - crossValidationArgs = CrossValidationParam(), - residualizationArgs = ResidualizationParam(), - pipCutoffToSkip = 0, - fineMappingResult = NULL, - seed = NULL, - credibleSetParam, - fitRetention -) { - pipeline <- .fmJointPipelineChecked( - parsedJointSpec, - credibleSetParam = credibleSetParam, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - crossValidationArgs = crossValidationArgs, - residualizationArgs = residualizationArgs, - verbose = verbose, - fitRetention = fitRetention, - seed = seed - ) - .runJointSpecs( - parsedJointSpec, - data, - dataForm = "individual", - pipeline = pipeline, - jointMethods = intersect(methods, c("mvsusie", "fsusie")), - contexts = contexts, - traitIds = traitIds, - args = list( - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - methodArgs = methodArgs, - cisWindow = cisWindow, - region = region, - verbose = verbose, - pipCutoffToSkip = pipCutoffToSkip, - cache = fineMappingResult - ) +.fmJointIndividualArgs <- function(cfg, methodArgs, region) { + list( + mrmashPrior = cfg$mrmashPrior, + dataDrivenPriorWeightsCutoff = cfg$dataDrivenPriorWeightsCutoff, + methodArgs = methodArgs, + cisWindow = cfg$cisWindow, + region = region, + verbose = cfg$verbose, + pipCutoffToSkip = cfg$screen, + cache = cfg$fineMappingResult, + residualizationParam = cfg$residualizationParam ) } - -# Top-level joint dispatcher for fineMappingPipeline(QtlSumStats). +# Run joint specs on a QtlSumStats collection. `cfg` is the summary-statistics +# joint record (.fmQssConfigs()$joint), or a MultiStudy record with the +# collection as its data. # @noRd -# FmJointPipeline for summary-statistics fine-mapping (RSS: no sample folds; -# LD sketch drawn from the data). -.fmSumStatsPipeline <- function( - data, - credibleSetParam, - dataDrivenPriorWeightsCutoff, - verbose, - fitRetention -) { - new( - "FmJointPipeline", - config = list( - credibleSetParam = credibleSetParam, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - verbose = verbose, - fitRetention = fitRetention, - crossValidationArgs = .cvResolve(CrossValidationParam()), - ldSketch = ldSketch(data) - ) - ) -} - .fmDispatchJointSpecsQtlSumStats <- function( parsedJointSpec, - data, - methods, - contexts, - traitIds, - verbose, - methodArgs = list(), - mrmashPrior = NULL, - dataDrivenPriorWeightsCutoff = 1e-10, - fineMappingResult = NULL, - panelFilterParam = PanelFilterParam(), - credibleSetParam, - fitRetention + tokens, + methodArgs, + cfg ) { - # Engine routing (jointEngine.R): the dispatch table + .runJointCell replace - # the per-axis switch + the cross-context/trait/study/composed leaf - # dispatchers. - pipeline <- .fmSumStatsPipeline( - data = data, - credibleSetParam = credibleSetParam, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - verbose = verbose, - fitRetention = fitRetention - ) .runJointSpecs( parsedJointSpec, - data, + cfg$data, dataForm = "sumstats", - pipeline = pipeline, - jointMethods = intersect(methods, c("mvsusie", "fsusie")), - contexts = contexts, - traitIds = traitIds, + pipeline = .fmJointPipeline( + cfg, + .cvResolve(CrossValidationParam()), + ldReference(cfg$data) + ), + jointMethods = intersect(tokens, c("mvsusie", "fsusie")), + contexts = cfg$contexts, + traitIds = cfg$traitId, args = list( - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, + mrmashPrior = cfg$mrmashPrior, + dataDrivenPriorWeightsCutoff = cfg$dataDrivenPriorWeightsCutoff, methodArgs = methodArgs, - verbose = verbose, - cache = fineMappingResult, - cutoffs = .panelCutoffs(panelFilterParam) + verbose = cfg$verbose, + cache = cfg$fineMappingResult, + cutoffs = .panelCutoffs( + cfg$panelFilterParam %||% PanelFilterParam() + ) ) ) } - -# Top-level joint dispatcher for fineMappingPipeline(MultiStudyQtlDataset). -# Routes per-component AND per-axis: a spec with `axes = "study"` only -# touches the sumStats slot; `axes = "context"` and `axes = "trait"` run -# on every component. +# Joint dispatch for a MultiStudyQtlDataset. Per component AND per axis: a +# spec with a `study` axis only touches the summary statistics, while the +# other specs run on every individual-level component -- each under the same +# settings, with the component's own data (its genotype-filter overrides +# applied, as the per-study recursion applies them) -- and on the summary +# statistics too. `cfg` is the individual-level joint record plus +# `genotypeFilterParam` and `panelFilterParam`, with the collection as data. # @noRd -# --- .fmDispatchJointSpecsMultiStudy helpers -------------------------------- - -# Partition joint specs into those with a `study` axis and the rest. -.fmSplitStudyAxisSpecs <- function(parsedJointSpec) { - hasStudy <- map_lgl(parsedJointSpec, .jsHasStudyAxis) - list( - study = parsedJointSpec[hasStudy], - nonStudy = parsedJointSpec[!hasStudy] - ) -} - -# Note that individual-level studies are excluded from cross-study fits. -.fmMultiStudyWarnExcluded <- function(studyAxisSpecs, qtlDatasets, verbose) { - if ( - length(studyAxisSpecs) > 0L && length(qtlDatasets) > 0L && verbose >= 1 - ) { - qdNames <- str_flatten(names(qtlDatasets), ", ") - msg <- glue( - "jointCrossStudy: excluding individual-level studies ", - "({qdNames}) from cross-study fits (no LD sketch available); ", - "sumstats studies participate." - ) - inform(msg) - } -} - -# Fine-map the non-study-axis specs on each individual-level QtlDataset. -.fmMultiStudyQtlLoop <- function( - nonStudyAxisSpecs, - qtlDatasets, - methods, - contexts, - traitIds, - cisWindow, - credibleSetParam, - verbose, +.fmDispatchJointSpecsMultiStudy <- function( + parsedJointSpec, + tokens, methodArgs, - xRegions, - mrmashPrior, - dataDrivenPriorWeightsCutoff + cfg ) { - if (length(nonStudyAxisSpecs) == 0L) { - return(NULL) + qtlDatasets <- qtlDatasets(cfg$data) + specs <- .fmSplitStudyAxisSpecs(parsedJointSpec) + .fmMultiStudyWarnExcluded(specs$study, qtlDatasets, cfg$verbose) + out <- if (length(specs$nonStudy) == 0L) { + NULL + } else { + .msFoldStudyResults( + map( + qtlDatasets, + .fmStudyJointResult, + specs = specs$nonStudy, + tokens = tokens, + methodArgs = methodArgs, + cfg = cfg + ), + .rbindFineMappingResult + ) } - .msFoldStudyResults( - map( - qtlDatasets, - .fmDispatchForStudy, - nonStudyAxisSpecs = nonStudyAxisSpecs, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - credibleSetParam = credibleSetParam, - verbose = verbose, - methodArgs = methodArgs, - xRegions = xRegions, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff - ), - .rbindFineMappingResult + .fmMultiStudySumStats( + parsedJointSpec, + specs$study, + out, + tokens, + methodArgs, + cfg ) } -# `map()` hands the dataset first; the dispatcher wants the specs first. +# One individual-level component's joint fits: the shared record with the +# component as its data. # @noRd -.fmDispatchForStudy <- function( - qd, - nonStudyAxisSpecs, - methods, - contexts, - traitIds, - cisWindow, - verbose, - methodArgs, - xRegions, - mrmashPrior, - dataDrivenPriorWeightsCutoff, - credibleSetParam -) { - .fmDispatchJointSpecsQtlDataset( - nonStudyAxisSpecs, - qd, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - credibleSetParam = credibleSetParam, - verbose = verbose, - methodArgs = methodArgs, - xRegions = xRegions, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff +.fmStudyJointResult <- function(qd, specs, tokens, methodArgs, cfg) { + studyCfg <- list_assign( + cfg, + data = .qtlApplyFilterOverrides(qd, cfg$genotypeFilterParam) ) + .fmDispatchJointSpecsQtlDataset(specs, tokens, methodArgs, studyCfg) } -# Fine-map all specs on the sumstats collection; rbind onto `out`. +# Fine-map every spec on the summary-statistics component and rbind onto the +# individual-level results `out`. +# @noRd .fmMultiStudySumStats <- function( parsedJointSpec, - sumStats, studyAxisSpecs, out, - methods, - contexts, - traitIds, - verbose, + tokens, methodArgs, - mrmashPrior, - dataDrivenPriorWeightsCutoff, - credibleSetParam + cfg ) { + sumStats <- sumStats(cfg$data) if (is.null(sumStats)) { - if (length(studyAxisSpecs) > 0L && verbose >= 1) { + if (length(studyAxisSpecs) > 0L && cfg$verbose >= 1) { msg <- glue( "jointCrossStudy: no sumStats slot present on this ", "MultiStudyQtlDataset; cross-study specs produce no result." @@ -2229,81 +1793,66 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { } ssRes <- .fmDispatchJointSpecsQtlSumStats( parsedJointSpec, - sumStats, - methods = methods, - contexts = contexts, - traitIds = traitIds, - credibleSetParam = credibleSetParam, - verbose = verbose, - methodArgs = methodArgs, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff + tokens, + methodArgs, + list_assign(cfg, data = sumStats) ) if (is.null(ssRes)) { return(out) } - embeddedLd <- ldSketch(ssRes) if (is.null(out)) { - ssRes - } else { - .rbindFineMappingResult(out, ssRes, ldSketch = embeddedLd) + return(ssRes) } + .rbindFineMappingResult(out, ssRes, ldReference = ldReference(ssRes)) } -.fmDispatchJointSpecsMultiStudy <- function( - parsedJointSpec, - data, - methods, - contexts, - traitIds, - cisWindow, - credibleSetParam, - verbose, - methodArgs = list(), - xRegions = list(NULL), - mrmashPrior = NULL, - dataDrivenPriorWeightsCutoff = 1e-10 -) { - qtlDatasets <- qtlDatasets(data) - sumStats <- sumStats(data) - specs <- .fmSplitStudyAxisSpecs(parsedJointSpec) - .fmMultiStudyWarnExcluded(specs$study, qtlDatasets, verbose) - out <- .fmMultiStudyQtlLoop( - specs$nonStudy, - qtlDatasets, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - credibleSetParam = credibleSetParam, - verbose = verbose, - methodArgs = methodArgs, - xRegions = xRegions, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff - ) - .fmMultiStudySumStats( - parsedJointSpec, - sumStats, - specs$study, - out, - methods = methods, - contexts = contexts, - traitIds = traitIds, - verbose = verbose, - methodArgs = methodArgs, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff + +# Merge the per-region joint results by (study, context, trait, method). A +# single block -- cis, or jointRegions = TRUE concatenated -- is returned +# directly rather than merged with itself. +# @noRd +.fmCombineRegionResults <- function(perRegion) { + if (length(perRegion) == 0L) { + return(NULL) + } + if (length(perRegion) == 1L) { + return(perRegion[[1L]]) + } + .fmMergeResultsByKey(perRegion) +} + + +# --- .fmDispatchJointSpecsMultiStudy helpers -------------------------------- + +# Partition joint specs into those with a `study` axis and the rest. +.fmSplitStudyAxisSpecs <- function(parsedJointSpec) { + hasStudy <- map_lgl(parsedJointSpec, .jsHasStudyAxis) + list( + study = parsedJointSpec[hasStudy], + nonStudy = parsedJointSpec[!hasStudy] ) } +# Note that individual-level studies are excluded from cross-study fits. +.fmMultiStudyWarnExcluded <- function(studyAxisSpecs, qtlDatasets, verbose) { + if ( + length(studyAxisSpecs) > 0L && length(qtlDatasets) > 0L && verbose >= 1 + ) { + qdNames <- str_flatten(names(qtlDatasets), ", ") + msg <- glue( + "jointCrossStudy: excluding individual-level studies ", + "({qdNames}) from cross-study fits (no LD reference available); ", + "sumstats studies participate." + ) + inform(msg) + } +} + # ============================================================================= # TWAS-weights dispatchers # ============================================================================= -# Top-level joint dispatcher for twasWeightsPipeline(QtlDataset). -# @noRd # Merge per-region TwasWeights collections (same keys across regions) into one # by concatenating each (study, context, trait, method) row's entry via # .twasMergeRegionEntries (stacked weights + flat per-region cvResult). @@ -2338,243 +1887,151 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { exec(TwasWeights, !!!twArgs) } -.twasDispatchJointSpecsQtlDataset <- function( - parsedJointSpec, - data, - methods, - contexts, - traitIds, - cisWindow, - weightsDataType, - verbose, - xRegions = list(NULL), - fitRetention = c("slim", "none", "full"), - seed = NULL -) { - fitRetention <- arg_match(fitRetention) - # Run the joint dispatch once per region block, then merge per - # (study, context, trait, method) across regions. A single block (cis or - # jointRegions=TRUE concatenated) returns its result directly. - allRegions <- map( - xRegions, - .twasDispatchJointSpecRegion, - parsedJointSpec = parsedJointSpec, - data = data, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - weightsDataType = weightsDataType, - verbose = verbose, - fitRetention = fitRetention, - seed = seed - ) - allLabs <- map_chr(xRegions, .twasRegionLabel) - keep <- !map_lgl(allRegions, is.null) - perRegion <- allRegions[keep] - labs <- allLabs[keep] - if (length(perRegion) == 0L) { - return(NULL) - } - if (length(perRegion) == 1L) { - return(perRegion[[1L]]) - } - .twasMergeResultsByKey(perRegion, labs) -} +# ---- TWAS joint dispatch ---------------------------------------------------- +# +# An explicit jointSpecification fits under the same pipeline marker and the +# same engine `args` as the per-tuple multivariate grid (.twasJointSetup() / +# .twasRunJointSpecGrid()), so cross-validation, residualization, +# fitRetention, the per-method options and the mash prior reach an explicit +# joint fit exactly as they reach the rest. The explicit path used to build a +# bare pipeline record of its own -- default cross-validation, no +# fitRetention -- and to hand the engine no residualization and no options. -.twasDispatchJointSpecsQtlDatasetOneRegion <- function( +# Explicit joint specs (mr.mash) on a QtlDataset. +# @noRd +.twasDispatchJointSpecsQtlDataset <- function( parsedJointSpec, - data, - methods, + tokens, + marker, + ctx, contexts, - traitIds, - cisWindow, - weightsDataType, - verbose, - region = NULL, - fitRetention = c("slim", "none", "full"), - seed = NULL + traitIds ) { - fitRetention <- arg_match(fitRetention) - # Engine routing (jointEngine.R); one region block (the caller loops - # regions). .jointRejectStudyOnIndividual(parsedJointSpec) - pipeline <- new( - "TwasJointPipeline", - config = list( - dataType = weightsDataType, - crossValidationArgs = .cvResolve(CrossValidationParam()), - fitFullData = TRUE, - # What the accessor will actually do on this path. No - # residualization bundle reaches the TWAS joint dispatcher, so - # `residualizedGenotypes()` falls back to the dataset's own - # `scaleResiduals` slot -- which is therefore the honest answer - # here, and `NULL` says that explicitly rather than by omission. - standardized = .qtlScaleResiduals(data, NULL), - seed = seed, - ldSketch = NULL - ) - ) - .runJointSpecs( + .twasRunJointSpecGrid( parsedJointSpec, - data, - dataForm = "individual", - pipeline = pipeline, - jointMethods = intersect(methods, "mrmash"), - contexts = contexts, - traitIds = traitIds, - args = list( - methodArgs = list(), - cisWindow = cisWindow, - region = region, - verbose = verbose - ) + intersect(tokens, "mrmash"), + contexts, + traitIds, + marker, + ctx ) } - -# Top-level joint dispatcher for twasWeightsPipeline(QtlSumStats). +# Explicit joint specs (mr.mash RSS) on a QtlSumStats collection. `cfg` is +# the summary-statistics joint record, or a MultiStudy record with the +# collection as its data; `methodArgs` are the summary-statistics path's +# per-method options. # @noRd .twasDispatchJointSpecsQtlSumStats <- function( parsedJointSpec, - data, - methods, - contexts, - traitIds, - weightsDataType, - verbose, - fitRetention = c("slim", "none", "full"), - panelFilterParam = PanelFilterParam() + tokens, + methodArgs, + cfg ) { - fitRetention <- arg_match(fitRetention) - # Engine routing (jointEngine.R). pipeline <- new( "TwasJointPipeline", config = list( - dataType = weightsDataType, - crossValidationArgs = .cvResolve(CrossValidationParam()), - fitFullData = TRUE, + dataType = cfg$weightsDataType, + crossValidationParam = .cvResolve(CrossValidationParam()), + fitRetention = cfg$fitRetention, standardized = TRUE, - ldSketch = ldSketch(data) + verbose = cfg$verbose, + seed = cfg$seed, + ldReference = ldReference(cfg$data) ) ) .runJointSpecs( parsedJointSpec, - data, + cfg$data, dataForm = "sumstats", pipeline = pipeline, - jointMethods = intersect(methods, "mrmash"), - contexts = contexts, - traitIds = traitIds, + jointMethods = intersect(tokens, "mrmash"), + contexts = cfg$contexts, + traitIds = cfg$traitId, args = list( - methodArgs = list(), - verbose = verbose, - cutoffs = .panelCutoffs(panelFilterParam) + methodArgs = methodArgs, + verbose = cfg$verbose, + cutoffs = .panelCutoffs( + cfg$panelFilterParam %||% PanelFilterParam() + ) ) ) } - -# Top-level joint dispatcher for twasWeightsPipeline(MultiStudyQtlDataset). +# Joint dispatch for a MultiStudyQtlDataset. Non-study specs run on every +# individual-level component, each fitting under the setup its own QtlDataset +# run would use (.twasJointSetup() on the component, its genotype-filter +# overrides applied); every spec runs on the summary-statistics component. +# `cfg` is .twasJointCfg()'s record plus the collection, `genotypeFilterParam` +# and `panelFilterParam`; `methods` is the translated methods record. # @noRd -# --- .twasDispatchJointSpecsMultiStudy helpers ------------------------------ - -# Note that individual-level studies are excluded from cross-study TWAS fits. -.twasMultiStudyWarnExcluded <- function(studyAxisSpecs, qtlDatasets, verbose) { - if ( - length(studyAxisSpecs) > 0L && length(qtlDatasets) > 0L && verbose >= 1 - ) { - qdNames <- str_flatten(names(qtlDatasets), ", ") - msg <- glue( - "jointCrossStudy (twas): excluding individual-level ", - "studies ({qdNames}) from cross-study fits; sumstats studies ", - "participate." - ) - inform(msg) - } -} - -# Learn weights for the non-study-axis specs on each individual-level dataset. -.twasMultiStudyQtlLoop <- function( - nonStudyAxisSpecs, - qtlDatasets, +.twasDispatchJointSpecsMultiStudy <- function( + parsedJointSpec, + tokens, methods, - contexts, - traitIds, - cisWindow, - weightsDataType, - verbose, - xRegions, - fitRetention, - seed + cfg ) { - if (length(nonStudyAxisSpecs) == 0L) { - return(NULL) + qtlDatasets <- qtlDatasets(cfg$data) + specs <- .fmSplitStudyAxisSpecs(parsedJointSpec) + .twasMultiStudyWarnExcluded(specs$study, qtlDatasets, cfg$verbose) + out <- if (length(specs$nonStudy) == 0L) { + NULL + } else { + .msFoldStudyResults( + map( + qtlDatasets, + .twasStudyJointResult, + specs = specs$nonStudy, + tokens = tokens, + methods = methods, + cfg = cfg + ), + .rbindTwasWeights + ) } - .msFoldStudyResults( - map( - qtlDatasets, - .twasDispatchForStudy, - nonStudyAxisSpecs = nonStudyAxisSpecs, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - weightsDataType = weightsDataType, - verbose = verbose, - xRegions = xRegions, - fitRetention = fitRetention, - seed = seed - ), - .rbindTwasWeights + .twasMultiStudySumStats( + parsedJointSpec, + specs$study, + out, + tokens, + methods, + cfg ) } -# `map()` hands the dataset first; the dispatcher wants the specs first. +# One individual-level component's joint fits. # @noRd -.twasDispatchForStudy <- function( - qd, - nonStudyAxisSpecs, - methods, - contexts, - traitIds, - cisWindow, - weightsDataType, - verbose, - xRegions, - fitRetention, - seed -) { +.twasStudyJointResult <- function(qd, specs, tokens, methods, cfg) { + setup <- .twasJointSetup( + .qtlApplyFilterOverrides(qd, cfg$genotypeFilterParam), + .twasNormalizeMethods(methods), + cfg + ) .twasDispatchJointSpecsQtlDataset( - nonStudyAxisSpecs, - qd, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - weightsDataType = weightsDataType, - verbose = verbose, - xRegions = xRegions, - fitRetention = fitRetention, - seed = seed + specs, + tokens, + setup$marker, + setup$ctx, + cfg$contexts, + cfg$traitId ) } -# Learn weights for all specs on the sumstats collection; rbind onto `out`. +# Fit every spec on the summary-statistics component and rbind onto the +# individual-level results `out`. +# @noRd .twasMultiStudySumStats <- function( parsedJointSpec, - sumStats, studyAxisSpecs, out, + tokens, methods, - contexts, - traitIds, - weightsDataType, - verbose, - fitRetention + cfg ) { + sumStats <- sumStats(cfg$data) if (is.null(sumStats)) { - if (length(studyAxisSpecs) > 0L && verbose >= 1) { + if (length(studyAxisSpecs) > 0L && cfg$verbose >= 1) { msg <- glue( "jointCrossStudy (twas): no sumStats slot present on this ", "MultiStudyQtlDataset; cross-study specs produce no result." @@ -2585,70 +2042,38 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { } ssRes <- .twasDispatchJointSpecsQtlSumStats( parsedJointSpec, - sumStats, - methods = methods, - contexts = contexts, - traitIds = traitIds, - weightsDataType = weightsDataType, - verbose = verbose, - fitRetention = fitRetention + tokens, + .twasSumStatsMethodTokens(methods)$methodArgs, + list_assign(cfg, data = sumStats) ) if (is.null(ssRes)) { return(out) } - embeddedLd <- ldSketch(ssRes) if (is.null(out)) { - ssRes - } else { - .rbindTwasWeights(out, ssRes, ldSketch = embeddedLd) + return(ssRes) } + .rbindTwasWeights(out, ssRes, ldReference = ldReference(ssRes)) } -.twasDispatchJointSpecsMultiStudy <- function( - parsedJointSpec, - data, - methods, - contexts, - traitIds, - cisWindow, - weightsDataType, - verbose, - xRegions = list(NULL), - fitRetention = c("slim", "none", "full"), - seed = NULL -) { - fitRetention <- arg_match(fitRetention) - qtlDatasets <- qtlDatasets(data) - sumStats <- sumStats(data) - specs <- .fmSplitStudyAxisSpecs(parsedJointSpec) - .twasMultiStudyWarnExcluded(specs$study, qtlDatasets, verbose) - out <- .twasMultiStudyQtlLoop( - specs$nonStudy, - qtlDatasets, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - weightsDataType = weightsDataType, - verbose = verbose, - xRegions = xRegions, - fitRetention = fitRetention, - seed = seed - ) - .twasMultiStudySumStats( - parsedJointSpec, - sumStats, - specs$study, - out, - methods = methods, - contexts = contexts, - traitIds = traitIds, - weightsDataType = weightsDataType, - verbose = verbose, - fitRetention = fitRetention - ) + +# --- .twasDispatchJointSpecsMultiStudy helpers ------------------------------ + +# Note that individual-level studies are excluded from cross-study TWAS fits. +.twasMultiStudyWarnExcluded <- function(studyAxisSpecs, qtlDatasets, verbose) { + if ( + length(studyAxisSpecs) > 0L && length(qtlDatasets) > 0L && verbose >= 1 + ) { + qdNames <- str_flatten(names(qtlDatasets), ", ") + msg <- glue( + "jointCrossStudy (twas): excluding individual-level ", + "studies ({qdNames}) from cross-study fits; sumstats studies ", + "participate." + ) + inform(msg) + } } + # ---- map/apply helpers (lambda-free callbacks) --------------------------- # Parse the joint spec at position `i` (element + its index carry to the @@ -2720,77 +2145,3 @@ validateMethodsVsJointSpec <- function(methodsParsed, jointSpecParsed) { .jsHasStudyAxis <- function(s) { is_in("study", s$axes) } - -# One region's FM joint-spec dispatch; `args` bundles the shared call arguments. -# @noRd -.fmDispatchJointSpecRegion <- function( - rg, - parsedJointSpec, - data, - methods, - contexts, - traitIds, - cisWindow, - credibleSetParam, - verbose, - methodArgs, - mrmashPrior, - dataDrivenPriorWeightsCutoff, - crossValidationArgs, - residualizationArgs, - pipCutoffToSkip, - fineMappingResult, - fitRetention, - seed -) { - .fmDispatchJointSpecsQtlDatasetOneRegion( - parsedJointSpec = parsedJointSpec, - data = data, - methods = methods, - contexts = contexts, - traitIds = traitIds, - cisWindow = cisWindow, - credibleSetParam = credibleSetParam, - verbose = verbose, - methodArgs = methodArgs, - mrmashPrior = mrmashPrior, - dataDrivenPriorWeightsCutoff = dataDrivenPriorWeightsCutoff, - crossValidationArgs = crossValidationArgs, - residualizationArgs = residualizationArgs, - pipCutoffToSkip = pipCutoffToSkip, - fineMappingResult = fineMappingResult, - fitRetention = fitRetention, - seed = seed, - region = rg - ) -} - -# One region's TWAS joint-spec dispatch over a QtlDataset. -# @noRd -.twasDispatchJointSpecRegion <- function( - rg, - parsedJointSpec, - data, - methods, - contexts, - traitIds, - cisWindow, - weightsDataType, - verbose, - fitRetention, - seed = NULL -) { - .twasDispatchJointSpecsQtlDatasetOneRegion( - parsedJointSpec, - data, - methods, - contexts, - traitIds, - cisWindow, - weightsDataType, - verbose, - region = rg, - fitRetention = fitRetention, - seed = seed - ) -} diff --git a/R/ld.R b/R/ld.R index 7b2c57f10..6e90a446f 100644 --- a/R/ld.R +++ b/R/ld.R @@ -119,20 +119,8 @@ extractFilePaths <- function(genomicData, intersectionRows, columnToExtract) { ) } -#' Find LD blocks overlapping a query region from a metadata TSV file. -#' -#' @param ldReferenceMetaFile TSV with columns chrom, start, end, path. The path -#' column may be comma-separated: "ld_file,bim_file". -#' @param region "chr:start-end" string or data.frame with chrom/start/end. -#' @param completeCoverageRequired If TRUE, error when the region extends beyond -#' available LD blocks. -#' @return A list with: intersections (LD_file_paths, bimFilePaths), ldMetaData, -#' and parsed region. -#' @importFrom stringr str_split -#' @importFrom dplyr select -#' @importFrom vroom vroom -#' @noRd # Split the comma-joined path column into LD (+ optional bim) path columns. +# @noRd .regionalLdParsePaths <- function(genomicData) { parts <- str_split(genomicData$path, ",", simplify = TRUE) pathNames <- if (ncol(parts) == 2) { @@ -183,6 +171,19 @@ extractFilePaths <- function(genomicData, intersectionRows, columnToExtract) { ) } +#' Find LD blocks overlapping a query region from a metadata TSV file. +#' +#' @param ldReferenceMetaFile TSV with columns chrom, start, end, path. The path +#' column may be comma-separated: "ld_file,bim_file". +#' @param region "chr:start-end" string or data.frame with chrom/start/end. +#' @param completeCoverageRequired If TRUE, error when the region extends beyond +#' available LD blocks. +#' @return A list with: intersections (LD_file_paths, bimFilePaths), ldMetaData, +#' and parsed region. +#' @importFrom stringr str_split +#' @importFrom dplyr select +#' @importFrom vroom vroom +#' @noRd regionalLdMeta <- function( ldReferenceMetaFile, region, @@ -231,658 +232,11 @@ regionalLdMeta <- function( ) } -#' Read a pre-computed LD matrix (.cor.xz) and its bim file, returning a -#' symmetric matrix with variants ordered by position. -#' @importFrom dplyr mutate -#' @importFrom utils read.table -#' @importFrom stats setNames -#' @noRd -# Auto-detect the variant-metadata file (.bim / .pvar / .pvar.zst). -.processLdSnpFile <- function(ldFilePath, snpFilePath) { - if (!is.null(snpFilePath)) { - return(snpFilePath) - } - candidates <- str_c(ldFilePath, c(".bim", ".pvar", ".pvar.zst")) - found <- candidates[file.exists(candidates)] - if (length(found) == 0) { - msg <- glue( - "No variant file found for: {ldFilePath} ", - "(tried .bim, .pvar, .pvar.zst)" - ) - abort(msg) - } - found[1] -} - -# Read + normalise the LD variant metadata (canonical chrom / variant id / GD). -.processLdVariants <- function(snpFilePath) { - raw <- readVariantMetadata(snpFilePath) - isPvar <- !is_in("gpos", names(raw)) - ldVariants <- mutate( - raw, - chrom = canonChrom(.data$chrom), - variants = normalizeVariantId(.data$id) - ) - if (!isPvar) { - return(rename(ldVariants, GD = "gpos")) - } - # A .pvar carries no genetic distance, so GD and pos both take the - # position parsed back out of the variant id. - parsedPos <- map_int(ldVariants$variants, .ldVariantPos) - rename(ldVariants, GD = "pos") |> - mutate(GD = parsedPos, pos = parsedPos) -} - -processLdMatrix <- function(ldFilePath, snpFilePath = NULL) { - ldFileCon <- xzfile(ldFilePath) - ldValues <- scan(ldFileCon, quiet = TRUE) - close(ldFileCon) - snpFilePath <- .processLdSnpFile(ldFilePath, snpFilePath) - rawVariants <- .processLdVariants(snpFilePath) - # Label and symmetrize the matrix. - labelled <- `dimnames<-`( - matrix(ldValues, ncol = sqrt(length(ldValues)), byrow = TRUE), - list(rawVariants$variants, rawVariants$variants) - ) - # Only one triangle is stored on disk; mirror whichever one is empty. - lower <- lower.tri(labelled) - upper <- upper.tri(labelled) - ldMatrix <- if (all(labelled[lower] == 0)) { - replace(labelled, lower, t(labelled)[lower]) - } else { - replace(labelled, upper, t(labelled)[upper]) - } - # Order variants by genomic position. - posOrder <- order(map_int(rawVariants$variants, .ldVariantPos)) - ldVariants <- slice(rawVariants, posOrder) - list( - ldMatrix = ldMatrix[ldVariants$variants, ldVariants$variants], - ldVariants = ldVariants - ) -} - -#' Subset an LD matrix and variant info to a genomic region, optionally further -#' restricted to specific coordinates. -#' @importFrom dplyr mutate select inner_join -#' @noRd -extractLdForRegion <- function(ldMatrix, variants, region, extractCoordinates) { - inRegion <- filter( - variants, - .data$chrom == region$chrom & - .data$pos >= region$start & - .data$pos <= region$end - ) - extracted <- if (is.null(extractCoordinates)) { - inRegion - } else { - .ldRestrictToCoordinates(inRegion, extractCoordinates) - } - mat <- ldMatrix[extracted$variants, extracted$variants, drop = FALSE] - list(extractedLdMatrix = mat, extractedLdVariants = extracted) -} - -# The region's variants restricted to an explicit (chrom, pos) list, keeping -# only the variant-info columns the callers read back. -# @noRd -.ldRestrictToCoordinates <- function(inRegion, extractCoordinates) { - wanted <- extractCoordinates |> - mutate(chrom = canonChrom(.data$chrom)) |> - select("chrom", "pos") - joined <- inRegion |> - mutate(chrom = canonChrom(.data$chrom)) |> - inner_join(wanted, by = c("chrom", "pos")) - keepCols <- intersect( - c( - "chrom", - "variants", - "pos", - "GD", - "A1", - "A2", - "variance", - "allele_freq", - "n_nomiss" - ), - names(joined) - ) - select(joined, all_of(keepCols)) -} - -# Concatenate per-block variant-id lists into one deduplicated vector, dropping -# a repeated boundary variant shared between adjacent blocks. -# @noRd -.ldMergeVariants <- function(variantList) { - # Only the running tail decides whether a block's first id is a repeated - # boundary variant, so the merge is a fold. - reduce(variantList, .ldAppendBlockVariants, .init = character(0)) -} - -# @noRd -.ldAppendBlockVariants <- function(merged, v) { - ids <- if (is.list(v) && !is.null(v$variants)) v$variants else v - if (length(ids) == 0) { - return(merged) - } - repeatsBoundary <- length(merged) > 0 && tail(merged, 1) == ids[1] - c(merged, if (repeatsBoundary) ids[-1] else ids) -} - -#' Combine multiple block-level LD matrices into one, handling boundary -#' overlaps. -#' @importFrom utils tail -#' @noRd -createLdMatrix <- function(ldMatrices, variants) { - allVariants <- .ldMergeVariants(variants) - combined <- matrix( - 0, - nrow = length(allVariants), - ncol = length(allVariants), - dimnames = list(allVariants, allVariants) - ) - - # Deliberate preallocate-and-scatter: `combined` is variants x variants, - # so building it by folding full-size copies would multiply the memory - # this function needs by the number of blocks. - for (i in seq_along(ldMatrices)) { - v <- rownames(ldMatrices[[i]]) - idx <- match(v, allVariants) - combined[idx, idx] <- ldMatrices[[i]] - } - combined -} - -# Dispatch to the genotype- or pre-computed-block LD loader for the source. -.loadLdDispatch <- function( - source, - isGeno, - region, - extractCoordinates, - returnGenotype, - nSample -) { - if (isGeno) { - genoPath <- resolveGenotypePathForRegion(source$metaPath, region) - return(loadLdFromGenotype( - genoPath, - region, - returnGenotype = returnGenotype, - nSample = nSample - )) - } - if (returnGenotype) { - msg <- glue( - "returnGenotype=TRUE requires genotype files, not ", - "pre-computed LD matrices." - ) - abort(msg) - } - loadLdFromBlocks( - source$metaPath, - region, - extractCoordinates, - nSample = nSample - ) -} - -# Drop duplicate variant ids (boundary-overlap safety net). -.loadLdDedup <- function(result) { - variantIds <- variantIds(result) - if (is.null(variantIds)) { - return(result) - } - dupIdx <- which(duplicated(variantIds)) - if (length(dupIdx) == 0) { - return(result) - } - full <- ldMatrix(result) - corr <- if (is.null(full)) { - NULL - } else { - full[-dupIdx, -dupIdx, drop = FALSE] - } - LdData( - correlation = corr, - genotypeHandle = genotypeHandle(result), - snpIdx = snpIdx(result), - variants = variantInfo(result)[-dupIdx], - blockMetadata = blockMetadata(result), - nSamples = ldPanelNSamples(result) - ) -} - -#' Load and Process Linkage Disequilibrium (LD) Matrix -#' -#' Unified entry point for loading LD data from a metadata TSV file. -#' -#' The metadata TSV must have columns: chrom, start, end, path. Two formats: -#' \itemize{ -#' \item Pre-computed LD blocks: many rows per chromosome with block -#' boundaries -#' in start/end and path pointing to .cor.xz files (optionally -#' comma-separated -#' with a .bim path). -#' \item PLINK genotype files: one row per chromosome with start=0, end=0, and -#' path pointing to a per-chromosome PLINK prefix (.pgen/.pvar[.zst]/.psam -#' or -#' .bed/.bim/.fam). LD is computed on the fly via \code{computeLd()}. -#' } -#' -#' @param ldMetaFilePath Path to the LD metadata TSV file. -#' @param region Region of interest: "chr:start-end" string or data.frame with -#' chrom/start/end. -#' @param block Integer block index (or vector of them), for sources that -#' carry no coordinates: an \code{ldInfo} table (the cTWAS \code{LD_map} -#' shape -- an \code{LD_file} column and optionally \code{SNP_file}), a -#' correlation or genotype matrix, or a list of either. Supply exactly one -#' of \code{region} and \code{block}. -#' @param dropMonomorphic Logical. Drop variants with no variation, which -#' carry no LD and make a correlation undefined. Needs allele frequencies, -#' so it applies to genotype sources. -#' @param materializeGenotypes Logical. Read the dosages once and keep them on -#' the returned object, so later access does no file I/O. Costs memory and -#' saves repeated reads. -#' @param maxVariants Integer or \code{NULL}. Randomly thin any block larger -#' than this, to bound memory. -#' @param seed Integer or \code{NULL}. Seeds the \code{maxVariants} draw, -#' offset by the block index so blocks are independent yet reproducible, via -#' a scoped \code{withr::local_seed} that leaves the session RNG alone. -#' @param extractCoordinates Optional data.frame with columns "chrom" and "pos" -#' for specific coordinates extraction (only for pre-computed LD blocks). -#' @param returnGenotype Controls what ldMatrix contains in the return value. -#' FALSE (default): always return correlation matrix R. TRUE: return genotype -#' matrix X (only valid for PLINK sources). "auto": return X for PLINK -#' sources, R for pre-computed sources. -#' @param nSample Optional sample size for computing variance (= -#' 2*p*(1-p)*n/(n-1)). If NULL, ref_panel will not include variance or -#' n_nomiss columns. Only used for PLINK genotype sources. -#' -#' @return A list with: -#' \describe{ -#' \item{ldVariants}{Character vector of variant IDs (canonical format).} -#' \item{ldMatrix}{LD correlation matrix R (or genotype matrix X when -#' returnGenotype is TRUE or "auto" with PLINK source).} -#' \item{ref_panel}{Data.frame with variant metadata (chrom, pos, A2, A1, -#' variant_id, -#' and optionally allele_freq, variance, n_nomiss).} -#' \item{is_genotype}{Logical: TRUE if ldMatrix contains genotype X, FALSE if -#' correlation R.} -#' \item{blockMetadata}{Data.frame with region/block info. For pre-computed -#' LD: one row per block. -#' For PLINK: a single row spanning the loaded region.} -#' } -#' @examples -#' meta <- system.file("extdata", "ld_reference", "ld_meta_file.tsv", -#' package = "pecotmr") -#' loadLdMatrix(ldMetaFilePath = meta, region = "chr22:16000000-18000000") -#' # Several regions at once return one LdData per block. -#' length(loadLdMatrix(meta, region = rep("chr22:16000000-18000000", 2))) -#' @export -loadLdMatrix <- function( - ldMetaFilePath, - region = NULL, - block = NULL, - extractCoordinates = NULL, - returnGenotype = FALSE, - dropMonomorphic = FALSE, - materializeGenotypes = FALSE, - maxVariants = NULL, - seed = NULL, - nSample = NULL -) { - .ldLoadValidateAddress(ldMetaFilePath, region, block) - keys <- if (is.null(region)) block else region - # Only an atomic vector addresses several blocks. `region` also accepts a - # single data.frame / GRanges spec, whose length is its column count -- - # treating that as many keys would map over the columns. - if (is.atomic(keys) && length(keys) > 1L) { - return(map( - keys, - .loadLdOne, - source = ldMetaFilePath, - byRegion = is.null(block), - extractCoordinates = extractCoordinates, - returnGenotype = returnGenotype, - dropMonomorphic = dropMonomorphic, - materializeGenotypes = materializeGenotypes, - maxVariants = maxVariants, - seed = seed, - nSample = nSample - )) - } - .loadLdOne( - keys, - ldMetaFilePath, - byRegion = is.null(block), - extractCoordinates = extractCoordinates, - returnGenotype = returnGenotype, - dropMonomorphic = dropMonomorphic, - materializeGenotypes = materializeGenotypes, - maxVariants = maxVariants, - seed = seed, - nSample = nSample - ) -} - -# A source is addressed either by genomic region (an LD meta file, which -# carries coordinates) or by block index (an `ldInfo` table or in-memory -# matrices, which do not). Exactly one addressing mode applies. -# @noRd -.ldLoadValidateAddress <- function(source, region, block) { - if (is.null(region) && is.null(block)) { - abort("loadLdMatrix: supply either `region` or `block`.") - } - if (!is.null(region) && !is.null(block)) { - abort("loadLdMatrix: supply `region` or `block`, not both.") - } - if (!is.null(region) && !is.character(source)) { - msg <- glue( - "loadLdMatrix: `region` addresses an LD meta file, but ", - "`ldMetaFilePath` is a {class(source)[[1L]]}. Coordinate-free ", - "sources (an `ldInfo` table, a matrix, or a list of matrices) ", - "are addressed with `block`." - ) - abort(msg) - } - invisible(TRUE) -} - -# One block, whatever the source. Everything that is not an LD meta file is -# coordinate-free and indexed by `block`. -# @noRd -.loadLdOne <- function( - key, - source, - byRegion, - extractCoordinates, - returnGenotype, - dropMonomorphic, - materializeGenotypes, - maxVariants, - seed, - nSample -) { - loaded <- if (byRegion) { - .loadLdFromMeta( - source, - key, - extractCoordinates, - returnGenotype, - nSample - ) - } else { - .loadLdFromIndexed(source, key) - } - loaded |> - .loadLdDedup() |> - .ldApplyMonomorphic(dropMonomorphic) |> - .ldApplySubsample(maxVariants, seed, key) |> - .ldApplyMaterialize(materializeGenotypes) -} - -# Coordinate-free sources, addressed by block index: an `ldInfo` table (the -# cTWAS LD_map shape -- LD_file plus optional SNP_file, no coordinates), a -# single in-memory matrix, or a list of them. Every source returns an LdData, -# so callers never have to branch on what they loaded from. -# @noRd -.loadLdFromIndexed <- function(source, block) { - if (is.data.frame(source)) { - return(.ldInfoBlock(source, block)) - } - mat <- if (is.list(source)) source[[block]] else source - if (!is.matrix(mat)) { - msg <- glue( - "loadLdMatrix: cannot address a {class(source)[[1L]]} by block. ", - "Supply an LD meta file path, an `ldInfo` data.frame, a matrix, ", - "or a list of matrices." - ) - abort(msg) - } - .ldDataFromMatrix(mat, isGenotype = nrow(mat) > ncol(mat)) -} - -# One row of an `ldInfo` table. Genotype paths are read and correlated; -# pre-computed .cor.xz blocks are read directly. -# @noRd -.ldInfoBlock <- function(ldInfo, block) { - if (!is_in("LD_file", colnames(ldInfo))) { - abort("loadLdMatrix: an `ldInfo` table needs an `LD_file` column.") - } - ldPath <- as.character(ldInfo$LD_file)[block] - if (isGenotypeSource(ldPath)) { - geno <- loadGenotypeRegion(ldPath) - return(.ldDataFromMatrix(geno, isGenotype = TRUE)) - } - snpFile <- if (is_in("SNP_file", colnames(ldInfo))) { - as.character(ldInfo$SNP_file)[block] - } else { - NULL # processLdMatrix auto-detects the .bim / .pvar companion - } - .ldDataFromProcessed(processLdMatrix(ldPath, snpFile)) -} - -# Wrap processLdMatrix()'s (matrix, variants) pair as an LdData so BOTH -# `ldInfo` sources return one type -- the contract .loadLdFromIndexed states -# and the post-load chain (dedup / monomorphic / subsample) relies on. -# -# Not .ldDataFromMatrix(): that one is for a bare matrix with no metadata and -# substitutes placeholder chrNA:1..n coordinates. A precomputed LD block comes -# with its .bim/.pvar, so the real chrom/pos/alleles are carried through. -# @noRd -.ldDataFromProcessed <- function(proc) { - v <- proc$ldVariants - n <- nrow(v) - gr <- .refPanelToGranges(data.frame( - chrom = as.character(v$chrom), - pos = as.integer(v$pos), - variant_id = as.character(v$variants), - A1 = as.character(v$A1), - A2 = as.character(v$A2), - stringsAsFactors = FALSE - )) - LdData( - correlation = proc$ldMatrix, - variants = gr, - blockMetadata = tibble( - blockId = 1L, - size = n, - startIdx = 1L, - endIdx = n - ), - nSamples = 0L - ) -} - -# Wrap a bare matrix as an LdData so every source returns one type. Variant -# identity comes from the dimnames when present. -# @noRd -.ldDataFromMatrix <- function(mat, isGenotype) { - n <- if (isGenotype) ncol(mat) else nrow(mat) - named <- if (isGenotype) colnames(mat) else rownames(mat) - ids <- named %||% str_c("v", seq_len(n)) - gr <- S4Vectors::`mcols<-`( - GRanges( - seqnames = rep("chrNA", n), - ranges = IRanges::IRanges(start = seq_len(n), width = 1L) - ), - value = S4Vectors::DataFrame(variant_id = ids) - ) - LdData( - correlation = if (isGenotype) NULL else mat, - genotypeHandle = if (isGenotype) mat else NULL, - variants = gr, - blockMetadata = tibble(blockId = 1L, size = n), - nSamples = if (isGenotype) nrow(mat) else 0L - ) -} - -# Drop variants with no variation. They carry no LD, and a zero-variance -# column makes a correlation undefined. -# @noRd -.ldApplyMonomorphic <- function(ld, dropMonomorphic) { - if (!isTRUE(dropMonomorphic)) { - return(ld) - } - refPanel <- refPanel(ld) - if (is.null(refPanel) || !is_in("allele_freq", colnames(refPanel))) { - return(ld) - } - p <- refPanel$allele_freq - keep <- !is.na(p) & p > 0 & p < 1 - if (all(keep)) { - return(ld) - } - .ldSubsetData(ld, which(keep)) -} - -# Randomly thin an oversized block, seeded per block so a run is reproducible -# without disturbing the session RNG. -# @noRd -.ldApplySubsample <- function(ld, maxVariants, seed, key) { - if (is.null(maxVariants) || length(ld) <= maxVariants) { - return(ld) - } - if (!is.null(seed)) { - offset <- if (is.numeric(key)) as.integer(key) else 0L - withr::local_seed(as.integer(seed) + offset) - } - .ldSubsetData(ld, sort(sample(length(ld), maxVariants))) -} - -# Read the dosages once and keep them on the object, so later access does no -# file I/O. -# @noRd -.ldApplyMaterialize <- function(ld, materializeGenotypes) { - if (!isTRUE(materializeGenotypes) || !hasGenotypes(ld)) { - return(ld) - } - X <- genotypes(ld) - if (!is.matrix(X)) { - return(ld) - } - LdData( - correlation = NULL, - genotypeHandle = X, - snpIdx = NULL, - variants = variantInfo(ld), - blockMetadata = blockMetadata(ld), - nSamples = ldPanelNSamples(ld) - ) -} - -# Narrow an LdData to a subset of its variants, keeping correlation and -# genotypes consistent with the ranges. -# @noRd -.ldSubsetData <- function(ld, idx) { - full <- ld@correlation - R <- if (!is.null(full) && is.matrix(full)) { - full[idx, idx, drop = FALSE] - } else { - full - } - raw <- ld@genotypeHandle - gh <- if (is.matrix(raw)) raw[, idx, drop = FALSE] else raw - snpIdx <- if (is.matrix(raw) || is.null(ld@snpIdx)) { - ld@snpIdx - } else { - ld@snpIdx[idx] - } - LdData( - correlation = R, - genotypeHandle = gh, - snpIdx = if (is.matrix(gh)) NULL else snpIdx, - variants = variantInfo(ld)[idx], - blockMetadata = blockMetadata(ld), - nSamples = ldPanelNSamples(ld) - ) -} - -# @noRd -.loadLdFromMeta <- function( - ldMetaFilePath, - region, - extractCoordinates, - returnGenotype, - nSample -) { - source <- resolveLdSource(ldMetaFilePath) - isGeno <- is_in(source$type, c("plink2", "plink1", "vcf", "gds")) - # "auto": return X for genotype sources, R for pre-computed. - if (identical(returnGenotype, "auto")) { - returnGenotype <- isGeno - } - .loadLdDispatch( - source, - isGeno, - region, - extractCoordinates, - returnGenotype, - nSample - ) -} - # ---------- Internal: resolve LD source type ---------- -#' @noRd -hasPlink2Files <- function(prefix) { - file.exists(str_c(prefix, ".pgen")) && - (file.exists(str_c(prefix, ".pvar")) || - file.exists(str_c(prefix, ".pvar.zst"))) && - file.exists(str_c(prefix, ".psam")) -} - -#' @noRd -hasPlink1Files <- function(prefix) { - file.exists(str_c(prefix, ".bed")) && - file.exists(str_c(prefix, ".bim")) && - file.exists(str_c(prefix, ".fam")) -} - -#' @noRd -isVcfPath <- function(path) { - str_detect(path, "\\.(vcf|vcf\\.gz|bcf)$") && file.exists(path) -} - -#' @noRd -isGdsPath <- function(path) { - str_detect(path, "\\.gds$") && file.exists(path) -} - -#' Check whether a path points to a genotype source (PLINK, VCF, or GDS). -#' @noRd -isGenotypeSource <- function(path) { - hasPlink2Files(path) || - hasPlink1Files(path) || - isVcfPath(path) || - isGdsPath(path) -} - -#' Resolve an LD source metadata TSV to its actual data type. -#' -#' The metadata TSV has columns: chrom, start, end, path. Three categories are -#' supported: -#' \itemize{ -#' \item Pre-computed LD blocks (.cor.xz): many rows per chromosome, each with -#' specific start/end block boundaries and path pointing to .cor.xz files. -#' \item Genotype files (PLINK2, PLINK1, VCF, or GDS): one row per chromosome -#' with start=0, end=0, and path pointing to a per-chromosome genotype file -#' or prefix. The actual region filter is applied by the genotype loader. -#' } -#' -#' This function peeks at the first row to determine the data type. The actual -#' per-chromosome path is resolved later by -#' \code{resolveGenotypePathForRegion()} at load time. -#' -#' @param path Path to a metadata TSV file with columns chrom, start, end, path. -#' @return A list with: -#' \item{type}{"plink2", "plink1", "vcf", "gds", or "precomputed"} -#' \item{dataPath}{Genotype path from first row (for type detection only; -#' actual -#' per-chromosome path is resolved at load time)} -#' \item{metaPath}{The metadata TSV path (always set)} -#' @importFrom vroom vroom -#' @noRd # Read + validate the first row of an LD metadata TSV (>=4 columns). #' @importFrom checkmate checkFileExists +# @noRd .resolveLdReadMeta <- function(path) { res <- checkFileExists(path, access = "r") if (!isTRUE(res)) { @@ -907,24 +261,53 @@ isGenotypeSource <- function(path) { ) } -# Genotype source descriptor for the resolved path, or NULL if pre-computed. +# Genotype source descriptor for the resolved path, or NULL when it is not a +# genotype file or PLINK prefix, its format told as readGenotypes() tells it. +# A PLINK source is described by its stem, which its readers take. .resolveLdGenotypeType <- function(resolved, path) { - if (hasPlink2Files(resolved)) { - return(list(type = "plink2", dataPath = resolved, metaPath = path)) - } - if (hasPlink1Files(resolved)) { - return(list(type = "plink1", dataPath = resolved, metaPath = path)) - } - if (isVcfPath(resolved)) { - return(list(type = "vcf", dataPath = resolved, metaPath = path)) + format <- .genotypePathFormat(resolved) + if (is.null(format)) { + return(NULL) } - if (isGdsPath(resolved)) { - return(list(type = "gds", dataPath = resolved, metaPath = path)) + dataPath <- if (str_starts(format, "plink")) { + .genotypeStem(resolved) + } else { + resolved } - NULL + list(type = format, dataPath = dataPath, metaPath = path) } +#' Resolve an LD source metadata TSV to its genotype file type. +#' +#' The metadata TSV has columns: chrom, start, end, path, with one row per +#' chromosome (start=0, end=0) whose path is a per-chromosome genotype file or +#' prefix (PLINK2, PLINK1, VCF, GDS or BGEN). The actual region filter is +#' applied by the genotype loader. pecotmr does not read pre-computed LD from +#' disk, so a row pointing at anything else is an error. +#' +#' This function peeks at the first row to determine the data type. The actual +#' per-chromosome path is resolved later by +#' \code{resolveGenotypePathForRegion()} at load time. +#' +#' A path that is itself a genotype file or PLINK prefix is that source, with +#' no metadata. +#' +#' @param path Path to a metadata TSV file with columns chrom, start, end, +#' path, or to a genotype file or PLINK prefix. +#' @return A list with: +#' \item{type}{"plink2", "plink1", "vcf", "gds" or "bgen"} +#' \item{dataPath}{Genotype path from first row (for type detection only; +#' actual +#' per-chromosome path is resolved at load time)} +#' \item{metaPath}{The metadata TSV path, or NULL for a genotype source +#' given directly} +#' @importFrom vroom vroom +#' @noRd resolveLdSource <- function(path) { + direct <- .resolveLdGenotypeType(path, NULL) + if (!is.null(direct)) { + return(direct) + } meta <- .resolveLdReadMeta(path) # Strip the comma-separated bim path, then resolve relative to the meta dir. rawPath <- str_remove(meta$path[1], ",.*$") @@ -933,22 +316,17 @@ resolveLdSource <- function(path) { if (!is.null(genoType)) { return(genoType) } - if ( - !is.na(meta$start) && - !is.na(meta$end) && - meta$start == 0 && - meta$end == 0 - ) { - msg <- glue( - "Metadata has start=0, end=0 but path does not resolve to ", - "genotype files: {resolved}", - "\n The 0:0 sentinel is only valid for whole-chromosome ", - "genotype files.", - .trim = FALSE + abort(c( + glue( + "The LD metadata's path does not resolve to genotype files: ", + "{resolved}" + ), + i = str_c( + "pecotmr reads LD only from genotypes (PLINK2, PLINK1, VCF, GDS, ", + "BGEN); ", + "wrap a pre-computed LD matrix in an LdMatrix instead." ) - abort(msg) - } - list(type = "precomputed", metaPath = path) + )) } #' Resolve the correct genotype path for a given region from a metadata TSV. @@ -975,220 +353,181 @@ resolveGenotypePathForRegion <- function(metaPath, region) { file.path(dirname(metaPath), rawPath) } -# ---------- Internal: load LD from genotype files ---------- - -#' Load genotype data and compute LD or return genotype matrix. -#' @noRd -# --- loadLdFromGenotype helpers --------------------------------------------- - -# Reference panel from variant ids + allele frequency (.afreq or dosage-derived) -# + variance when a sample size is supplied. -.loadLdGtRefPanel <- function( - X, - variantInfo, - variantIds, - genotypePath, - nSample -) { - afreq <- readAfreq(genotypePath) - alleleFreq <- if (is.null(afreq)) { - colMeans(X, na.rm = TRUE) / 2 - } else { - freqMatch <- match(variantInfo$id, afreq$id) - nUnmatched <- sum(is.na(freqMatch)) - if (nUnmatched > 0) { - nFreq <- length(freqMatch) - msg <- glue( - "{nUnmatched} out of {nFreq} variants have no allele ", - "frequency in .afreq file." - ) - warn(msg) - } - afreq$alt_freq[freqMatch] +# ---------- LD reference: what it is and how it is held ---------- +# +# An LD reference is one of these, and every helper below answers for each: +# +# a genotype panel a RangedSummarizedExperiment over a GenotypeHandle, +# read lazily from disk -- the same shape as QtlDataset's +# genotype experiment. A bare GenotypeHandle, which objects +# serialised before the slot became an RSE still carry, is +# treated as one. +# a GenotypeMixture several genotype panels whose LD is averaged, still +# lazy: each panel is read only for the variants asked +# about, and answers for its own samples. +# an LdMatrix a correlation matrix the caller built, held in memory. + +# Normalise an LD-reference input to the shape the slot holds: a panel or an +# LdMatrix as given, a bare GenotypeHandle wrapped into a panel -- so a sketch +# and QtlDataset's genotype experiment end up being the same object with +# different provenance rather than two spellings of one idea. +# @noRd +.asLdRef <- function(x) { + if (is.null(x) || .ldIsMatrixRef(x) || .ldIsMixtureRef(x)) { + return(x) } - mutate( - parseVariantId(variantIds), - variant_id = variantIds, - allele_freq = alleleFreq, - !!!.ldPanelSampleCols(alleleFreq, nSample) - ) + if (methods::is(x, "RangedSummarizedExperiment")) { + return(x) + } + if (methods::is(x, "GenotypeHandle")) { + return(.genotypeExperiment(x)) + } + abort(glue( + "`ldReference` must be a genotype panel (see readGenotypes()), a ", + "GenotypeMixture or an LdMatrix (got {class(x)[[1L]]})." + )) } -# The panel's per-variant variance / non-missing count, which are only -# derivable once the caller declares the sample size behind the frequencies. +# TRUE for an LD reference held as a matrix rather than as genotypes. # @noRd -.ldPanelSampleCols <- function(p, nSample) { - if (is.null(nSample)) { - return(list()) - } - list( - variance = 2 * p * (1 - p) * nSample / (nSample - 1), - n_nomiss = nSample - ) +.ldIsMatrixRef <- function(x) { + methods::is(x, "LdMatrix") } -# Single-block metadata spanning the loaded region. -.loadLdGtBlockMeta <- function(variantInfo, variantIds) { - positions <- variantInfo$pos - tibble( - blockId = 1L, - chrom = as.character(variantInfo$chrom[1]), - blockStart = min(positions), - blockEnd = max(positions), - size = length(variantIds), - startIdx = 1L, - endIdx = length(variantIds) - ) +# TRUE for a mixture of genotype panels. +# @noRd +.ldIsMixtureRef <- function(x) { + methods::is(x, "GenotypeMixture") } -# Lazy-genotype LdData result (handle + region snp index, no correlation). -.loadLdGtGenotypeResult <- function( - genotypePath, - region, - variantsGr, - blockMetadata, - X -) { - handle <- .readGenotypeHandle(genotypePath) - snpIdx <- .regionToSnpIdx(snpInfo(handle), region) - LdData( - correlation = NULL, - genotypeHandle = handle, - snpIdx = snpIdx, - variants = variantsGr, - blockMetadata = blockMetadata, - nSamples = as.integer(nrow(X)) - ) +# TRUE for an LD reference backed by genotypes: a panel or a mixture of them. +# @noRd +.ldIsGenotypeRef <- function(x) { + .ldIsPanel(x) || .ldIsMixtureRef(x) } -loadLdFromGenotype <- function( - genotypePath, - region, - returnGenotype = FALSE, - nSample = NULL -) { - result <- loadGenotypeRegion( - genotypePath, - region = region, - returnVariantInfo = TRUE - ) - variantInfo <- result$variant_info - variantIds <- normalizeVariantId(formatVariantId( - variantInfo$chrom, - variantInfo$pos, - variantInfo$A2, - variantInfo$A1 - )) - X <- `colnames<-`(result$X, variantIds) - refPanel <- .loadLdGtRefPanel( - X, - variantInfo, - variantIds, - genotypePath, - nSample - ) - blockMetadata <- .loadLdGtBlockMeta(variantInfo, variantIds) - variantsGr <- .refPanelToGranges(refPanel) - if (returnGenotype) { - return(.loadLdGtGenotypeResult( - genotypePath, - region, - variantsGr, - blockMetadata, - X - )) - } - R <- computeLd(X, method = "sample") - LdData( - correlation = R, - genotypeHandle = NULL, - snpIdx = NULL, - variants = variantsGr, - blockMetadata = blockMetadata, - nSamples = as.integer(nrow(X)) - ) +# TRUE for anything that can serve as an LD reference. +# @noRd +.ldIsReference <- function(x) { + .ldIsGenotypeRef(x) || .ldIsMatrixRef(x) } -# ---------- LD sketch: per-variant LD matrix ---------- - -# Internal: build a sample-correlation LD matrix for a specified variant -# subset of an `ldSketch` `GenotypeHandle`. Called directly by -# twasWeightsPipeline, fineMappingPipeline, jointEngine, -# causalInferencePipeline, colocboostPipeline and the coloc / summaryStatsQc -# paths, which differ only in their error message prefix and in whether -# variants absent from the panel raise an error or get silently dropped -- -# both of which are arguments here rather than a per-pipeline wrapper. -# -# Arguments: -# ldSketch A GenotypeHandle. -# variantIds Character vector of SNP IDs to extract. -# label Error-message prefix, e.g. "twasWeightsPipeline". -# onMissing "error" (default) -> any unmatched id stops the call; -# "drop" -> unmatched ids are silently filtered. -# When "drop" leaves no surviving variants the function -# returns NULL. -# -# Returns: -# A `length(variantIds) x length(variantIds)` symmetric LD matrix with -# rows/cols named by the (possibly filtered) `variantIds`. -# With `onMissing = "drop"` the returned matrix carries an attribute -# `"keptVariantIds"` so callers can recover which ids survived. -# Require a non-NULL GenotypeHandle ldSketch. -# Normalise an LD-sketch input to the RangedSummarizedExperiment the slot -# holds. Accepts a bare GenotypeHandle, which is what every caller has passed -# until now, and wraps it -- so a sketch and QtlDataset's genotype experiment -# end up being the same object with different provenance rather than two -# spellings of one idea. -# @noRd -.asLdSketch <- function(x) { - if (is.null(x)) { +# The GenotypeHandle a panel reads through, or NULL when it reads through +# none: an LdMatrix, a mixture, or a panel whose dosages come from another +# backend (a VCF, GDS or PLINK1 seed, or a matrix held in memory). The handle +# is the DelayedArray seed of the dosage assay, so it stays reachable after +# the panel is subset. Tolerates a bare handle, which is what objects +# serialised before the slot became an RSE still carry. +# @noRd +.ldRefHandle <- function(x) { + if (is.null(x) || .ldIsMatrixRef(x) || .ldIsMixtureRef(x)) { return(NULL) } - if (methods::is(x, "RangedSummarizedExperiment")) { + if (methods::is(x, "GenotypeHandle")) { return(x) } - if (methods::is(x, "GenotypeHandle")) { - return(.genotypeExperiment(x)) + seed <- .panelSeed(x) + if (!methods::is(seed, "GhSeed")) { + return(NULL) } - abort(glue( - "`ldSketch` must be a genotype panel -- a ", - "RangedSummarizedExperiment or a GenotypeHandle (got ", - "{class(x)[[1L]]})." - )) + .ghSeedHandle(seed) } -# The GenotypeHandle a sketch reads through. The handle is the DelayedArray -# seed of the dosage assay, so it stays reachable after the sketch is subset. -# Tolerates a bare handle, which is what objects serialised before the slot -# became an RSE still carry. +# The DelayedArray seed under a panel's dosage assay -- a GhSeed, or a +# GenotypeDosageSeed for a VCF, GDS or PLINK1 panel -- or NULL for an assay +# held in memory or combined from several seeds (a per-chromosome set of +# those). # @noRd -.ldSketchHandle <- function(x) { - if (is.null(x)) { +#' @importFrom rlang try_fetch +.panelSeed <- function(panel) { + dosage <- SummarizedExperiment::assay( + panel, + "dosage", + withDimnames = FALSE + ) + if (!methods::is(dosage, "DelayedArray")) { return(NULL) } - if (methods::is(x, "GenotypeHandle")) { - return(x) - } - .ghSeedHandle( - DelayedArray::seed(SummarizedExperiment::assay(x, "dosage")) + try_fetch(DelayedArray::seed(dosage), error = function(cnd) NULL) +} + +# ---------- LD reference: where its genotypes come from ---------- +# +# A panel records when it is built where its genotypes come from -- +# list(format, path, chromPaths) -- as metadata(panel)$genotypeSource. What +# needs the file reads it there: a sketch's .afreq sidecar, a label, ctwas's +# check that the payload exists, a per-chromosome manifest's shards. So a +# panel need not read through a GenotypeHandle: a VCF, GDS or PLINK1 panel +# reads through its own backend, and any RSE with a dosage assay can serve. + +# The record of a handle's genotype source. +# @noRd +.genotypeSourceRecord <- function(handle) { + list( + format = genotypeFormat(handle), + path = path(handle), + chromPaths = .genotypeChromPaths(handle) ) } -# Narrow a sketch to a variant subset. On an RSE this is ordinary subsetting, -# which brings the DelayedArray's index bookkeeping with it; a legacy bare -# handle falls back to the handle-level trim, which needs fileIdx to stay -# read-safe. +# An LD reference's genotype source: the record its panel carries -- or, for +# a panel built before panels carried one and for a bare handle, its +# handle's -- or NULL for a panel held in memory, an LdMatrix or a mixture. +# @noRd +.ldRefSource <- function(x) { + if (is.null(x) || .ldIsMatrixRef(x) || .ldIsMixtureRef(x)) { + return(NULL) + } + if (methods::is(x, "RangedSummarizedExperiment")) { + recorded <- S4Vectors::metadata(x)$genotypeSource + if (!is.null(recorded)) { + return(recorded) + } + } + handle <- .ldRefHandle(x) + if (is.null(handle)) NULL else .genotypeSourceRecord(handle) +} + +# @noRd +.ldRefFormat <- function(x) { + .ldRefSource(x)$format +} + +# The file an LD reference reads, resolved to a real path (a bundled +# example's portable reference resolves under the installed package), or +# NULL when it reads none. +# @noRd +.ldRefReadPath <- function(x) { + p <- .ldRefSource(x)$path + if (is.null(p)) NULL else .resolveGenotypeResourcePath(p) +} + +# The per-chromosome files of a panel read from a manifest, named by +# chromosome; empty for a panel read from one file. +# @noRd +.ldRefChromPaths <- function(x) { + .ldRefSource(x)$chromPaths %||% character(0) +} + +# Narrow an LD reference to a variant subset. A panel is cut so that, where it +# can, its seed sheds the dropped variants too (.panelSubsetRows()); an +# LdMatrix subsets its matrix along with its variants; a legacy bare handle +# falls back to the handle-level trim, which needs fileIdx to stay read-safe. # @noRd -.ldSketchSubset <- function(x, keep) { +.ldRefSubset <- function(x, keep) { if (is.null(x)) { return(NULL) } + if (.ldIsMatrixRef(x)) { + return(x[keep]) + } + if (.ldIsMixtureRef(x)) { + return(.gmRowsAt(x, keep)) + } if (methods::is(x, "GenotypeHandle")) { return(.subsetGenotypeHandle(x, keep)) } - # `drop = FALSE`: the fallback shape is the matrix sketch, which a - # one-variant `keep` would otherwise collapse to a vector. - x[keep, , drop = FALSE] + .panelSubsetRows(x, keep) } # A sketch trimmed to zero variants, every other property preserved. Used when @@ -1197,26 +536,135 @@ loadLdFromGenotype <- function( # which on a real panel is ~135 MB of serialized dead weight per such entry. # NULL-safe and idempotent. # @noRd -.emptySketch <- function(x) { +.emptyLdRef <- function(x) { if (is.null(x)) { return(NULL) } + if (.ldIsMatrixRef(x)) { + return(x[integer(0)]) + } + if (.ldIsMixtureRef(x)) { + return(.gmRowsAt(x, integer(0))) + } if (methods::is(x, "GenotypeHandle")) { return(.emptyGenotypeHandle(x)) } - # A panel holds its handle TWICE -- as rowRanges and inside the dosage - # assay's DelayedArray seed. `x[integer(0), ]` drops the rows but leaves - # the seed carrying the whole panel's snpInfo, which is precisely the - # weight this exists to shed. Rebuild from an emptied handle so both go. - .genotypeExperiment(.emptyGenotypeHandle(.ldSketchHandle(x))) + .emptyPanel(x, keepSamples = FALSE) +} + +# A panel cut to the variants `keep` marks (a mask or positions). A panel +# whose rows are exactly its seed's, cut to an ordered subset, has the seed +# cut too -- a GenotypeHandle trimmed, a VCF, GDS or PLINK1 seed cut to the +# records the kept rows read -- so the cut panel's seed does not carry the +# dropped variants' metadata (see .emptyPanel()); its rowRanges, samples, +# colData and recorded metadata are its own, cut alike, so no sidecar is read +# again. Any other cut -- of a panel that is itself a row subset, of one held +# in memory or whose handle predates fileIdx, or one that reorders or repeats +# rows -- is taken from its assay, which then reads only those rows. +# @noRd +.panelSubsetRows <- function(panel, keep) { + keepIdx <- if (is.logical(keep)) which(keep) else as.integer(keep) + dosage <- .panelCutDosage(panel, keepIdx) + if (is.null(dosage)) { + # `drop = FALSE`: a one-variant `keep` would otherwise collapse the + # assay to a vector. + return(panel[keep, , drop = FALSE]) + } + SummarizedExperiment::SummarizedExperiment( + assays = list(dosage = dosage[, colnames(panel), drop = FALSE]), + rowRanges = SummarizedExperiment::rowRanges(panel)[keepIdx], + colData = SummarizedExperiment::colData(panel), + metadata = S4Vectors::metadata(panel) + ) +} + +# The dosage assay of a panel cut to rows `keepIdx`, over a seed cut alike, +# or NULL when the seed cannot be cut so (see .panelSubsetRows()). +# @noRd +.panelCutDosage <- function(panel, keepIdx) { + if ( + length(keepIdx) >= nrow(panel) || + is.unsorted(keepIdx, strictly = TRUE) + ) { + return(NULL) + } + seed <- .panelSeed(panel) + if ( + methods::is(seed, "GenotypeDosageSeed") && + identical(rownames(panel), dimnames(seed)[[1L]]) + ) { + return(DelayedArray::DelayedArray(.gdSeedRows(seed, keepIdx))) + } + handle <- .ldRefHandle(panel) + if (is.null(handle) || !.panelRowsAreHandle(panel, handle)) { + return(NULL) + } + genotypeDelayedArray(.subsetGenotypeHandle(handle, keepIdx)) +} + +# A panel's dimensions before any subsetting: its dosage seed's, or its own +# when it holds its dosages in memory. +# @noRd +.panelFullDims <- function(panel) { + seed <- .panelSeed(panel) + if (is.null(seed)) dim(panel) else dim(seed) +} + +# TRUE when a panel's rows are its handle's own, in order, and the handle can +# be trimmed (it records each variant's file position). +# @noRd +.panelRowsAreHandle <- function(panel, handle) { + si <- snpInfo(handle) + is_in("fileIdx", names(si)) && + identical(rownames(panel), normalizeVariantId(as.character(si$SNP))) +} + +# A panel with no variants, held in memory. `x[integer(0), ]` would drop the +# rows but keep the dosage seed -- a handle carrying every variant's +# metadata, or a backend over the whole file -- which is precisely the +# weight an empty panel exists to shed, so no seed is kept. Where it came +# from stays recorded (.ldRefSource()). `keepSamples` keeps its samples and +# their colData, as a mixture's sampleMap needs; otherwise they go too. +# @noRd +.emptyPanel <- function(panel, keepSamples) { + cols <- if (keepSamples) seq_len(ncol(panel)) else integer(0) + SummarizedExperiment::SummarizedExperiment( + assays = list( + dosage = matrix( + numeric(0), + 0L, + length(cols), + dimnames = list(NULL, colnames(panel)[cols]) + ) + ), + rowRanges = SummarizedExperiment::rowRanges(panel)[integer(0)], + colData = SummarizedExperiment::colData(panel)[cols, , drop = FALSE], + metadata = list_assign( + S4Vectors::metadata(panel), + genotypeSource = .ldRefSource(panel) + ) + ) } # "format @ path" for show methods, which describe where a panel came from -# rather than what is in it. +# rather than what is in it. An LdMatrix came from no file, so it is +# described by its size instead. # @noRd -.ldSketchLabel <- function(x) { - handle <- .ldSketchHandle(x) - glue("{genotypeFormat(handle)} @ {path(handle)}") +.ldRefLabel <- function(x) { + if (.ldIsMatrixRef(x)) { + return(glue("LdMatrix of {length(x)} variants")) + } + if (.ldIsMixtureRef(x)) { + return(glue( + "GenotypeMixture of {length(.gmComponents(x))} panels: ", + "{str_flatten(map_chr(.gmComponents(x), .ldRefLabel), '; ')}" + )) + } + source <- .ldRefSource(x) + if (is.null(source)) { + return(glue("genotype panel of {ncol(x)} samples held in memory")) + } + glue("{source$format} @ {source$path}") } # TRUE for anything that can serve as an LD panel. A bare GenotypeHandle @@ -1228,97 +676,301 @@ loadLdFromGenotype <- function( methods::is(x, "GenotypeHandle") } -# The handle behind an already-open genotype source, or NULL when the value is -# not one. Callers that accept "a panel, a bare handle, or a spec to resolve" -# use this to separate the first two -- which are ready to use -- from the -# third. A panel is the public shape (readGenotypes()); a bare handle is what -# internal callers and objects serialised before the change still carry. +# An already-open genotype source -- a panel, or a bare handle -- as given, +# or NULL when the value is not one. Callers that accept "a panel, a bare +# handle, or a spec to resolve" use this to separate the first two, which are +# ready to use, from the third. A panel is the public shape (readGenotypes()); +# a bare handle is what internal callers and objects serialised before the +# change still carry. # @noRd -.openGenotypeHandle <- function(x) { - if (!.ldIsPanel(x)) { - return(NULL) - } - .ldSketchHandle(x) +.openGenotypeSource <- function(x) { + if (.ldIsPanel(x)) x else NULL } -# ---------- LD sketch: the panel's own view of itself ---------- +# ---------- LD reference: its own view of itself ---------- # -# These read the panel through the SummarizedExperiment interface rather than +# These read a panel through the SummarizedExperiment interface rather than # reaching past it to the GenotypeHandle. The handle is the DelayedArray's # seed -- the READ PATH -- and unwrapping it for metadata leaks that seed into # callers that only ever wanted chrom/pos/alleles or a sample count, which the -# RSE already answers. Reaching for `.ldSketchHandle()` is now reserved for +# RSE already answers. Reaching for `.ldRefHandle()` is now reserved for # what genuinely lives on the handle: file format/path, chromosome shard # routing, the `.afreq` sidecar, and building or pruning the seed itself. # # A bare GenotypeHandle -- what objects serialised before the slot became an # RSE carry -- is projected exactly the way `.genotypeExperiment()` projects -# one, so both shapes answer identically. +# one, so both shapes answer identically. An LdMatrix is itself the GRanges +# of its variants, with the same SNP / A1 / A2 columns. -# The panel's variant ranges: seqnames = chromosome (chr-prefixed), start = -# position, mcols = SNP / A1 / A2. +# The reference's variant ranges: seqnames = chromosome (chr-prefixed), start = +# position, mcols = SNP / A1 / A2 (and AF on an LdMatrix that records it). # @noRd -.ldSketchRanges <- function(x, label = "LD sketch") { +.ldRefRanges <- function(x, label = "LD reference") { # The chokepoint every panel accessor funnels through, so the guard lives # here rather than being repeated at each entry point. Without it a NULL # or non-panel sketch surfaces as "unable to find an inherited method for # 'snpInfo'" from whichever accessor happened to touch it first, which # says nothing about the LD reference being the problem. Callers that - # validate with their own label (`.ldFromSketch`, the ctwas assembler) do + # validate with their own label (`.ldFromRef`, the ctwas assembler) do # so first, so their message wins. - .ldFromSketchValidate(x, label) + .ldFromRefValidate(x, label) + if (.ldIsMatrixRef(x)) { + return(as(x, "GRanges")) + } + if (.ldIsMixtureRef(x)) { + return(.gmRanges(x)) + } if (methods::is(x, "RangedSummarizedExperiment")) { return(SummarizedExperiment::rowRanges(x)) } .genotypeSnpRanges(x, normalizeVariantId(as.character(snpInfo(x)$SNP))) } +# How many variants an LD reference covers. +# @noRd +.ldRefNVariants <- function(x) { + length(.ldRefRanges(x)) +} + # The panel's variant ids in the panel's OWN labelling. This is the mcols # column, not `names()`: the names are normalized ids, while anything keyed on # the panel FILE -- the `.afreq` sidecar, the ctwas snp_map -- has to see the # panel's raw labels. # @noRd -.ldSketchVariantIds <- function(x) { - as.character(S4Vectors::mcols(.ldSketchRanges(x))$SNP) +.ldRefVariantIds <- function(x) { + as.character(S4Vectors::mcols(.ldRefRanges(x))$SNP) } # The panel's variant ids as MATCHING keys: the raw labels, with any id whose # allele slot holds a tag rather than an allele (chr21:13988152:INS:T) re- # rendered from the panel's own A1 / A2 columns, which are correct. Use this # wherever a panel id is matched against summary statistics or another id set, -# and `.ldSketchVariantIds()` only where the file's literal label is wanted. +# and `.ldRefVariantIds()` only where the file's literal label is wanted. # # The repair is what lets an untidily-named panel entry be MATCHED rather than # discarded: harmonization re-keys a surviving variant to the reference-allele # id, so the panel side has to speak the same form or every later id lookup # misses the variant it just harmonized. # @noRd -.ldSketchMatchIds <- function(x) { - mc <- S4Vectors::mcols(.ldSketchRanges(x)) - ids <- as.character(mc$SNP) - if (is.null(mc$A1) || is.null(mc$A2)) { - return(ids) +.ldRefMatchIds <- function(x) { + mc <- S4Vectors::mcols(.ldRefRanges(x)) + ids <- as.character(mc$SNP) + if (is.null(mc$A1) || is.null(mc$A2)) { + return(ids) + } + .repairVariantIds(ids, as.character(mc$A2), as.character(mc$A1)) +} + +# Per-variant chromosome, canonical (no chr prefix). +# @noRd +.ldRefChrom <- function(x) { + canonChrom(as.character(GenomicRanges::seqnames(.ldRefRanges(x)))) +} + +# A panel's sample ids. An LdMatrix has no samples, so asking is a bug in the +# caller rather than something to answer with an empty vector. +# @noRd +.ldRefSampleIds <- function(x) { + if (.ldIsMatrixRef(x) || .ldIsMixtureRef(x)) { + abort( + "Only a single genotype panel has one sample set.", + .internal = TRUE + ) + } + if (methods::is(x, "RangedSummarizedExperiment")) { + return(as.character(colnames(x))) + } + as.character(sampleIds(x)) +} + +# A panel's column count -- its samples, or a sketch's pseudo-samples -- or +# the size an LdMatrix records (NA when it records none). +# @noRd +.ldRefNSamples <- function(x) { + if (.ldIsMatrixRef(x)) { + return(nSamples(x)) + } + if (.ldIsMixtureRef(x)) { + return(.gmPooledN(x, .ldRefNSamples)) + } + length(.ldRefSampleIds(x)) +} + +# The number of real people behind an LD reference, for statistics that count +# them -- a MAC cutoff's allele counts, DENTIST's truncation rank. A genotype +# panel's columns are its samples. A stochastic sketch's columns are B +# pseudo-samples projected from n real ones (the projection cannot raise the +# rank of their LD above n), so its n is the one its projection record holds. +# An LdMatrix records its size, or NA. +# @noRd +.ldRefRealNSamples <- function(x) { + if (.ldIsMatrixRef(x)) { + return(nSamples(x)) + } + if (.ldIsMixtureRef(x)) { + return(sum(map_int(.gmComponents(x), .ldRefRealNSamples))) + } + .ldRefProjection(x)$nSamples %||% .ldRefNSamples(x) +} + +# The number of independent samples a reference's LD is worth, for the +# estimators that correct for LD-estimation noise (ldsc's unbiased r^2, HDL's +# finite reference). A stochastic sketch's LD carries the noise of its n real +# samples and of its B pseudo-samples, so it is worth 1 / (1/n + 1/B). An +# LdMatrix records this directly. +# @noRd +.ldRefNoiseN <- function(x) { + if (.ldIsMatrixRef(x)) { + return(nSamples(x)) + } + if (.ldIsMixtureRef(x)) { + return(.gmPooledN(x, .ldRefNoiseN)) + } + nCols <- .ldRefNSamples(x) + n <- .ldRefProjection(x)$nSamples + if (is.null(n)) { + return(nCols) + } + as.integer(round(1 / (1 / n + 1 / nCols))) +} + +# Each of the reference's variants' A1 frequency, or NULL when it records +# none. An LdMatrix and a stochastic sketch carry an `AF` column; a bare +# sketch handle (an object serialised before the slot became an RSE) answers +# from its sidecar. A panel of real genotypes measures frequencies from its +# dosages instead, so it answers NULL here. +# @noRd +.ldRefAf <- function(x) { + if (.ldIsMixtureRef(x)) { + return(.gmAf(x)) + } + af <- S4Vectors::mcols(.ldRefRanges(x))$AF + if (!is.null(af) || !methods::is(x, "GenotypeHandle")) { + return(af) + } + record <- .ldRefProjection(x) + if (is.null(record)) { + return(NULL) + } + as.numeric(record$af$alt_freq)[match(.ldRefVariantIds(x), record$af$id)] +} + +# The real sample size a step needs, or an error naming the step when the +# reference cannot say -- an LdMatrix built without `nSamples`. +# @noRd +.ldRefRequireNSamples <- function(x, step) { + n <- .ldRefRealNSamples(x) + if (is.na(n)) { + abort(c( + glue("{step} needs the LD reference's sample size."), + i = "Record it with LdMatrix(nSamples = )." + )) + } + n +} + +# A stochastic sketch's projection record -- list(nSamples, af) -- or NULL for +# a panel of real genotypes and for an LdMatrix. A panel built by +# .genotypeExperiment() carries it from construction (see .withLdProjection); +# a bare handle, which only objects serialised before the slot became an RSE +# carry, is read from its sidecar on demand. +# @noRd +.ldRefProjection <- function(x) { + if (is.null(x) || .ldIsMatrixRef(x) || .ldIsMixtureRef(x)) { + return(NULL) + } + if (methods::is(x, "RangedSummarizedExperiment")) { + return(S4Vectors::metadata(x)$ldProjection) + } + .sketchProjectionRecord(.ldRefHandle(x)) +} + +# TRUE for a stochastic sketch: a panel whose columns are pseudo-samples +# projected from real ones, so its dosages are not genotypes and say nothing +# about allele frequency or missingness. +# @noRd +.ldRefIsProjection <- function(x) { + !is.null(.ldRefProjection(x)) +} + +# Read a stochastic sketch's sidecar: its real sample count -- the largest +# OBS_CT, halved (OBS_CT counts alleles, two per diploid sample; variants with +# missing calls count fewer) -- and each variant's real A1 frequency. NULL +# when the handle is not a sketch, i.e. carries no PLINK2-style .afreq beside +# it (in any genotype format) with U_MIN / U_MAX and OBS_CT. +# @noRd +.sketchProjectionRecord <- function(handle) { + if (is.null(handle)) { + return(NULL) } - .repairVariantIds(ids, as.character(mc$A2), as.character(mc$A1)) + .afreqProjectionRecord(.genotypeSourceRecord(handle), snpInfo(handle)$CHR) } -# Per-variant chromosome, canonical (no chr prefix). +# .sketchProjectionRecord() for a genotype source and the chromosomes its +# panel spans, for a panel that reads through no handle. # @noRd -.ldSketchChrom <- function(x) { - canonChrom(as.character(GenomicRanges::seqnames(.ldSketchRanges(x)))) +.afreqProjectionRecord <- function(source, chroms) { + tbls <- compact(map(.afreqPrefixes(source, chroms), .afreqIfStochastic)) + if (length(tbls) == 0L) { + return(NULL) + } + af <- list_rbind(tbls) + list( + nSamples = as.integer(max(as.numeric(af$obs_ct), na.rm = TRUE) %/% 2), + af = select(af, all_of(c("id", "alt_freq"))) + ) } +# Record a stochastic sketch's real sample count and allele frequencies on the +# panel built over it: the count in metadata, the frequencies as an `AF` +# column of rowRanges (the frequency of A1, which PLINK2 writes as ALT). Done +# once, at construction, so nothing downstream re-reads the sidecar -- or, +# worse, measures frequencies off the projected dosages. # @noRd -.ldSketchSampleIds <- function(x) { - if (methods::is(x, "RangedSummarizedExperiment")) { - return(as.character(colnames(x))) +.withLdProjection <- function(panel, record) { + if (is.null(record)) { + return(panel) } - as.character(sampleIds(x)) + rr <- SummarizedExperiment::rowRanges(panel) + mc <- S4Vectors::mcols(rr) + mc$AF <- as.numeric(record$af$alt_freq)[ + match(as.character(mc$SNP), record$af$id) + ] + withAf <- SummarizedExperiment::`rowRanges<-`( + panel, + value = S4Vectors::`mcols<-`(rr, value = mc) + ) + S4Vectors::`metadata<-`( + withAf, + value = c( + S4Vectors::metadata(withAf), + list(ldProjection = list(nSamples = record$nSamples)) + ) + ) +} + +# .withLdProjection() for a panel that reads through no handle -- a VCF or +# GDS panel, or a per-chromosome set of them -- its sidecar found from the +# genotype source the panel records. +# @noRd +.withSourceProjection <- function(panel) { + .withLdProjection( + panel, + .afreqProjectionRecord(.ldRefSource(panel), .ldRefChrom(panel)) + ) } +# The .afreq at `prefix` when it is a stochastic sketch's (it carries U_MIN / +# U_MAX) and records OBS_CT, else NULL. A malformed sidecar reads as absent, +# as it does for the panel filter. # @noRd -.ldSketchNSamples <- function(x) { - length(.ldSketchSampleIds(x)) +#' @importFrom rlang try_fetch +.afreqIfStochastic <- function(prefix) { + af <- try_fetch(readAfreq(prefix), error = function(cnd) NULL) + if (is.null(af) || !all(is_in(c("u_min", "obs_ct"), colnames(af)))) { + return(NULL) + } + af } # Dosage for a variant subset, samples x variants. @@ -1329,9 +981,15 @@ loadLdFromGenotype <- function( # delegates to that same function with `meanImpute = FALSE`, so imputation is # layered on here rather than pushed down. # @noRd -.ldSketchDosage <- function(x, snpIdx, meanImpute = TRUE) { - # The other primitive that does not pass through `.ldSketchRanges()`. - .ldFromSketchValidate(x, "LD sketch") +.ldRefDosage <- function(x, snpIdx, meanImpute = TRUE) { + # The other primitive that does not pass through `.ldRefRanges()`. + .ldFromRefValidate(x, "LD reference") + if (.ldIsMatrixRef(x) || .ldIsMixtureRef(x)) { + abort( + "Only a single genotype panel has dosages to read.", + .internal = TRUE + ) + } if (!methods::is(x, "RangedSummarizedExperiment")) { return(.dosageMatrix(x, snpIdx, meanImpute = meanImpute)) } @@ -1341,39 +999,43 @@ loadLdFromGenotype <- function( if (meanImpute) .qtlMeanImpute(dosage) else dosage } -.ldFromSketchValidate <- function(ldSketch, label) { - if (is.null(ldSketch)) { +# Require a non-NULL LD reference: a genotype panel (a +# RangedSummarizedExperiment, or a bare GenotypeHandle) or an LdMatrix. +# @noRd +.ldFromRefValidate <- function(ldReference, label) { + if (is.null(ldReference)) { msg <- glue( - "{label}: the SumStats/collection carries no ldSketch ", - "(ldSketch = NULL); this step needs an LD reference." + "{label}: the SumStats/collection carries no ldReference ", + "(ldReference = NULL); this step needs an LD reference." ) abort(msg) } # A bare GenotypeHandle is still accepted: that is what objects # serialised before the slot became an RSE carry. - if (!.ldIsPanel(ldSketch)) { + if (!.ldIsReference(ldReference)) { msg <- glue( - "{label}: ldSketch must be a genotype panel ", - "(RangedSummarizedExperiment or GenotypeHandle)." + "{label}: ldReference must be a genotype panel ", + "(RangedSummarizedExperiment or GenotypeHandle), a ", + "GenotypeMixture or an LdMatrix." ) abort(msg) } } # Match requested ids to the panel; NULL if none match. Returns kept ids/order. -.ldFromSketchMatch <- function(ldSketch, variantIds, label, onMissing) { +.ldFromRefMatch <- function(ldReference, variantIds, label, onMissing) { # Match by (chrom, pos, allele) tuple with an exact id-string fallback for # rsID panels; the caller's original ids and order are preserved. m <- matchVariants( variantIds, - .ldSketchMatchIds(ldSketch), + .ldRefMatchIds(ldReference), removeStrandAmbiguous = FALSE ) nMissing <- length(variantIds) - length(m$idxA) if (nMissing > 0L && onMissing == "error") { msg <- glue( "{label}: {nMissing} variant id(s) not present in the LD ", - "sketch panel." + "reference." ) abort(msg) } @@ -1388,23 +1050,44 @@ loadLdFromGenotype <- function( ) } -.ldFromSketch <- function( - ldSketch, +# The LD matrix over a specified variant subset of an LD reference: computed +# from a genotype panel's dosages, or read out of an LdMatrix. Called directly +# by +# twasWeightsPipeline, fineMappingPipeline, jointEngine, +# causalInferencePipeline, colocboostPipeline and the coloc / summaryStatsQc +# paths, which differ only in their error message prefix and in whether +# variants absent from the panel raise an error or get silently dropped -- +# both of which are arguments here rather than a per-pipeline wrapper. +# +# Arguments: +# ldReference A genotype panel (RangedSummarizedExperiment, or a bare +# GenotypeHandle) or an LdMatrix. +# variantIds Character vector of SNP IDs to extract. +# label Error-message prefix, e.g. "twasWeightsPipeline". +# onMissing "error" (default) -> any unmatched id stops the call; +# "drop" -> unmatched ids are silently filtered. +# When "drop" leaves no surviving variants the function +# returns NULL. +# +# Returns: +# A `length(variantIds) x length(variantIds)` symmetric LD matrix with +# rows/cols named by the (possibly filtered) `variantIds`. +# With `onMissing = "drop"` the returned matrix carries an attribute +# `"keptVariantIds"` so callers can recover which ids survived. +# @noRd +.ldFromRef <- function( + ldReference, variantIds, - label = ".ldFromSketch", + label = ".ldFromRef", onMissing = c("error", "drop") ) { - .ldFromSketchValidate(ldSketch, label) + .ldFromRefValidate(ldReference, label) onMissing <- arg_match(onMissing) - matched <- .ldFromSketchMatch(ldSketch, variantIds, label, onMissing) + matched <- .ldFromRefMatch(ldReference, variantIds, label, onMissing) if (is.null(matched)) { return(NULL) } - ldMat <- computeLd( - ldSketch, - method = "sample", - snpIdx = matched$idx - ) + ldMat <- .ldRefLdAt(ldReference, matched$idx) # The match above is allele-aware, so a panel entry whose alleles are # swapped relative to the caller's id still matches -- but the dosage it # returns counts the OTHER allele, which negates every correlation that @@ -1424,7 +1107,238 @@ loadLdFromGenotype <- function( named } -# ---------- LD sketch: per-variant panel statistics and filtering ---------- +# The factor of a genotype reference's LD over `variantIds` (see +# .ldRefFactor()), matched and allele-aligned exactly as .ldFromRef() does: +# columns follow the caller's ids and are named by them, and a column whose +# panel alleles are swapped is negated, so crossprod(F) / (nrow(F) - 1) is +# .ldFromRef()'s matrix. An LdMatrix has no factor, so callers send it down +# the matrix path instead. +# @noRd +.ldFactorFromRef <- function( + ldReference, + variantIds, + label = ".ldFactorFromRef", + onMissing = c("error", "drop") +) { + .ldFromRefValidate(ldReference, label) + onMissing <- arg_match(onMissing) + matched <- .ldFromRefMatch(ldReference, variantIds, label, onMissing) + if (is.null(matched)) { + return(NULL) + } + factor <- .ldRefFactor(ldReference, matched$idx) + signed <- if (any(matched$sign < 0)) { + sweep(factor, 2L, matched$sign, `*`) + } else { + factor + } + named <- `dimnames<-`(signed, list(NULL, matched$keptIds)) + if (onMissing == "drop") { + return(`attr<-`(named, "keptVariantIds", matched$keptIds)) + } + named +} + +# The LD among the reference's variants at `idx`, in the reference's own +# allele coding: the sample correlation of a panel's dosages, or the +# corresponding block of an LdMatrix. +# @noRd +.ldRefLdAt <- function(ldReference, idx) { + if (.ldIsMatrixRef(ldReference)) { + return(unname(ldMatrix(ldReference)[idx, idx, drop = FALSE])) + } + if (.ldIsMixtureRef(ldReference)) { + return(.gmLdAt(ldReference, idx)) + } + computeLd(ldReference, method = "sample", snpIdx = idx) +} + +# A factor F of a genotype reference's LD over `idx`: a matrix with +# zero-mean columns whose crossprod(F) / (nrow(F) - 1) is exactly the LD +# .ldRefLdAt() returns, so every computation on that LD -- quadratic forms, +# products, columns, eigenvectors -- can run on F without forming the p x p +# matrix. Its rows behave as samples, so an engine that correlates them +# itself recovers the same LD. A panel's factor is its standardized dosages; +# a mixture's stacks its panels' (.gmFactor()). +# @noRd +.ldRefFactor <- function(ldReference, idx) { + if (.ldIsMixtureRef(ldReference)) { + return(.gmFactor(ldReference, idx)) + } + .ldFactorFromDosage(.ldRefDosage(ldReference, idx, meanImpute = TRUE)) +} + +# A panel's mean-imputed, standardized dosages over `idx`, with a monomorphic +# column left at zero: the blocks a mixture's factor is stacked from. +# @noRd +.ldRefStandardized <- function(ldReference, idx) { + .ldStandardize(.ldRefDosage(ldReference, idx, meanImpute = TRUE)) +} + +# The factor of a dosage matrix's (samples x variants) LD, under +# .ldRefFactor()'s contract. A monomorphic variant is independent of every +# other with unit variance in computeLd()'s LD, while its standardized +# column is all zero; two padding rows of +/- sqrt((m - 1) / 2) restore its +# variance without correlating it with anything or moving any column's mean, +# and the other rows are rescaled for the m rows the factor then has. +# @noRd +.ldFactorFromDosage <- function(X) { + standardized <- .ldStandardize(X) + dead <- which(colSums(standardized^2) == 0) + if (length(dead) == 0L) { + return(standardized) + } + n <- nrow(standardized) + m <- n + 2L * length(dead) + rbind( + standardized * sqrt((m - 1) / (n - 1)), + .ldPaddingRows(dead, ncol(standardized), sqrt((m - 1) / 2)) + ) +} + +# Mean-imputed columns, centred and scaled to unit variance (n - 1 +# denominator), as scale() would make them, except that a constant column +# stays zero rather than turning into NaN. +# @noRd +.ldStandardize <- function(X) { + imputed <- if (anyNA(X)) .ldMeanImputeColumns(X) else X + centred <- sweep(imputed, 2L, colMeans(imputed)) + sds <- sqrt(colSums(centred^2) / (nrow(centred) - 1)) + sweep(centred, 2L, replace(sds, sds == 0, 1), `/`) +} + +# Two rows per column in `cols`, holding +value and -value in that column +# and zero elsewhere. They add 2 value^2 to the column's sum of squares and +# nothing to its mean or to any crossproduct. +# @noRd +.ldPaddingRows <- function(cols, p, value) { + rows <- matrix(0, 2L * length(cols), p) + i <- seq_along(cols) + rows[cbind(2L * i - 1L, cols)] <- value + rows[cbind(2L * i, cols)] <- -value + rows +} + +# The eigendecomposition of the LD R = crossprod(F) / (m - 1), from the thin +# SVD of its factor F (m x p) rather than from R: the eigenvalues d^2 / (m - 1) +# and the right singular vectors, for the min(m, p) directions the SVD +# returns, largest first. R's other p - min(m, p) eigenvalues are exactly +# zero; `nNull` counts them, for an estimator that counts every direction. +# @noRd +.ldFactorEigen <- function(F) { + s <- svd(F, nu = 0L) + list( + values = s$d^2 / (nrow(F) - 1), + vectors = s$v, + nNull = ncol(F) - length(s$d) + ) +} + +# ---------- LD rows a stripe at a time ---------- +# +# An LD source is one block's LD in any of three forms: the matrix itself, +# list(R = ); a factor of it, list(F = ) with R = crossprod(F) / (m - 1); or +# an eigendecomposition, list(vectors = , values = ) with R = V diag(values) +# V'. The helpers below read R's rows from any of them, so a computation over +# R's rows never needs the matrix whole. + +# How many of R's rows a stripe holds. +# @noRd +.ldStripeSize <- function() 256L + +# The number of variants an LD source covers. +# @noRd +.ldSourceSize <- function(ld) { + if (!is.null(ld$R)) { + return(nrow(ld$R)) + } + if (!is.null(ld$F)) { + return(ncol(ld$F)) + } + nrow(ld$vectors) +} + +# Rows `rows` of an LD source's R, without forming the rest. +# @noRd +.ldSourceRows <- function(ld, rows) { + if (!is.null(ld$R)) { + return(ld$R[rows, , drop = FALSE]) + } + if (!is.null(ld$F)) { + return( + crossprod(ld$F[, rows, drop = FALSE], ld$F) / (nrow(ld$F) - 1) + ) + } + scaled <- sweep(ld$vectors[rows, , drop = FALSE], 2L, ld$values, "*") + tcrossprod(scaled, ld$vectors) +} + +# (R o R) A -- R squared element-wise, times A -- for an LD source: column a +# of the result is the LD score sum_k r^2_jk a_k. The element-wise square +# does not inherit a factor's low rank (R has rank below m, R o R is in +# general full rank), so no product through the factor serves every column +# of A at once: one Gram matrix F diag(a) F' per column would cost m^2 p for +# each. Instead R's rows are formed a stripe at a time, squared, and +# multiplied by all of A, which costs p^2 (m + ncol(A)) and holds one stripe. +# A stored matrix is used as it is. +# @noRd +.ldSquaredProduct <- function(ld, A) { + if (!is.null(ld$R)) { + return((ld$R^2) %*% A) + } + p <- .ldSourceSize(ld) + starts <- seq.int(1L, p, by = .ldStripeSize()) + stripes <- map(starts, .ldSquaredProductStripe, ld = ld, A = A, p = p) + exec(rbind, !!!stripes) +} + +# @noRd +.ldSquaredProductStripe <- function(start, ld, A, p) { + rows <- seq.int(start, min(start + .ldStripeSize() - 1L, p)) + .ldSourceRows(ld, rows)^2 %*% A +} + +# `ldReference` cut to the variants `variantIds` names (matched as +# .ldFromRef() matches them), for a caller that reads the same block's LD +# several times: a panel's dosage is read from its files once, here, and held +# in memory, rather than read again by every reader. An LdMatrix is cut to +# those variants, and a GenotypeMixture is cut with its panels left to read +# as they are needed. +# @noRd +.ldRefInMemory <- function(ldReference, variantIds, label) { + .ldFromRefValidate(ldReference, label) + matched <- .ldFromRefMatch(ldReference, variantIds, label, "error") + rows <- sort(unique(matched$idx)) + if (!methods::is(ldReference, "RangedSummarizedExperiment")) { + if (methods::is(ldReference, "GenotypeHandle")) { + return(ldReference) + } + return(ldReference[rows]) + } + block <- ldReference[rows, ] + SummarizedExperiment::`assay<-`( + block, + "dosage", + withDimnames = FALSE, + value = as.matrix(SummarizedExperiment::assay( + block, + "dosage", + withDimnames = FALSE + )) + ) +} + +# b' R b for each column b of B, from an LD matrix or a factor of it: +# ||F b||^2 / (m - 1), never forming R. +# @noRd +.ldQuadForms <- function(ld, B) { + if (!is.null(ld$R)) { + return(colSums(B * (ld$R %*% B))) + } + colSums((ld$F %*% B)^2) / (nrow(ld$F) - 1) +} + +# ---------- LD reference: per-variant statistics and filtering ---------- # Per-variant minor allele frequency and missingness rate from panel dosage. # @@ -1459,19 +1373,23 @@ loadLdFromGenotype <- function( max(mafCutoff, macAsMaf) } -# The .afreq stems to read for a panel: the handle's own stem, or one per -# chromosome the panel actually spans. A sharded handle lists every -# chromosome in its manifest, and reading the shards the summary statistics -# never touch is the cost per-chromosome sketch trimming exists to avoid. +# The .afreq stems to read for a genotype source: its file's, or one per +# chromosome of `chroms` (those its panel spans) for a per-chromosome +# manifest. A manifest lists every chromosome, and reading the shards the +# summary statistics never touch is the cost per-chromosome sketch trimming +# exists to avoid. None for a panel that reads no file. # @noRd -.panelAfreqPrefixes <- function(handle) { - chromPaths <- .genotypeChromPaths(handle) +.afreqPrefixes <- function(source, chroms) { + if (is.null(source)) { + return(character(0)) + } + chromPaths <- source$chromPaths %||% character(0) if (length(chromPaths) == 0L) { - return(.genotypeReadPath(handle)) + return(.genotypeStem(.resolveGenotypeResourcePath(source$path))) } - spanned <- unique(canonChrom(as.character(snpInfo(handle)$CHR))) + spanned <- unique(canonChrom(as.character(chroms))) paths <- unname(chromPaths[is_in(names(chromPaths), spanned)]) - map_chr(paths, .resolveGenotypeResourcePath) + .genotypeStem(map_chr(paths, .resolveGenotypeResourcePath)) } # One shard's id/alt_freq pairs, or NULL when the sidecar is missing or does @@ -1487,18 +1405,16 @@ loadLdFromGenotype <- function( select(af, all_of(c("id", "alt_freq"))) } -# Panel minor allele frequency for `variantIds` read from the PLINK2 .afreq -# sidecar, or NULL when the sidecar cannot answer for every id. +# Panel minor allele frequency for `variantIds` read from the PLINK2-style +# .afreq sidecar beside the panel's file, in any genotype format, or NULL +# when the sidecar cannot answer for every id. # # Refusing a PARTIAL sidecar is deliberate: the dosage path treats a variant # it cannot measure as a drop, so a fast path that quietly kept the ids # .afreq happens to omit would disagree with it about the same variant. # @noRd -.panelAfreqMaf <- function(handle, variantIds) { - if (genotypeFormat(handle) != "plink2") { - return(NULL) - } - tbls <- compact(map(.panelAfreqPrefixes(handle), .panelAfreqTable)) +.panelAfreqMaf <- function(source, chroms, variantIds) { + tbls <- compact(map(.afreqPrefixes(source, chroms), .panelAfreqTable)) if (length(tbls) == 0L) { return(NULL) } @@ -1511,54 +1427,160 @@ loadLdFromGenotype <- function( pmin(altFreq, 1 - altFreq) } -# TRUE for each matched panel variant that fails the cutoffs. -# -# Allele frequency alone answers MAF and MAC, so with no missingness cutoff -# set the .afreq sidecar decides the whole mask without materializing panel -# dosage -- which is what makes the filter affordable on a panel far larger -# than the analysed variant set. Missingness is not derivable from the -# sidecar (its observation count is an ALLELE count, whose ploidy the mask -# would have to guess), so an imissCutoff always takes the dosage path. -# @noRd -.panelDropMask <- function(ldSketch, matched, panelFilterParam) { +# TRUE for each matched variant of the LD reference that fails the cutoffs. +# The statistics come from `.ldRefVariantStats()`, which knows where each +# kind of reference keeps them truthfully; missingness is only asked for when +# a missingness cutoff is set. +# @noRd +.panelDropMask <- function(ldReference, matched, panelFilterParam) { imissCutoff <- panelFilterParam$imissCutoff - if (imissCutoff >= 1) { - # Keyed on the PANEL's own variant labels, not the caller's: .afreq - # carries the .pvar ids, while `keptIds` is whatever id form the - # caller asked in (normalized, most often). - panelIds <- .ldSketchVariantIds(ldSketch)[matched$idx] - # The handle stays for this one read: the sidecar is a file beside - # the genotypes, which only the seed knows how to find. - maf <- .panelAfreqMaf(.ldSketchHandle(ldSketch), panelIds) - if (!is.null(maf)) { - effMaf <- .panelEffectiveMaf( - panelFilterParam$mafCutoff, - panelFilterParam$macCutoff, - .ldSketchNSamples(ldSketch) - ) - return(is.na(maf) | maf < effMaf) - } - } - dosage <- .ldSketchDosage(ldSketch, matched$idx, meanImpute = FALSE) - stats <- .panelVariantStats(dosage) + stats <- .ldRefVariantStats( + ldReference, + matched$idx, + needMissing = imissCutoff < 1, + step = "The LD-panel filter" + ) effMaf <- .panelEffectiveMaf( panelFilterParam$mafCutoff, panelFilterParam$macCutoff, - nrow(dosage) + .ldRefMacSamples(ldReference, panelFilterParam$macCutoff) ) - is.na(stats$maf) | stats$maf < effMaf | stats$missRate > imissCutoff + fails <- is.na(stats$maf) | stats$maf < effMaf + if (is.null(stats$missRate)) { + return(fails) + } + fails | stats$missRate > imissCutoff +} + +# Per-variant MAF -- and missingness, when `needMissing` -- for the +# reference's variants at `idx`, from wherever the reference records them +# truthfully: +# +# genotype panel the .afreq sidecar when it answers for every variant and +# no missingness is wanted (no dosage need be read, which is +# what keeps the filter affordable on a panel far larger +# than the analysed variant set); the dosages otherwise +# sketch the real frequencies its projection record holds. Its +# dosages are projections taken after mean-filling, so they +# carry neither frequencies nor missingness +# LdMatrix its AF column; it has no genotypes to be missing +# +# `step` names the caller in the error when the reference cannot answer. +# @noRd +.ldRefVariantStats <- function(ldReference, idx, needMissing, step) { + if (.ldIsMixtureRef(ldReference)) { + return(.gmVariantStats(ldReference, idx, needMissing, step)) + } + if (.ldIsMatrixRef(ldReference) || .ldRefIsProjection(ldReference)) { + return(.ldRefRecordedStats(ldReference, idx, needMissing, step)) + } + if (!needMissing) { + # Keyed on the PANEL's own variant labels: .afreq carries the .pvar + # ids. The handle stays for this one read, since the sidecar is a + # file beside the genotypes, which only the seed knows how to find. + panelIds <- .ldRefVariantIds(ldReference)[idx] + maf <- .panelAfreqMaf( + .ldRefSource(ldReference), + .ldRefChrom(ldReference), + panelIds + ) + if (!is.null(maf)) { + return(list(maf = maf, missRate = NULL)) + } + } + .panelVariantStats(.ldRefDosage(ldReference, idx, meanImpute = FALSE)) +} + +# Frequencies from a reference that records them rather than measuring them: +# an LdMatrix or a stochastic sketch. Neither can say how often a genotype is +# missing, so a missingness cutoff is refused rather than silently skipped. +# @noRd +.ldRefRecordedStats <- function(ldReference, idx, needMissing, step) { + what <- if (.ldIsMatrixRef(ldReference)) { + "an LdMatrix, which has no genotypes" + } else { + "a stochastic sketch, whose dosages are projections" + } + if (needMissing) { + abort(c( + glue( + "{step} needs genotype missingness, and the LD reference is ", + "{what}." + ), + i = "Leave its missingness cutoff unset." + )) + } + af <- .ldRefAf(ldReference) + if (is.null(af)) { + abort(c( + glue( + "{step} needs allele frequencies, and the LD reference ", + "records none." + ), + i = "Give the LdMatrix's variants an `AF` column." + )) + } + af <- as.numeric(af)[idx] + list(maf = pmin(af, 1 - af), missRate = NULL) +} + +# Per-variant genotype variance at `idx`, for scaling non-standardized weights +# to the correlation scale. A panel of real genotypes measures it on its +# (mean-imputed) dosages. A sketch or an LdMatrix has no genotypes to measure, +# so it gives 2p(1 - p) from its recorded A1 frequency -- the dosage variance +# under Hardy-Weinberg -- or NA when it records none. +# @noRd +.ldRefVariance <- function(ldReference, idx) { + if (.ldIsMixtureRef(ldReference)) { + return(.gmVariance(ldReference, idx)) + } + if (.ldIsMatrixRef(ldReference) || .ldRefIsProjection(ldReference)) { + af <- .ldRefAf(ldReference) + if (is.null(af)) { + return(rep(NA_real_, length(idx))) + } + af <- as.numeric(af)[idx] + return(2 * af * (1 - af)) + } + geno <- .ldRefDosage(ldReference, idx, meanImpute = TRUE) + apply(geno, 2, stats::var, na.rm = TRUE) +} + +# The real sample count a MAC cutoff divides by, or 0 when no MAC cutoff is +# set (which .panelEffectiveMaf() reads as "no MAC term"). A mixture of +# panels has no single count, so a MAC cutoff on one is refused. +# @noRd +.ldRefMacSamples <- function(ldReference, macCutoff) { + if (macCutoff <= 0) { + return(0L) + } + # A mixture's statistics are its panels' worst (see .gmVariantStats()), so + # its counts are judged against its smallest panel. + if (.ldIsMixtureRef(ldReference)) { + return(min(map_int(.gmComponents(ldReference), .ldRefRealNSamples))) + } + if (.ldIsMatrixRef(ldReference) && !is.null(mixtureWeights(ldReference))) { + abort(c( + str_c( + "A MAC cutoff counts one panel's alleles, and the LdMatrix ", + "is a mixture." + ), + i = "Use `mafCutoff` instead." + )) + } + .ldRefRequireNSamples(ldReference, "A MAC cutoff") } # The subset of `variantIds` whose LD-panel genotypes clear the cutoffs, # returned in the caller's order. # # Variants absent from the panel are passed through untouched. Whether a -# missing variant is an error or is silently dropped belongs to `.ldFromSketch` +# missing variant is an error or is silently dropped belongs to `.ldFromRef` # (its `onMissing`), and deciding it a second time here would let the two # disagree about the same variant. # @noRd .panelVariantFilter <- function( - ldSketch, + ldReference, variantIds, panelFilterParam = PanelFilterParam(), label = ".panelVariantFilter" @@ -1569,25 +1591,25 @@ loadLdFromGenotype <- function( if (is.null(cutoffs)) { return(variantIds) } - if (is.null(ldSketch) || length(variantIds) == 0L) { + if (is.null(ldReference) || length(variantIds) == 0L) { return(variantIds) } - matched <- .ldFromSketchMatch(ldSketch, variantIds, label, "drop") + matched <- .ldFromRefMatch(ldReference, variantIds, label, "drop") if (is.null(matched)) { return(variantIds) } - drop <- .panelDropMask(ldSketch, matched, cutoffs) + drop <- .panelDropMask(ldReference, matched, cutoffs) variantIds[!is_in(variantIds, matched$keptIds[drop])] } # Row mask for the variants clearing the LD-panel cutoffs, reporting how many # were dropped. A no-op when no cutoff is set. # @noRd -.panelKeepMask <- function(variantIds, ldSketch, cutoffs, label) { - if (is.null(ldSketch) || is.null(cutoffs)) { +.panelKeepMask <- function(variantIds, ldReference, cutoffs, label) { + if (is.null(ldReference) || is.null(cutoffs)) { return(rep(TRUE, length(variantIds))) } - kept <- .panelVariantFilter(ldSketch, variantIds, cutoffs, label = label) + kept <- .panelVariantFilter(ldReference, variantIds, cutoffs, label = label) keep <- is_in(variantIds, kept) nDropped <- sum(!keep) if (nDropped > 0L) { @@ -1615,11 +1637,12 @@ loadLdFromGenotype <- function( } list(mafCutoff = maf, macCutoff = mac, imissCutoff = imiss) } -# ---------- LD sketch: cross-pipeline LD-panel compatibility check ---------- +# ---------- LD reference: cross-pipeline compatibility check ---------- -# Internal: assert that two LD sketches describe the same reference panel -- -# the same samples, and the same allele coding (A1/A2 in the same orientation) -# on the variants they share. The SNP label is not compared, so a pure +# Internal: assert that two LD references describe the same reference panel -- +# the same samples (for genotype panels) or the same LD (for LdMatrix +# objects), and the same allele coding (A1/A2 in the same orientation) on the +# variants they share. The SNP label is not compared, so a pure # chr-prefix difference does not fail; an allele swap still does, since it # means the two panels code their LD in opposite directions. # @@ -1642,11 +1665,11 @@ loadLdFromGenotype <- function( # "twasWeights" or "fineMappingResult"); it is woven into the error # messages when provided. `pipelineName` prefixes every error so the # failure source remains discoverable. -# --- .requireMatchingLdSketches helpers ------------------------------------- +# --- .requireMatchingLdRefs helpers ------------------------------------- # Null handling: TRUE if the caller should return early (qtl NULL, or gwas NULL # under a lenient policy); errors when a non-NULL qtl faces a NULL gwas sketch. -.ldSketchNullGuard <- function(qtlLd, gwasLd, pipelineName, label, nullPolicy) { +.ldRefNullGuard <- function(qtlLd, gwasLd, pipelineName, label, nullPolicy) { if (is.null(qtlLd)) { return(TRUE) } @@ -1655,12 +1678,12 @@ loadLdFromGenotype <- function( return(TRUE) } labelPart <- if (!is.null(label)) { - glue("ldSketch on `{label}` is non-NULL ") + glue("ldReference on `{label}` is non-NULL ") } else { - "qtl ldSketch is non-NULL " + "qtl ldReference is non-NULL " } msg <- glue( - "{pipelineName}: {labelPart}but the GWAS ldSketch is NULL." + "{pipelineName}: {labelPart}but the GWAS ldReference is NULL." ) abort(msg) } @@ -1670,32 +1693,54 @@ loadLdFromGenotype <- function( # Both must be genotype panels. Panel SIZE is deliberately NOT compared: two # objects that share one LD sketch stop carrying identical variant sets as soon # as they are QC'd separately, because each object's sketch is trimmed to its -# own position span at load (`.subsetSketchToRange`) and to its own surviving -# variants at the end of QC (`.subsetSketchToIds`). A QTL and a GWAS collection +# own position span at load (`.subsetLdRefToRange`) and to its own surviving +# variants at the end of QC (`.subsetLdRefToIds`). A QTL and a GWAS collection # put through `summaryStatsQc` independently therefore diverge by construction, # which is the normal case rather than a mistake. Every downstream LD lookup -# matches by variant id (`.ldFromSketch`), never by position in the panel, so +# matches by variant id (`.ldFromRef`), never by position in the panel, so # divergent variant sets cost only the variants one side lacks. What would not # be harmless -- two genuinely different reference panels -- is what -# `.ldSketchCheckContent` catches, by sample set and by allele coding. -.ldSketchCheckShape <- function(qtlLd, gwasLd, pipelineName, between) { - if (!.ldIsPanel(qtlLd) || !.ldIsPanel(gwasLd)) { +# `.ldRefCheckContent` catches, by sample set and by allele coding. +.ldRefCheckShape <- function(qtlLd, gwasLd, pipelineName, between) { + if (!.ldIsReference(qtlLd) || !.ldIsReference(gwasLd)) { + msg <- glue( + "{pipelineName}: ldReference slots{between} must both be genotype ", + "panels or LdMatrix objects for the cross-pipeline LD reference ", + "check." + ) + abort(msg) + } + kinds <- c(.ldRefKind(qtlLd), .ldRefKind(gwasLd)) + if (kinds[[1L]] != kinds[[2L]]) { msg <- glue( - "{pipelineName}: ldSketch slots{between} must both be genotype ", - "panels for the cross-pipeline LD reference check." + "{pipelineName}: one side's ldReference is {kinds[[1L]]} and ", + "the other's {kinds[[2L]]}{between}; use the same LD reference ", + "on both." ) abort(msg) } } +# What kind of LD reference `x` is, in words, for messages that compare two. +# @noRd +.ldRefKind <- function(x) { + if (.ldIsMatrixRef(x)) { + return("an LdMatrix") + } + if (.ldIsMixtureRef(x)) { + return("a GenotypeMixture") + } + "a genotype panel" +} + # The (chrom, position, allele-pair) key the two panels are compared on. The # allele pair is order-insensitive, so an A1/A2 swap keys to the SAME variant # and is then reported as an allele-coding difference rather than looking like # two unrelated variants that happen not to overlap. A panel carrying no allele # columns falls back to chrom:position, which is all it can be keyed on. # @noRd -.ldSketchVariantKeys <- function(x) { - gr <- .ldSketchRanges(x) +.ldRefVariantKeys <- function(x) { + gr <- .ldRefRanges(x) mc <- S4Vectors::mcols(gr) stem <- str_c( canonChrom(as.character(GenomicRanges::seqnames(gr))), @@ -1717,8 +1762,8 @@ loadLdFromGenotype <- function( # The panel's A1 (effect / counted) allele, which fixes the sign of every # correlation the variant takes part in. # @noRd -.ldSketchA1 <- function(x) { - as.character(S4Vectors::mcols(.ldSketchRanges(x))$A1) +.ldRefA1 <- function(x) { + as.character(S4Vectors::mcols(.ldRefRanges(x))$A1) } # A shared variant whose A1/A2 are swapped between the panels is the same @@ -1726,7 +1771,7 @@ loadLdFromGenotype <- function( # in sign wherever it appears. That is a different reference, not a trimming # difference, and it is an error. # @noRd -.ldSketchCheckAlleleCoding <- function( +.ldRefCheckAlleleCoding <- function( qtlLd, gwasLd, qIdx, @@ -1734,18 +1779,18 @@ loadLdFromGenotype <- function( pipelineName, between ) { - qA1 <- .ldSketchA1(qtlLd)[qIdx] - gA1 <- .ldSketchA1(gwasLd)[gIdx] + qA1 <- .ldRefA1(qtlLd)[qIdx] + gA1 <- .ldRefA1(gwasLd)[gIdx] if (length(qA1) == 0L || length(gA1) == 0L) { return(invisible(NULL)) } nSwapped <- sum(qA1 != gA1, na.rm = TRUE) if (nSwapped > 0L) { msg <- glue( - "{pipelineName}: {nSwapped} variant(s) present in both ldSketch ", - "panels carry swapped A1/A2 alleles{between}, so the two panels ", - "code their LD in opposite directions; use the same ldSketch on ", - "both." + "{pipelineName}: {nSwapped} variant(s) present in both ", + "ldReference panels carry swapped A1/A2 alleles{between}, so the ", + "two panels code their LD in opposite directions; use the same ", + "ldReference on both." ) abort(msg) } @@ -1756,20 +1801,21 @@ loadLdFromGenotype <- function( # so it is reported once per session rather than on every call: ctwasPipeline # and colocboostPipeline run this check once per region / per bundle. # @noRd -.ldSketchReportOverlap <- function(nQ, nG, nShared, pipelineName, between) { +.ldRefReportOverlap <- function(nQ, nG, nShared, pipelineName, between) { if (nShared == nQ && nShared == nG) { return(invisible(NULL)) } msg <- glue( - "{pipelineName}: the two ldSketch panels share {nShared} variant(s) ", - "of {nQ} (QTL side) and {nG} (GWAS side){between}. LD is looked up ", - "per variant, so only the shared ones contribute. Differing variant ", - "sets are expected when the two sides were QC'd separately." + "{pipelineName}: the two ldReference panels share {nShared} ", + "variant(s) of {nQ} (QTL side) and {nG} (GWAS side){between}. LD is ", + "looked up per variant, so only the shared ones contribute. ", + "Differing variant sets are expected when the two sides were QC'd ", + "separately." ) warn( msg, .frequency = "once", - .frequency_id = str_c("pecotmrLdSketchOverlap-", pipelineName) + .frequency_id = str_c("pecotmrLdReferenceOverlap-", pipelineName) ) invisible(NULL) } @@ -1778,9 +1824,9 @@ loadLdFromGenotype <- function( # fails is no overlap at all (two unrelated panels) or a shared variant whose # alleles are swapped. # @noRd -.ldSketchCheckOverlap <- function(qtlLd, gwasLd, pipelineName, between) { - qKey <- .ldSketchVariantKeys(qtlLd) - gKey <- .ldSketchVariantKeys(gwasLd) +.ldRefCheckOverlap <- function(qtlLd, gwasLd, pipelineName, between) { + qKey <- .ldRefVariantKeys(qtlLd) + gKey <- .ldRefVariantKeys(gwasLd) # An emptied panel -- what a zero-variant object carries, since it # references no LD -- contradicts nothing, so there is nothing to compare. if (length(qKey) == 0L || length(gKey) == 0L) { @@ -1790,13 +1836,13 @@ loadLdFromGenotype <- function( shared <- which(!is.na(idx)) if (length(shared) == 0L) { msg <- glue( - "{pipelineName}: the two ldSketch panels share no variant", + "{pipelineName}: the two ldReference panels share no variant", "{between} ({length(qKey)} vs {length(gKey)} variants); they ", "describe different LD references." ) abort(msg) } - .ldSketchCheckAlleleCoding( + .ldRefCheckAlleleCoding( qtlLd, gwasLd, shared, @@ -1804,248 +1850,118 @@ loadLdFromGenotype <- function( pipelineName, between ) - .ldSketchReportOverlap( + .ldRefReportOverlap( length(qKey), length(gKey), length(shared), pipelineName, between ) + invisible(list(qIdx = shared, gIdx = idx[shared])) } -# Panels must describe the same reference panel: the same samples, and the same -# allele coding on the variants they share. Their variant SETS need not agree -# -- see `.ldSketchCheckShape` for why. -.ldSketchCheckContent <- function(qtlLd, gwasLd, pipelineName, between) { - qIds <- .ldSketchSampleIds(qtlLd) - gIds <- .ldSketchSampleIds(gwasLd) - if (!identical(qIds, gIds)) { +# Two LdMatrix objects describe the same LD reference when they agree on the +# LD among the variants they share -- what two QC'd subsets of one matrix do, +# and two unrelated matrices do not. Checked on an evenly spaced subset of the +# shared variants, as validity checks symmetry, so the cost does not grow with +# the overlap. +# @noRd +.ldRefCheckSameLd <- function(qtlLd, gwasLd, shared, pipelineName, between) { + if (is.null(shared)) { + return(invisible(NULL)) + } + if (!.ldmAgreesOn(qtlLd, gwasLd, shared$qIdx, shared$gIdx)) { msg <- glue( - "{pipelineName}: ldSketch panels have different sample sets", - "{between}; use the same ldSketch on both." + "{pipelineName}: the two LdMatrix references disagree on the LD ", + "among the variants they share{between}; they describe different ", + "LD references." ) abort(msg) } - .ldSketchCheckOverlap(qtlLd, gwasLd, pipelineName, between) -} - -.requireMatchingLdSketches <- function( - qtlLd, - gwasLd, - pipelineName, - label = NULL, - nullPolicy = c("qtl-required", "lenient") -) { - nullPolicy <- arg_match(nullPolicy) - if (.ldSketchNullGuard(qtlLd, gwasLd, pipelineName, label, nullPolicy)) { - return(invisible(NULL)) - } - between <- if (!is.null(label)) { - glue(" between `{label}` and gwas inputs", .trim = FALSE) - } else { - "" - } - .ldSketchCheckShape(qtlLd, gwasLd, pipelineName, between) - .ldSketchCheckContent(qtlLd, gwasLd, pipelineName, between) invisible(NULL) } -# ---------- LD sketch: genotype loading ---------- - -#' HWE-based standardization of a genotype matrix -#' -#' Centers by 2*alleleFreq, scales by sqrt(2*alleleFreq*(1-alleleFreq)). Assumes -#' monomorphic variants have already been removed. -#' -#' @param X Numeric genotype matrix (n x p). -#' @param alleleFreq Numeric vector of allele frequencies (length p). -#' @return Standardized matrix (n x p). -#' @noRd -standardizeGenotypeHwe <- function(X, alleleFreq) { - Xstd <- sweep(X, 2, 2 * alleleFreq) - sweep(Xstd, 2, sqrt(2 * alleleFreq * (1 - alleleFreq)), "/") -} - - -# ---------- Internal: load LD from pre-computed blocks ---------- - -#' Load pre-computed LD from block-based metadata files. -#' @importFrom purrr map_chr map_int map_dbl -#' @noRd -# --- loadLdFromBlocks helpers ----------------------------------------------- - -# One block's record: the region-extracted matrix, its variants, and the -# chromosome it sits on (falling back to the requested region's when the -# block contributed nothing). -# -# A RECORD rather than a position in three parallel lists: the filter and the -# metadata builder below would otherwise have to keep four sequences in the -# same order by hand, and a slip there would silently attach one block's -# chromosome to another block's variants. -# @noRd -.loadLdOneBlock <- function( - j, - ldFilePaths, - bimFilePaths, - intersectedLdFiles, - extractCoordinates -) { - proc <- processLdMatrix(ldFilePaths[[j]], bimFilePaths[[j]]) - extracted <- extractLdForRegion( - ldMatrix = proc$ldMatrix, - variants = proc$ldVariants, - region = intersectedLdFiles$region, - extractCoordinates = extractCoordinates - ) - blockVariants <- extracted$extractedLdVariants - list( - matrix = extracted$extractedLdMatrix, - variants = blockVariants, - chrom = if (nrow(blockVariants) > 0) { - as.character(blockVariants$chrom[[1L]]) - } else { - as.character(intersectedLdFiles$region$chrom) - } - ) -} - -# Load + region-extract each LD block, one record per block. -.loadLdBlocksLoop <- function( - ldFilePaths, - bimFilePaths, - intersectedLdFiles, - extractCoordinates -) { - map( - seq_along(ldFilePaths), - .loadLdOneBlock, - ldFilePaths = ldFilePaths, - bimFilePaths = bimFilePaths, - intersectedLdFiles = intersectedLdFiles, - extractCoordinates = extractCoordinates - ) -} - -# Drop blocks with no variants in the region (error if none remain). -.loadLdFilterEmpty <- function(blocks) { - nonEmpty <- map_lgl(map(blocks, "variants"), .ldBlockHasVariants) - if (!any(nonEmpty)) { - abort("No variants found in any LD block for the specified region.") - } - if (any(!nonEmpty)) { - nEmpty <- sum(!nonEmpty) +# Two mixtures describe the same LD reference when they average the same +# panels (by their samples) with the same weights. +# @noRd +.ldRefCheckSameMixture <- function(qtlLd, gwasLd, pipelineName, between) { + if (!.gmSameMixture(qtlLd, gwasLd)) { msg <- glue( - "Removing {nEmpty} empty LD block(s) with no variants in ", - "the region." + "{pipelineName}: the two GenotypeMixture references{between} ", + "average different panels or weight them differently; use the ", + "same LD reference on both." ) - inform(msg) + abort(msg) } - blocks[nonEmpty] -} - -# Per-block metadata (id, chrom, span, size, index range in the merged matrix). -.loadLdBlockMetadata <- function(blocks, ldVariants) { - variants <- map(blocks, "variants") - blockVariants <- map(variants, "variants") - blockPositions <- map(variants, "pos") - tibble( - blockId = seq_along(blocks), - chrom = map_chr(blocks, "chrom"), - blockStart = map_dbl(blockPositions, min), - blockEnd = map_dbl(blockPositions, max), - size = map_int(blockVariants, length), - startIdx = map_int( - blockVariants, - .ldBlockStartIdx, - ldVariants = ldVariants - ), - endIdx = map_int( - blockVariants, - .ldBlockEndIdx, - ldVariants = ldVariants - ) - ) + invisible(NULL) } -# Build the reference panel: variant ids + merged per-variant annotations, -# deriving variance from nSample + allele_freq when it is otherwise absent. -# One annotation column aligned to the panel's variant order. +# TRUE when two LdMatrix objects hold the same LD among the variants at +# `xIdx` in one and `yIdx` in the other (matched pairs, same allele coding). +# Compared on an evenly spaced subset, so the cost does not grow with the +# overlap. # @noRd -.ldAnnotationColumn <- function(col, mergedVariantList, ids) { - mergedVariantList[[col]][match(ids, mergedVariantList$variants)] +.ldmAgreesOn <- function(x, y, xIdx, yIdx, tol = 1e-6) { + n <- length(xIdx) + if (n == 0L) { + return(TRUE) + } + probe <- unique(round(seq(1, n, length.out = min(n, 200L)))) + xR <- ldMatrix(x)[xIdx[probe], xIdx[probe], drop = FALSE] + yR <- ldMatrix(y)[yIdx[probe], yIdx[probe], drop = FALSE] + max(abs(xR - yR)) <= tol } -.loadLdRefPanel <- function(ldMatrix, extractedLdVariantsList, nSample) { - ids <- rownames(ldMatrix) - mergedVariantList <- bind_rows(extractedLdVariantsList) - annotations <- intersect( - c("allele_freq", "variance", "n_nomiss"), - colnames(mergedVariantList) - ) - panel <- mutate( - parseVariantId(ids), - variant_id = ids, - !!!set_names( - map( - annotations, - .ldAnnotationColumn, - mergedVariantList = mergedVariantList, - ids = ids - ), - annotations +# Panels must describe the same reference panel: the same samples, and the same +# allele coding on the variants they share. Two LdMatrix objects must agree on +# the LD they share instead, having no samples to compare. Their variant SETS +# need not agree -- see `.ldRefCheckShape` for why. +.ldRefCheckContent <- function(qtlLd, gwasLd, pipelineName, between) { + if (.ldIsMatrixRef(qtlLd)) { + shared <- .ldRefCheckOverlap(qtlLd, gwasLd, pipelineName, between) + return( + .ldRefCheckSameLd(qtlLd, gwasLd, shared, pipelineName, between) ) - ) - needVar <- !is_in("variance", colnames(panel)) || all(is.na(panel$variance)) - if ( - is.null(nSample) || - !needVar || - !is_in("allele_freq", colnames(panel)) - ) { - return(panel) } - p <- panel$allele_freq - mutate( - panel, - variance = 2 * p * (1 - p) * nSample / (nSample - 1), - n_nomiss = nSample - ) + if (.ldIsMixtureRef(qtlLd)) { + .ldRefCheckSameMixture(qtlLd, gwasLd, pipelineName, between) + return(.ldRefCheckOverlap(qtlLd, gwasLd, pipelineName, between)) + } + qIds <- .ldRefSampleIds(qtlLd) + gIds <- .ldRefSampleIds(gwasLd) + if (!identical(qIds, gIds)) { + msg <- glue( + "{pipelineName}: ldReference panels have different sample sets", + "{between}; use the same ldReference on both." + ) + abort(msg) + } + .ldRefCheckOverlap(qtlLd, gwasLd, pipelineName, between) } -loadLdFromBlocks <- function( - ldMetaFilePath, - region, - extractCoordinates = NULL, - nSample = NULL +.requireMatchingLdRefs <- function( + qtlLd, + gwasLd, + pipelineName, + label = NULL, + nullPolicy = c("qtl-required", "lenient") ) { - intersectedLdFiles <- regionalLdMeta(ldMetaFilePath, region) - ldFilePaths <- intersectedLdFiles$intersections$LD_file_paths - bimFilePaths <- intersectedLdFiles$intersections$bimFilePaths - blocks <- .loadLdBlocksLoop( - ldFilePaths, - bimFilePaths, - intersectedLdFiles, - extractCoordinates - ) - kept <- .loadLdFilterEmpty(blocks) - keptVariants <- map(kept, "variants") - ldMatrix <- createLdMatrix( - ldMatrices = map(kept, "matrix"), - variants = keptVariants - ) - ldVariants <- rownames(ldMatrix) - blockMetadata <- .loadLdBlockMetadata(kept, ldVariants) - refPanel <- .loadLdRefPanel(ldMatrix, keptVariants, nSample) - variantsGr <- .refPanelToGranges(refPanel) - LdData( - correlation = ldMatrix, - genotypeHandle = NULL, - snpIdx = NULL, - variants = variantsGr, - blockMetadata = blockMetadata, - nSamples = if (is.null(nSample)) 0L else as.integer(nSample) - ) + nullPolicy <- arg_match(nullPolicy) + if (.ldRefNullGuard(qtlLd, gwasLd, pipelineName, label, nullPolicy)) { + return(invisible(NULL)) + } + between <- if (!is.null(label)) { + glue(" between `{label}` and gwas inputs", .trim = FALSE) + } else { + "" + } + .ldRefCheckShape(qtlLd, gwasLd, pipelineName, between) + .ldRefCheckContent(qtlLd, gwasLd, pipelineName, between) + invisible(NULL) } +# ---------- Variant lookups against an LD reference on disk ---------- + #' Filter variants by LD Reference #' #' Filters a vector of variant IDs to those present in the LD reference panel. @@ -2108,390 +2024,7 @@ filterVariantsByLdReference <- function( list(data = variantIds[keepIndices], idx = keepIndices) } -#' Partition LD Matrix into Block-Specific Matrices -#' -#' This function takes the output from loadLdMatrix and partitions the combined -#' LD matrix into a list of smaller matrices based on the block_indices, making -#' it easier to work with large LD matrices that span multiple blocks. -#' -#' @param ldData An \code{LdData} S4 object as returned by -#' \code{loadLdMatrix()}. -#' @param mergeSmallBlocks Logical, whether to merge blocks smaller than -#' minMergedBlockSize (default: TRUE). -#' @param minMergedBlockSize Integer, minimum number of variants for a block -#' after merging (default: 500). -#' @param maxMergedBlockSize Integer, maximum number of variants in a block -#' after merging (default: 10000). -#' -#' @return returns a list containing: -#' \describe{ -#' \item{ldMatrices}{A list of matrices, each representing LD for a specific -#' block.} -#' \item{variantIndices}{A data frame that maps variant IDs to their -#' corresponding block.} -#' \item{blockMetadata}{Information about each block including size, -#' chromosome, start and end positions.} -#' } -#' @noRd -# --- partitionLdMatrix helpers ---------------------------------------------- - -# Reject empty/NULL matrices; align row/col names to the variant ids. -.partitionValidateMatrix <- function(combinedMatrix, variantIds) { - if ( - is.null(combinedMatrix) || - nrow(combinedMatrix) == 0 || - ncol(combinedMatrix) == 0 - ) { - abort("Empty or NULL LD matrix provided.") - } - wanted <- list(variantIds, variantIds) - if (!identical(dimnames(combinedMatrix), wanted)) { - return(`dimnames<-`(combinedMatrix, wanted)) - } - combinedMatrix -} - -# Drop blocks with invalid/out-of-range indices; renumber the survivors. -.partitionFilterBlocks <- function(blockMetadata, nVariants) { - validBlocks <- map_lgl( - seq_along(blockMetadata), - .ldBlockValid, - blockMetadata = blockMetadata, - nVariants = nVariants - ) - if (!any(validBlocks)) { - msg <- glue( - "No valid LD blocks found. All block indices are out of ", - "range or empty." - ) - abort(msg) - } - if (any(!validBlocks)) { - nInvalid <- sum(!validBlocks) - msg <- glue( - "Removing {nInvalid} LD block(s) with invalid or ", - "out-of-range indices." - ) - inform(msg) - kept <- blockMetadata[validBlocks] - kept$blockId <- seq_along(kept) - return(kept) - } - blockMetadata -} - -#' @importFrom checkmate assertClass -partitionLdMatrix <- function( - ldData, - mergeSmallBlocks = TRUE, - minMergedBlockSize = 500, - maxMergedBlockSize = 10000 -) { - assertClass(ldData, "LdData") - variantIds <- variantIds(ldData) - combinedMatrix <- .partitionValidateMatrix( - ldMatrix(ldData), - variantIds - ) - # The slot is always a GRanges now, so no flattening: every consumer - # below reads it as one (mcols via `$`, blocks via `[` and length()). - blocks <- .partitionFilterBlocks( - blockMetadata(ldData), - length(variantIds) - ) - # Validate the block structure of the matrix (skip if only one block). - if (length(blocks) > 1) { - validateBlockStructure(combinedMatrix, blocks, variantIds) - } - merging <- mergeSmallBlocks && - any(blocks$size < minMergedBlockSize) && - length(blocks) > 1 - blockMetadata <- if (merging) { - mergeBlocks(blocks, minMergedBlockSize, maxMergedBlockSize) - } else { - blocks - } - extractBlockMatrices(combinedMatrix, blockMetadata, variantIds) -} - -# Blocks must not overlap. The merge logic only ever considers i - 1 and -# i + 1, so a block's position in the table has to mean its position on the -# chromosome; that held by convention -- the loaders sort by position -- but -# nothing checked it, and a violation would silently merge non-neighbours. -# As a GRanges it is one call. Width-0 blocks are skipped: they mark an -# out-of-range index range that .partitionFilterBlocks() drops anyway. -# @noRd -.ldBlockOrderAssert <- function(blockMetadata) { - spanned <- blockMetadata[GenomicRanges::width(blockMetadata) > 0L] - if (length(spanned) < 2L || GenomicRanges::isDisjoint(spanned)) { - return(invisible(NULL)) - } - abort( - "LD blocks overlap: each block must cover a span of its own." - ) -} - -# Every (i, j) block pair with i < j. -# @noRd -.ldUpperPairs <- function(nBlocks) { - grid <- expand.grid(i = seq_len(nBlocks), j = seq_len(nBlocks)) - grid[grid$i < grid$j, , drop = FALSE] -} - -# @noRd -.blockPairMessagesAt <- function( - k, - pairs, - blockMetadata, - matrix, - variantIds, - n -) { - .blockPairMessages( - pairs$i[[k]], - pairs$j[[k]], - blockMetadata, - matrix, - variantIds, - n - ) -} - -# Concatenate per-item message vectors, empty-safe. -# @noRd -.ldConcatChr <- function(pieces) { - if (length(pieces) == 0L) { - return(character(0)) - } - as.character(list_c(pieces)) -} - -#' Validate that cross-block entries are zero (excluding boundary variants). -#' @noRd -validateBlockStructure <- function(matrix, blockMetadata, variantIds) { - .ldBlockOrderAssert(blockMetadata) - n <- length(variantIds) - pairs <- .ldUpperPairs(length(blockMetadata)) - msgs <- .ldConcatChr(map( - seq_len(nrow(pairs)), - .blockPairMessagesAt, - pairs = pairs, - blockMetadata = blockMetadata, - matrix = matrix, - variantIds = variantIds, - n = n - )) - if (length(msgs) > 0) { - msgList <- str_flatten(msgs, collapse = "\n") - msg <- glue( - "Matrix lacks expected block structure:\n{msgList}", - .trim = FALSE - ) - abort(msg) - } -} - -# Overlap-consistency messages for one block pair (i, j): out-of-range indices, -# or non-zero cross-block correlation with boundary variants excluded. Returns a -# (possibly empty) character vector. -# @noRd -.blockPairMessages <- function(i, j, blockMetadata, matrix, variantIds, n) { - si <- blockMetadata$startIdx[i] - ei <- blockMetadata$endIdx[i] - sj <- blockMetadata$startIdx[j] - ej <- blockMetadata$endIdx[j] - if (si > n || ei > n || sj > n || ej > n) { - return(str_c( - "Block indices out of range for blocks", - i, - "and", - j, - sep = " " - )) - } - # Exclude boundary variants (potential overlaps) - vi <- variantIds[si:(ei - 1)] - vj <- variantIds[(sj + 1):ej] - maxVal <- max(abs(matrix[vi, vj, drop = FALSE])) - if (maxVal <= 1e-10) { - return(character(0)) - } - str_c( - "Non-zero correlation between blocks", - i, - "and", - j, - "- max:", - maxVal, - sep = " " - ) -} - -#' Can blocks `i` and `j` of `blockMetadata` merge (same chrom, combined size -#' within `maxSize`)? Indexes the columns directly rather than slicing rows. -#' @noRd -canMerge <- function(blockMetadata, i, j, maxSize) { - chrom <- as.character(GenomicRanges::seqnames(blockMetadata)) - chrom[i] == chrom[j] && - (blockMetadata$size[i] + blockMetadata$size[j]) <= maxSize -} - -#' @noRd -mergeTwoBlocks <- function(blockMetadata, idx1, idx2) { - if (idx1 > idx2) { - tmp <- idx1 - idx1 <- idx2 - idx2 <- tmp - } - merged <- blockMetadata - merged$endIdx[idx1] <- blockMetadata$endIdx[idx2] - merged$size[idx1] <- blockMetadata$size[idx1] + - blockMetadata$size[idx2] - # The merged block spans both, so its RANGE has to grow with its index - # range. While this was a data.frame the two could drift apart unnoticed, - # because nothing read blockStart / blockEnd. - GenomicRanges::end(merged)[idx1] <- - GenomicRanges::end(blockMetadata)[idx2] - merged <- merged[-idx2] - merged$blockId <- seq_along(merged) - merged -} - -#' Find blocks below minSize and identify the best neighbor to merge with. -#' @noRd -findMergeCandidates <- function(blockMetadata, minSize, maxSize) { - found <- compact(map( - seq_along(blockMetadata), - .ldMergeCandidateFor, - blockMetadata = blockMetadata, - minSize = minSize, - maxSize = maxSize - )) - if (length(found) == 0L) { - return(tibble(block_idx = integer(), merge_with = integer())) - } - bind_rows(found) -} - -# Which neighbour block `i` should merge into (the smaller one when both -# qualify), or NULL when it is big enough already or neither neighbour can -# take it. -# @noRd -.ldMergeCandidateFor <- function(i, blockMetadata, minSize, maxSize) { - if (blockMetadata$size[i] >= minSize) { - return(NULL) - } - prevOk <- i > 1 && canMerge(blockMetadata, i, i - 1, maxSize) - nextOk <- i < length(blockMetadata) && - canMerge(blockMetadata, i, i + 1, maxSize) - mergeWith <- if (prevOk && nextOk) { - if (blockMetadata$size[i - 1] <= blockMetadata$size[i + 1]) { - i - 1 - } else { - i + 1 - } - } else if (prevOk) { - i - 1 - } else if (nextOk) { - i + 1 - } else { - return(NULL) - } - tibble(block_idx = i, merge_with = mergeWith) -} - -#' Iteratively merge blocks below minSize with their smallest neighbor. -#' @noRd -mergeBlocks <- function(blockMetadata, minSize, maxSize) { - if (length(blockMetadata) <= 1) { - return(blockMetadata) - } - candidates <- findMergeCandidates(blockMetadata, minSize, maxSize) - if (nrow(candidates) == 0) { - return(blockMetadata) - } - # Each merge changes which blocks are still too small, so this is a fixed - # point: recurse on the merged metadata rather than rebinding it. - mergeBlocks( - mergeTwoBlocks( - blockMetadata, - candidates$block_idx[1], - candidates$merge_with[1] - ), - minSize, - maxSize - ) -} - -# Helper function to extract block matrices -# Extract one block's submatrix + mapping (NULL to skip empty/OOB blocks). -.extractOneBlock <- function(matrix, variantIds, startIdx, endIdx, i) { - if (endIdx < startIdx) { - return(NULL) - } - if (startIdx > length(variantIds) || endIdx > length(variantIds)) { - msg <- glue( - "Block {i} has indices outside the range of variantIds. ", - "Skipping." - ) - warn(msg) - return(NULL) - } - blockVariants <- variantIds[startIdx:endIdx] - list( - matrix = matrix[blockVariants, blockVariants, drop = FALSE], - mapping = tibble( - variant_id = blockVariants, - blockId = i - ) - ) -} - -# @noRd -.ldExtractBlockAt <- function(i, matrix, variantIds, blockMetadata) { - .extractOneBlock( - matrix, - variantIds, - blockMetadata$startIdx[i], - blockMetadata$endIdx[i], - i - ) -} - -# `x[[i]] <- v` in a loop never extends past the last assigned position, so -# trailing skipped blocks leave no entry at all. -# @noRd -.ldTrimTrailingNull <- function(xs) { - filled <- which(!map_lgl(xs, is.null)) - if (length(filled) == 0L) { - return(list()) - } - xs[seq_len(max(filled))] -} - -extractBlockMatrices <- function(matrix, blockMetadata, variantIds) { - blocks <- map( - seq_along(blockMetadata), - .ldExtractBlockAt, - matrix = matrix, - variantIds = variantIds, - blockMetadata = blockMetadata - ) - kept <- compact(blocks) - mappings <- map(kept, "mapping") - list( - # A skipped block leaves a hole, as `ldMatrices[[i]] <- ...` did: - # positions stay aligned with blockMetadata rows. - ldMatrices = .ldTrimTrailingNull(map(blocks, "matrix")), - variantIndices = if (length(mappings) == 0L) { - tibble(variant_id = character(), blockId = integer()) - } else { - bind_rows(mappings) - }, - blockMetadata = blockMetadata - ) -} - +# ---------- LD matrix checks and repair ---------- # The PSD repair `method` asks for, or the matrix unchanged when it is # already positive definite. Returns list(R, methodApplied). @@ -2610,19 +2143,18 @@ checkLd <- function( ) } -# hclust (single-linkage) LD pruning: keep one representative per |cor| cluster. +# Single-linkage LD pruning: keep one representative per |cor| cluster. +# Single-linkage clusters cut at 1 - corThres are the connected components +# of the graph joining columns with |cor| >= corThres, so they are found from +# the columns' correlations a stripe of rows at a time +# (ldThresholdComponents()), never the full matrix. .ldPruneHclust <- function(X, corThres, verbose) { - # Engine-call exception: Rfast::cora is a fast drop-in for base cor(), a - # numeric primitive rather than an analysis engine. p <- ncol(X) - if (requireNamespace("Rfast", quietly = TRUE)) { - cor.X <- Rfast::cora(X, large = TRUE) - } else { - cor.X <- cor(X) - } - Sigma.distance <- as.dist(1 - abs(cor.X)) - fit <- hclust(Sigma.distance, method = "single") - clusters <- cutree(fit, h = 1 - corThres) + clusters <- ldThresholdComponents( + .ldStandardize(X), + corThres, + .ldStripeSize() + ) # Keep the first member of each cluster and drop the rest -- which is # exactly the entries that repeat a cluster already seen. ind.delete <- which(duplicated(clusters)) @@ -2695,9 +2227,12 @@ LdPruningOptions <- function(...) { #' Prune columns by pairwise correlation (LD-style prune) #' #' Performs LD pruning using one of two backends. The default \code{"hclust"} -#' backend computes the full correlation matrix, builds a single-linkage -#' hierarchical clustering on the distance (1 - |cor|), and keeps one -#' representative column per cluster. The \code{"snprelate"} backend delegates +#' backend groups the columns as single-linkage hierarchical clustering on the +#' distance (1 - |cor|), cut at 1 - \code{corThres}, would, and keeps the +#' first column of each group. Those groups are the connected components +#' of the graph joining columns with |cor| >= \code{corThres}, found from the +#' correlations a stripe of rows at a time, so the full correlation matrix is +#' never formed. The \code{"snprelate"} backend delegates #' to \code{SNPRelate::snpgdsLDpruning}, which performs a sliding-window greedy #' prune directly on a temporary GDS file. #' @@ -2709,11 +2244,13 @@ LdPruningOptions <- function(...) { #' @param backend Character, one of \code{"hclust"} (default) or #' \code{"snprelate"}. Controls the pruning algorithm: #' \describe{ -#' \item{\code{"hclust"}}{Uses the internal hierarchical-clustering approach -#' with \code{Rfast::cora} (if available) or base \code{cor()}.} +#' \item{\code{"hclust"}}{Single-linkage groups, found as the connected +#' components of the |cor| >= \code{corThres} graph.} #' \item{\code{"snprelate"}}{Requires \pkg{SNPRelate} and \pkg{gdsfmt}. #' Creates a temporary GDS file and runs -#' \code{SNPRelate::snpgdsLDpruning(method = "corr")}.} +#' \code{SNPRelate::snpgdsLDpruning(method = "corr")}. GDS stores +#' genotypes as the integer codes 0, 1 and 2, so fractional dosages are +#' refused rather than rounded.} #' } #' @param verbose Logical. If TRUE, print progress messages. Default FALSE. #' @param methodArgs Extra arguments for @@ -2737,7 +2274,7 @@ LdPruningOptions <- function(...) { #' res <- ldPruneByCorrelation(X, corThres = 0.9) #' ncol(res$X.new) #' -#' @importFrom stats as.dist hclust cutree cor +#' @importFrom stats cor #' @export ldPruneByCorrelation <- function( X, @@ -2782,9 +2319,9 @@ ldPruneByCorrelation <- function( } } -# Write X (rounded to integer genotype codes) to a temporary GDS for SNPRelate. +# Write X, integer genotype codes, to a temporary GDS for SNPRelate. .ldPruneSnprelateCreateGds <- function(tmpGds, X, snpNames, p) { - genoInt <- `storage.mode<-`(round(X), "integer") + genoInt <- `storage.mode<-`(X, "integer") SNPRelate::snpgdsCreateGeno( gds.fn = tmpGds, genmat = t(genoInt), @@ -2799,6 +2336,7 @@ ldPruneByCorrelation <- function( .ldPruneSnprelate <- function(X, corThres, verbose, methodArgs) { .ldPruneSnprelateDeps() + .ldRequireIntegerGenotypes(X, "snprelate", "hclust") p <- ncol(X) snpNames <- colnames(X) %||% str_c("snp", seq_len(p)) tmpGds <- tempfile(fileext = ".gds") @@ -2827,43 +2365,8 @@ ldPruneByCorrelation <- function( list(X.new = X.new, filter.id = keepIds) } -#' Drop collinear columns from a design matrix by a chosen strategy -#' -#' Given a numeric matrix \code{X} and a set of column names known to be -#' involved in linear dependencies, remove one column using one of three -#' strategies. Designed to be called iteratively by -#' \code{\link{enforceDesignFullRank}}, but can be used standalone. -#' -#' @param X Numeric matrix. Must have column names covering -#' \code{problematicCols}. -#' @param problematicCols Character vector of column names in \code{X} that are -#' candidates for removal. If empty, \code{X} is returned unchanged. -#' @param strategy One of \code{"correlation"} (remove the column with the -#' largest sum of absolute pairwise correlations among the candidates; when -#' only two candidates, one is picked at random), \code{"variance"} (remove -#' the lowest-variance candidate), or \code{"responseCorrelation"} (remove -#' the candidate whose correlation with \code{response} has the smallest -#' magnitude). -#' @param response Numeric vector required when \code{strategy = -#' "responseCorrelation"}; the outcome to correlate against. -#' @param verbose Logical. If TRUE, print which column was removed. Default -#' FALSE. -#' -#' @return \code{X} with exactly one column removed (or unchanged if -#' \code{problematicCols} is empty). -#' -#' @examples -#' set.seed(1) -#' X <- matrix(rnorm(100 * 3), 100, 3) -#' X[, 3] <- X[, 1] + X[, 2] -#' colnames(X) <- c("a", "b", "c") -#' dropCollinearColumns(X, problematicCols = c("a", "b", "c"), -#' strategy = "variance") -#' -#' @importFrom stats var cor -#' @keywords internal -#' @noRd # Correlation strategy: drop the most-connected column (random tie-break at 2). +# @noRd .dropCollinearPickCor <- function(X, problematicCols, verbose, seed = NULL) { corMatrix <- `diag<-`( abs(cor(X[, problematicCols, drop = FALSE])), @@ -2937,6 +2440,42 @@ ldPruneByCorrelation <- function( colToRemove } +#' Drop collinear columns from a design matrix by a chosen strategy +#' +#' Given a numeric matrix \code{X} and a set of column names known to be +#' involved in linear dependencies, remove one column using one of three +#' strategies. Designed to be called iteratively by +#' \code{\link{enforceDesignFullRank}}, but can be used standalone. +#' +#' @param X Numeric matrix. Must have column names covering +#' \code{problematicCols}. +#' @param problematicCols Character vector of column names in \code{X} that are +#' candidates for removal. If empty, \code{X} is returned unchanged. +#' @param strategy One of \code{"correlation"} (remove the column with the +#' largest sum of absolute pairwise correlations among the candidates; when +#' only two candidates, one is picked at random), \code{"variance"} (remove +#' the lowest-variance candidate), or \code{"responseCorrelation"} (remove +#' the candidate whose correlation with \code{response} has the smallest +#' magnitude). +#' @param response Numeric vector required when \code{strategy = +#' "responseCorrelation"}; the outcome to correlate against. +#' @param verbose Logical. If TRUE, print which column was removed. Default +#' FALSE. +#' +#' @return \code{X} with exactly one column removed (or unchanged if +#' \code{problematicCols} is empty). +#' +#' @examples +#' set.seed(1) +#' X <- matrix(rnorm(100 * 3), 100, 3) +#' X[, 3] <- X[, 1] + X[, 2] +#' colnames(X) <- c("a", "b", "c") +#' dropCollinearColumns(X, problematicCols = c("a", "b", "c"), +#' strategy = "variance") +#' +#' @importFrom stats var cor +#' @keywords internal +#' @noRd dropCollinearColumns <- function( X, problematicCols, @@ -3314,34 +2853,6 @@ ldClumpByScore <- function( keep } - -# ============================================================================= -# Block-wise LD loaders -# ----------------------------------------------------------------------------- -# High-level helpers that sit on top of `loadLdMatrix` / `processLdMatrix` -# to retrieve per-block LD or genotype matrices on demand. Used by -# downstream pipelines (cTWAS, etc.) that need to walk many LD blocks -# without materializing them all in memory at once. -# ============================================================================= - -#' Extract the LD or genotype matrix from an LdData S4 object. -#' @param ld An LdData object. -#' @param wantGenotype Logical; if TRUE, extract the genotype matrix (via -#' \code{genotypes()}). -#' @return A matrix. -#' @importFrom checkmate assertClass -#' @noRd -extractLdMatrix <- function(ld, wantGenotype = FALSE) { - assertClass(ld, "LdData") - if (wantGenotype && hasGenotypes(ld)) { - return(genotypes(ld)) - } - ldMatrix(ld) -} - - -# ---- Per-block helpers ------------------------------------------------------ - # ============================================================================= # LD correlation matrix from a dosage matrix # ----------------------------------------------------------------------------- @@ -3354,10 +2865,10 @@ extractLdMatrix <- function(ld, wantGenotype = FALSE) { # LD for a block of a genotype panel: read the dosages, then correlate them. # -# The guards are the reason this is a function rather than two lines at the -# call site. A backend that cannot read the block returns NULL, and a -# single-variant block has no correlation structure; both mean "identity", -# and open-coding that in every caller is how one of them ends up omitting it. +# The guard is the reason this is a function rather than two lines at the +# call site: a single-variant block has no correlation structure, so its LD +# is the identity, and open-coding that in every caller is how one of them +# ends up omitting it. # @noRd .computeLdFromPanel <- function( panel, @@ -3368,14 +2879,11 @@ extractLdMatrix <- function(ld, wantGenotype = FALSE) { shrinkage ) { idx <- if (is.null(snpIdx)) { - seq_along(.ldSketchRanges(panel)) + seq_along(.ldRefRanges(panel)) } else { snpIdx } - geno <- .ldSketchDosage(panel, idx) - if (is.null(geno)) { - return(diag(length(idx))) - } + geno <- .ldRefDosage(panel, idx) if (ncol(geno) < 2L) { return(diag(length(idx))) } @@ -3425,21 +2933,30 @@ extractLdMatrix <- function(ld, wantGenotype = FALSE) { "already in memory, which is what onDisk exists to avoid." )) } - fmt <- genotypeFormat(.ldSketchHandle(X)) + fmt <- .ldRefFormat(X) %||% "held in memory" if (fmt != "gds") { abort(glue( "computeLd(onDisk = TRUE) needs a GDS-backed panel; this one is ", "'{fmt}'. Only GDS exposes an on-disk LD routine." )) } + if (.withGdsFile(.ldRefReadPath(X), .gdsFileFormat) != "SNP_ARRAY") { + abort(glue( + "computeLd(onDisk = TRUE) reads SNPRelate's SNP GDS files; ", + "{.ldRefReadPath(X)} is a SeqArray GDS file. Use ", + "computeLd(onDisk = FALSE), which reads its dosages." + )) + } idx <- if (is.null(snpIdx)) { - seq_along(.ldSketchRanges(X)) + seq_along(.ldRefRanges(X)) } else { snpIdx } - # The handle stays here on purpose: the GDS on-disk LD routine reads the - # file directly, which is a seed-level operation with no assay equivalent. - R <- .ldCleanCorrelation(.computeBlockLdGds(.ldSketchHandle(X), idx)) + # The GDS on-disk LD routine reads the file directly, which has no assay + # equivalent: it is handed the file and the variants' own ids. + R <- .ldCleanCorrelation( + .computeBlockLdGds(.ldRefReadPath(X), .ldRefVariantIds(X)[idx]) + ) .ldShrinkToIdentity(R, shrinkage) } @@ -3581,7 +3098,10 @@ extractLdMatrix <- function(ld, wantGenotype = FALSE) { #' \code{cor()} without it) and is both the fastest and the default; #' \code{"snprelate"} and \code{"snpstats"} exist to cross-check it and do #' strictly more work, since each converts the same matrix into its own -#' representation first. +#' representation first. Both store genotypes as the integer codes 0, 1 and +#' 2, so they refuse fractional dosages (imputed, or a stochastic sketch's) +#' rather than round them; \code{"internal"} correlates dosages as they +#' are. #' @param X A numeric dosage matrix (samples x variants), or a genotype panel #' -- a \code{RangedSummarizedExperiment} whose dosage assay reads through #' a genotype handle. @@ -3611,7 +3131,7 @@ extractLdMatrix <- function(ld, wantGenotype = FALSE) { #' \code{dimnames} taken from the block where available. #' @examples #' data(qtlSumStatsExample) -#' panel <- ldSketch(qtlSumStatsExample) +#' panel <- ldReference(qtlSumStatsExample) #' R <- computeLd(panel, snpIdx = 1:5) #' dim(R) #' # A dosage matrix already in memory is correlated directly. @@ -3673,6 +3193,28 @@ computeLd <- function( `dimnames<-`(R, list(nms, nms)) } +# SNPRelate (GDS) and snpStats (SnpMatrix) hold a genotype as a 2-bit code, +# so a dosage outside 0, 1 and 2 -- an imputed or projected one -- would be +# rounded, and its LD changed, without a word. They take genotype codes only; +# dosages go to the backend that correlates them as they are. +# @noRd +.ldRequireIntegerGenotypes <- function(X, backend, instead) { + bad <- !is.na(X) & !is_in(X, c(0, 1, 2)) + if (!any(bad)) { + return(invisible(TRUE)) + } + abort(c( + glue( + "backend = \"{backend}\" stores genotypes as the codes 0, 1 and ", + "2, and {sum(bad)} value(s) here are not one of them." + ), + i = glue( + "Rounding them would change their LD. Use backend = ", + "\"{instead}\", which takes dosages as they are." + ) + )) +} + #' Compute LD via SNPRelate (creates a temporary GDS file from the dosage #' matrix). #' @param X Numeric genotype matrix (samples x SNPs). @@ -3686,12 +3228,12 @@ computeLd <- function( abort("Package 'gdsfmt' is required for backend='snprelate'") } + .ldRequireIntegerGenotypes(X, "snprelate", "internal") tmpGds <- tempfile(fileext = ".gds") on.exit(unlink(tmpGds), add = TRUE) - # Round to integer dosage for GDS (0/1/2) - # 3L is the GDS missing code. - X_int <- replace(`storage.mode<-`(round(X), "integer"), is.na(X), 3L) + # Integer genotype codes 0/1/2, with 3L, the GDS missing code, for NA. + X_int <- replace(`storage.mode<-`(X, "integer"), is.na(X), 3L) snpIds <- colnames(X) %||% seq_len(ncol(X)) sampleIds <- rownames(X) %||% seq_len(nrow(X)) @@ -3726,11 +3268,11 @@ computeLd <- function( if (!requireNamespace("snpStats", quietly = TRUE)) { abort("Package 'snpStats' is required for backend='snpstats'") } + .ldRequireIntegerGenotypes(X, "snpstats", "internal") # snpStats expects counts of the B allele as raw codes: 1=AA, 2=AB, 3=BB, # 0=NA pecotmr dosage is ALT count (0/1/2), so map: 0->1, 1->2, 2->3, NA->0 - shifted <- round(X) + 1L - coded <- pmin(replace(shifted, is.na(X) | shifted < 1L, 0L), 3L) + coded <- replace(X + 1L, is.na(X), 0L) sm <- new("SnpMatrix", `storage.mode<-`(coded, "raw")) raw <- as.matrix(snpStats::ld(sm, stats = "R", depth = ncol(X) - 1L)) @@ -3738,49 +3280,6 @@ computeLd <- function( .ldCleanCorrelation(raw) } -# ---- map/apply helpers (lambda-free callbacks) --------------------------- - -# The integer BP position parsed from a "chrom:pos:a1:a2" variant id. -# @noRd -.ldVariantPos <- function(v) { - as.integer(str_split(v, ":")[[1L]][2]) -} - -# TRUE when a per-block variant table has at least one row. -# @noRd -.ldBlockHasVariants <- function(v) { - nrow(v) > 0 -} - -# The first index of a block's variants in the merged variant order. -# @noRd -.ldBlockStartIdx <- function(v, ldVariants) { - min(match(v, ldVariants)) -} - -# The last index of a block's variants in the merged variant order. -# @noRd -.ldBlockEndIdx <- function(v, ldVariants) { - max(match(v, ldVariants)) -} - -# TRUE when block `i`'s index range is present, finite, and within -# [1, nVariants]. -# @noRd -.ldBlockValid <- function(i, blockMetadata, nVariants) { - s <- blockMetadata$startIdx[i] - e <- blockMetadata$endIdx[i] - sz <- blockMetadata$size[i] - !is.na(s) && - !is.na(e) && - is.finite(s) && - is.finite(e) && - sz > 0 && - s >= 1 && - e >= s && - e <= nVariants -} - #' @rdname PanelFilterParam #' @aliases PanelFilterParam-class #' @exportClass PanelFilterParam @@ -3802,8 +3301,9 @@ setClass( #' missingness default is \code{1} (no filter) rather than \code{0}. #' @param mafCutoff Minor-allele-frequency floor. Default \code{0}. #' @param macCutoff Minor-allele-count floor; the stricter of this and -#' \code{mafCutoff} applies, using the panel's own sample count. Default -#' \code{0}. +#' \code{mafCutoff} applies, using the panel's real sample count -- for a +#' stochastic LD sketch, the one its \code{.afreq} records, not its +#' pseudo-samples. Default \code{0}. #' @param imissCutoff Per-variant missingness ceiling. Default \code{1}, which #' filters nothing and lets the allele-frequency sidecar be read instead of #' materializing dosage. diff --git a/R/manifestLoaders.R b/R/manifestLoaders.R index f3f909f74..a2f009024 100644 --- a/R/manifestLoaders.R +++ b/R/manifestLoaders.R @@ -70,10 +70,6 @@ NULL file.path(base, p) } -# Rename accepted aliases to the canonical camelCase name and validate that all -# `required` canonical columns are present. `aliases` is a named list keyed by -# canonical name; each value is the character vector of accepted source names -# (including the canonical name itself). The first alias present wins. # The QtlDataset pass-through arguments, asserted at the loader so a bad value # is reported against the argument the caller passed rather than surfacing from # QtlDataset's validity several hundred lines later. Types match @@ -84,8 +80,7 @@ NULL assertLogical(scaleResiduals, len = 1L) .assertMethodParam( genotypeFilterParam, - "GenotypeFilterParam", - "genotypeFilter" + "GenotypeFilterParam" ) # Only what the caller actually set: an unset field defers to QtlDataset's # own default and has nothing to check here. @@ -138,6 +133,11 @@ NULL hit[[1L]] } +# Rename accepted aliases to the canonical camelCase name and validate that all +# `required` canonical columns are present. `aliases` is a named list keyed by +# canonical name; each value is the character vector of accepted source names +# (including the canonical name itself). The first alias present wins. +# @noRd .canonManifestCols <- function(df, aliases, required, label) { # Each alias resolves against the ORIGINAL names, so renaming one cannot # change what another matches. @@ -204,149 +204,124 @@ NULL # Genotype-format detection + LD-sketch resolution # ============================================================================= -# Build a single-file / prefix GenotypeHandle, detecting the backend from the -# path. Recognised: .vcf[.gz|.bgz]/.bcf/.gds single files, .bed/.pgen single -# files, and bare PLINK1/PLINK2 prefixes (probed by sidecar existence). -.detectGenotypeFormat <- function(path) { - lower <- str_to_lower(path) - if ( - str_detect(lower, "\\.vcf(\\.b?gz)?$") || - str_ends(lower, "\\.bcf") || - str_ends(lower, "\\.gds") - ) { - return(GenotypeHandle(path = path)) - } - if (str_ends(lower, "\\.bed")) { - return(GenotypeHandle( - plink1Prefix = str_remove( - path, - regex("\\.bed$", ignore_case = TRUE) - ) - )) - } - if (str_ends(lower, "\\.pgen")) { - return(GenotypeHandle( - plink2Prefix = str_remove( - path, - regex("\\.pgen$", ignore_case = TRUE) - ) - )) - } - if (file.exists(str_c(path, ".bed"))) { - return(GenotypeHandle(plink1Prefix = path)) - } - if (file.exists(str_c(path, ".pgen"))) { - return(GenotypeHandle(plink2Prefix = path)) - } - msg <- glue( - "Could not determine genotype format for '{path}' ", - "(expected .bed/.pgen/.vcf[.gz]/.bcf/.gds or a PLINK prefix)." +# The genotype panel of a single file or PLINK prefix, its format told as +# readGenotypes() tells it (.genotypePathFormat()). +.readGenotypePath <- function(path) { + .readGenotypePanel( + path, + format = .requireGenotypeFormat(path, "manifest") ) - abort(msg) } -# Turn an ldSketch specification into a GenotypeHandle. Accepts a prebuilt -# handle, a named chrom -> path vector (genoMeta), or a single string that is -# either a genotype file/prefix or a chrom-sharded genoMeta meta file. -# `chroms` (optional) restricts a chrom-sharded panel to those chromosomes so -# the other per-chromosome shards are never read; it is ignored for a single -# genotype file (which has no shards to skip). -.resolveLdSketch <- function(spec, chroms = NULL) { +# Turn an ldReference specification into a genotype panel, or pass an LdMatrix +# or a mixture through. Accepts a panel (or a bare handle), a named chrom -> +# path vector (genoMeta), or a single string that is either a genotype +# file/prefix or a chrom-sharded genoMeta meta file. `chroms` (optional) +# restricts a chrom-sharded panel to those chromosomes so the other +# per-chromosome shards are never read; it is ignored for a single genotype +# file (which has no shards to skip). +.resolveLdRef <- function(spec, chroms = NULL) { if (is.null(spec)) { return(NULL) } - open <- .openGenotypeHandle(spec) + open <- .openLdRef(spec) if (!is.null(open)) { return(open) } if (is.character(spec) && !is.null(names(spec))) { - return(GenotypeHandle(genoMeta = spec, chroms = chroms)) + return(.chromMetaPanel(spec, chroms)) } if (is.character(spec) && length(spec) == 1L) { - lower <- str_to_lower(spec) - looksLikeGenotype <- str_detect(lower, "\\.vcf(\\.b?gz)?$") || - str_ends(lower, "\\.bcf") || - str_ends(lower, "\\.gds") || - str_ends(lower, "\\.bed") || - str_ends(lower, "\\.pgen") || - file.exists(str_c(spec, ".bed")) || - file.exists(str_c(spec, ".pgen")) - if (looksLikeGenotype) { - return(.detectGenotypeFormat(spec)) + if (!is.null(.genotypePathFormat(spec))) { + return(.readGenotypePath(spec)) } # Otherwise treat it as a chrom-sharded genoMeta meta file. - return(GenotypeHandle(genoMeta = spec, chroms = chroms)) + return(.chromMetaPanel(spec, chroms)) } msg <- glue( - "`ldSketch` must be a genotype panel, a genotype path/prefix, or a ", - "genoMeta spec (named chrom->path vector or meta-file path)." + "`ldReference` must be a genotype panel, a genotype path/prefix, a ", + "genoMeta spec (named chrom->path vector or meta-file path), or an ", + "LdMatrix." ) abort(msg) } -# Resolve the LD sketch SPEC from the (argument, ldSketchPath column) pair, -# without reading any genotype metadata yet. Returns the handle behind an -# already-open panel (when the caller passed one), or the reconciled spec +# An LD reference that is already open -- a genotype panel or a bare handle, +# an LdMatrix or a mixture, as it stands -- or NULL for a spec still to be +# resolved. +# @noRd +.openLdRef <- function(x) { + if (.ldIsMatrixRef(x) || .ldIsMixtureRef(x)) { + return(x) + } + .openGenotypeSource(x) +} + +# Resolve the LD sketch SPEC from the (argument, ldReferencePath column) pair, +# without reading any genotype metadata yet. Returns an already-open panel +# (when the caller passed one) as it is, or the reconciled spec # (a path/prefix or a # genoMeta path/named-vector) for later materialisation. Deferring the read to -# `.materializeLdSketch` lets the loader first learn which chromosomes the +# `.materializeLdRef` lets the loader first learn which chromosomes the # summary statistics cover and skip the panel's other per-chromosome shards. -.resolveLdSketchInput <- function(df, ldSketch, base) { - open <- .openGenotypeHandle(ldSketch) +.resolveLdRefInput <- function(df, ldReference, base) { + open <- .openLdRef(ldReference) if (!is.null(open)) { return(open) } - # is_in guard, not `!is.null(df$ldSketchPath)`: on a tibble `$` for an + # is_in guard, not `!is.null(df$ldReferencePath)`: on a tibble `$` for an # absent column NULLs but WARNS ("Unknown or uninitialised column"), where a - # data.frame was silent. ldSketchPath is optional (LD may come via - # `ldSketch`). - colSpec <- if (!is_in("ldSketchPath", names(df))) { + # data.frame was silent. ldReferencePath is optional (LD may come via + # `ldReference`). + colSpec <- if (!is_in("ldReferencePath", names(df))) { NULL } else { - v <- unique(as.character(df$ldSketchPath[ - !is.na(df$ldSketchPath) & - str_length(as.character(df$ldSketchPath)) > 0L + v <- unique(as.character(df$ldReferencePath[ + !is.na(df$ldReferencePath) & + str_length(as.character(df$ldReferencePath)) > 0L ])) if (length(v) > 1L) { - abort("`ldSketchPath` column must be constant across the manifest.") + abort( + "`ldReferencePath` column must be constant across the manifest." + ) } if (length(v) == 1L) .resolveRel(v, base) else NULL } - spec <- ldSketch %||% colSpec + spec <- ldReference %||% colSpec if ( - !is.null(ldSketch) && + !is.null(ldReference) && !is.null(colSpec) && - !identical(as.character(ldSketch), as.character(colSpec)) + !identical(as.character(ldReference), as.character(colSpec)) ) { msg <- glue( - "`ldSketch` argument disagrees with the manifest ", - "`ldSketchPath` column." + "`ldReference` argument disagrees with the manifest ", + "`ldReferencePath` column." ) abort(msg) } if (is.null(spec)) { msg <- glue( - "`ldSketch` must be provided as an argument or an ", - "`ldSketchPath` column." + "`ldReference` must be provided as an argument or an ", + "`ldReferencePath` column." ) abort(msg) } spec } -# Materialise a resolved ldSketch spec into a GenotypeHandle, reading only the -# shards for `chroms` when the spec is a chrom-sharded panel. A spec that is -# already a GenotypeHandle (the caller passed a prebuilt handle) is returned -# unchanged: its snpInfo is already in memory, so there is no read to restrict. -.materializeLdSketch <- function(spec, chroms) { +# Materialise a resolved ldReference spec into a genotype panel, reading only +# the shards for `chroms` when the spec is a chrom-sharded panel. A spec that is +# already open (the caller passed a panel) is returned unchanged: its variants +# are already in memory, so there is no read to restrict. +.materializeLdRef <- function(spec, chroms) { if (is.null(spec)) { return(NULL) } - open <- .openGenotypeHandle(spec) + open <- .openLdRef(spec) if (!is.null(open)) { return(open) } - .resolveLdSketch(spec, chroms = chroms) + .resolveLdRef(spec, chroms = chroms) } # Canonical chromosomes present across a list of sumstats entry GRanges. NULL / @@ -363,15 +338,15 @@ NULL # LD-sketch containment check (validation only; no filtering) # ============================================================================= -# Canonical chromosomes present in an LD sketch: for a sharded handle these are -# the chromPaths names; for a single-file handle the unique snpInfo chromosomes. -.ldSketchChroms <- function(ldSketch) { - handle <- .ldSketchHandle(ldSketch) - if (length(chromPaths(handle)) > 0L) { - canonChrom(names(chromPaths(handle))) - } else { - unique(.ldSketchChrom(ldSketch)) +# Canonical chromosomes present in an LD reference: for a panel read from a +# per-chromosome manifest these are the manifest's chromosomes; otherwise the +# chromosomes of its variants. +.ldRefChroms <- function(ldReference) { + chromPaths <- .ldRefChromPaths(ldReference) + if (length(chromPaths) > 0L) { + return(canonChrom(names(chromPaths))) } + unique(.ldRefChrom(ldReference)) } # Two-tier containment check for one entry GRanges against the LD sketch: @@ -379,15 +354,15 @@ NULL # * variant tier: match entry variants against the sketch's variants on the # shared chromosomes. Zero overlap -> error; overlap fraction below # `minLdOverlapWarn` -> warning. Never filters. -.checkLdContainment <- function(ldSketch, entryGr, minLdOverlapWarn, label) { +.checkLdContainment <- function(ldReference, entryGr, minLdOverlapWarn, label) { if (length(entryGr) == 0L) { return(invisible(NULL)) } - reqChrom <- .ldContainmentCheckChroms(entryGr, ldSketch, label) - sketchGr <- .ldSketchRanges(ldSketch) + reqChrom <- .ldContainmentCheckChroms(entryGr, ldReference, label) + sketchGr <- .ldRefRanges(ldReference) if (length(sketchGr) == 0L) { msg <- glue( - "{label}: LD sketch carries no variant metadata; ", + "{label}: the LD reference carries no variant metadata; ", "skipping the variant-overlap check." ) warn(msg) @@ -404,16 +379,16 @@ NULL # The entry's chromosomes must all be present in the LD sketch; returns them. # @noRd -.ldContainmentCheckChroms <- function(entryGr, ldSketch, label) { +.ldContainmentCheckChroms <- function(entryGr, ldReference, label) { reqChrom <- unique(canonChrom( as.character(GenomicRanges::seqnames(entryGr)) )) - availChrom <- .ldSketchChroms(ldSketch) + availChrom <- .ldRefChroms(ldReference) missingChrom <- setdiff(reqChrom, availChrom) if (length(missingChrom) > 0L) { msg <- glue( "{label}: chromosome(s) {str_flatten(missingChrom, ', ')} ", - "are absent from the LD sketch ", + "are absent from the LD reference ", "(available: {str_flatten(availChrom, ', ')})." ) abort(msg) @@ -434,7 +409,7 @@ NULL canonChrom(as.character(GenomicRanges::seqnames(sketchGr))), reqChrom ) - # Same matching relation `.ldFromSketch()` will use when the LD is + # Same matching relation `.ldFromRef()` will use when the LD is # actually loaded, palindromes included -- otherwise this counts an A/T # variant as absent, under-reports the overlap and warns about a panel # that will in fact match. @@ -448,7 +423,7 @@ NULL if (nOverlap == 0L) { msg <- glue( "{label}: none of the {length(entryGr)} summary-statistic ", - "variants are present in the LD sketch." + "variants are present in the LD reference." ) abort(msg) } @@ -457,7 +432,7 @@ NULL pct <- sprintf("%.1f", 100 * frac) msg <- glue( "{label}: only {pct}% of summary-statistic variants ", - "({nOverlap}/{length(entryGr)}) are present in the LD sketch." + "({nOverlap}/{length(entryGr)}) are present in the LD reference." ) warn(msg) } @@ -806,9 +781,6 @@ NULL ) } -# Assemble the standardized sumstat data.frame (chrom/pos/SNP/A1/A2/Z, optional -# N / N_CASE / N_CONTROL, and any present BETA/SE/P/MAF/INFO). -# @noRd # The source columns named by explicit mappings, so an auto-detected key never # re-reads a column another key already claimed. # @noRd @@ -866,6 +838,9 @@ NULL keep(sources, .sumstatSourceFound) } +# Assemble the standardized sumstat data.frame (chrom/pos/SNP/A1/A2/Z, optional +# N / N_CASE / N_CONTROL, and any present BETA/SE/P/MAF/INFO). +# @noRd .buildSumstatOut <- function(df, resolved, z, n, mapping, label) { claimed <- .sumstatClaimedCols(mapping) present <- .sumstatOptionalSources(df, mapping, label, claimed) @@ -920,7 +895,8 @@ NULL )) return(invisible(NULL)) } - if (all(is.na(out$AF))) { + # An empty result has no values to be unusable: all() of none is TRUE. + if (length(out$AF) > 0L && all(is.na(out$AF))) { warn(glue( "{label}: effect-allele frequency `af` was declared but its ", "values are all missing/unusable; top_loci$af will be NA." @@ -989,24 +965,25 @@ NULL # Read column `tag` from the GWAS-VCF geno matrix for one sample as numeric, or # NULL when the tag is absent/unmapped. -# @noRd +# # `tag` may name SEVERAL candidate keys in priority order (the spec's key then # the spellings found in the wild), so the first one the file actually carries -# wins. A single string still works -- it is a length-1 candidate list. +# wins. A single string still works -- it is a length-1 candidate list. The +# result is unnamed: geno matrix columns carry variant-id element names that +# the canonical sumstat tibble must not keep (data.frame stripped them). # @noRd .vcfGetField <- function(tag, geno, sample) { hit <- intersect(as.character(tag %||% character(0)), names(geno)) if (length(hit) == 0L) { return(NULL) } - # unname: geno matrix columns carry variant-id element names that the - # canonical sumstat tibble must not keep (data.frame stripped them). unname(as.numeric(geno[[hit[[1L]]]][, sample])) } # Convert a VCF (GWAS-VCF format) into the canonical sumstats data.frame. The # effect allele (A1) is ALT; the other allele (A2) is REF. Stats come from the # per-study FORMAT fields ES/SE/LP/SS/EAF, with Z = ES / SE. +# @noRd .vcfToSumstatDf <- function(vcf, sampleSelect, formatMapping, label) { fmap <- list_modify( .gwasVcfFormatDefaults, @@ -1016,59 +993,66 @@ NULL ) rr <- SummarizedExperiment::rowRanges(vcf) altList <- VariantAnnotation::alt(vcf) - alt <- unname(map_chr(as.list(altList), .mlFirstAlt)) - ref <- unname(as.character(VariantAnnotation::ref(vcf))) - geno <- VariantAnnotation::geno(vcf) sample <- .pickVcfSample(colnames(vcf), sampleSelect, label) - es <- .vcfGetField(fmap$BETA, geno, sample) - se <- .vcfGetField(fmap$SE, geno, sample) - ez <- .vcfGetField(fmap$Z, geno, sample) - lp <- .vcfGetField(fmap$P, geno, sample) - ss <- .vcfGetField(fmap$N, geno, sample) - af <- .vcfGetField(fmap$AF, geno, sample) - eaf <- .vcfGetField(fmap$MAF, geno, sample) - if ((is.null(es) || is.null(se)) && is.null(ez)) { + fld <- map( + fmap[c("BETA", "SE", "Z", "P", "N", "AF", "MAF")], + .vcfGetField, + geno = VariantAnnotation::geno(vcf), + sample = sample + ) + .vcfRequireSumstatFields(fld, label) + z <- .vcfSumstatZ(fld$BETA, fld$SE, fld$Z, length(rr)) + out <- tibble( + chrom = as.character(GenomicRanges::seqnames(rr)), + pos = unname(as.integer(GenomicRanges::start(rr))), + SNP = names(rr), + A1 = unname(map_chr(as.list(altList), .mlFirstAlt)), + A2 = unname(as.character(VariantAnnotation::ref(vcf))), + Z = z$z, + z_supplied = z$supplied, + N = fld$N + ) + mutate(out, !!!.vcfOptionalSumstatCols(fld)) +} + +# A GWAS-VCF must yield a z (supplied, or derivable from ES and SE) and a +# sample size. `fld` is the per-field read of `.vcfToSumstatDf()`. +# @noRd +.vcfRequireSumstatFields <- function(fld, label) { + if ((is.null(fld$BETA) || is.null(fld$SE)) && is.null(fld$Z)) { msg <- glue( "{label}: GWAS-VCF must carry either a supplied z (EZ) or ES and ", "SE to derive one from (configure via `formatMapping`)." ) abort(msg) } - if (is.null(ss)) { + if (is.null(fld$N)) { msg <- glue( "{label}: GWAS-VCF must carry a sample-size FORMAT field ", "(NS, or SS in older files; configure via `formatMapping`)." ) abort(msg) } - z <- .vcfSumstatZ(es, se, ez, length(rr)) - out <- tibble( - chrom = as.character(GenomicRanges::seqnames(rr)), - pos = unname(as.integer(GenomicRanges::start(rr))), - SNP = names(rr), - A1 = alt, - A2 = ref, - Z = z$z, - z_supplied = z$supplied, - N = ss - ) - mutate( - out, - !!!compact(list( - BETA = es, - SE = se, - P = if (!is.null(lp)) 10^(-lp), - AF = af, - # A directionless MAF of its own when the file has one; else - # folded from AF, which is QC-usable and keeps `AF` intact - # alongside it rather than consuming it. - MAF = if (!is.null(eaf)) { - pmin(eaf, 1 - eaf) - } else if (!is.null(af)) { - pmin(af, 1 - af) - } - )) - ) +} + +# The optional sumstat columns a GWAS-VCF may carry, NULL ones dropped. MAF is +# the file's own directionless MAF when it has one; else folded from AF, which +# is QC-usable and keeps `AF` intact alongside it rather than consuming it. +# @noRd +.vcfOptionalSumstatCols <- function(fld) { + eaf <- fld$MAF + af <- fld$AF + compact(list( + BETA = fld$BETA, + SE = fld$SE, + P = if (!is.null(fld$P)) 10^(-fld$P), + AF = af, + MAF = if (!is.null(eaf)) { + pmin(eaf, 1 - eaf) + } else if (!is.null(af)) { + pmin(af, 1 - af) + } + )) } # The z and its provenance, per variant. A supplied `EZ` wins where it has a @@ -1178,11 +1162,14 @@ NULL if (is.null(order) || identical(order, "none")) { return(df) } - have <- map_lgl( + alleleCols <- map( c("A1", "A2"), - function(k) !.mlIsNaOrNull(.resolveSumstatKey(k, df, mapping, label)) + .resolveSumstatKey, + df = df, + mapping = mapping, + label = label ) - if (any(have)) { + if (!all(map_lgl(alleleCols, .mlIsNaOrNull))) { return(df) # a real allele column always wins } idCol <- .resolveSumstatKey("variant_id", df, mapping, label) @@ -1332,13 +1319,15 @@ NULL # holds row identifiers; `transpose = TRUE` handles QTLtools-format inputs # (covariates as rows, samples as columns). .readCovariateMatrix <- function(path, transpose = FALSE) { - raw <- as.data.frame( - readr::read_tsv(path, show_col_types = FALSE, progress = FALSE), - check.names = FALSE + raw <- readr::read_tsv( + path, + show_col_types = FALSE, + progress = FALSE, + name_repair = "minimal" ) m <- `rownames<-`( - `storage.mode<-`(as.matrix(raw[, -1L, drop = FALSE]), "double"), - as.character(raw[[1L]]) + `storage.mode<-`(as.matrix(select(raw, -1L)), "double"), + as.character(pull(raw, 1L)) ) if (transpose) { return(t(m)) @@ -1359,8 +1348,6 @@ NULL ) } -# Read a phenotype BED into an expression matrix (genes x samples) + rowRanges. -# @noRd # Column names (and their case/alias variants) that are metadata, not # expression samples, in a phenotype BED. # @noRd @@ -1382,15 +1369,15 @@ NULL ) } +# Read a phenotype BED into an expression matrix (genes x samples) + rowRanges. +# @noRd .readPhenotypeBed <- function(bedPath) { - bed <- as.data.frame( - readr::read_tsv( - bedPath, - show_col_types = FALSE, - progress = FALSE, - comment = "" - ), - check.names = FALSE + bed <- readr::read_tsv( + bedPath, + show_col_types = FALSE, + progress = FALSE, + comment = "", + name_repair = "minimal" ) chrCol <- intersect(c("#chr", "#chrom", "chrom", "chr"), names(bed))[1L] startCol <- intersect(c("start", "Start"), names(bed))[1L] @@ -1404,12 +1391,12 @@ NULL abort(msg) } meta <- `names<-`( - bed[, c(chrCol, startCol, endCol, geneCol)], + select(bed, all_of(c(chrCol, startCol, endCol, geneCol))), c("chrom", "start", "end", "gene_id") ) sampleCols <- setdiff(names(bed), .bedReservedCols()) expr <- `rownames<-`( - `storage.mode<-`(as.matrix(bed[, sampleCols, drop = FALSE]), "double"), + `storage.mode<-`(as.matrix(select(bed, all_of(sampleCols))), "double"), meta$gene_id ) rr <- `names<-`( @@ -1501,7 +1488,7 @@ NULL ), contexts ) - genoHandle <- .resolveGenoHandle(rows, studyName, base, genotypesOverride) + genotypes <- .resolveGenotypes(rows, studyName, base, genotypesOverride) genoCov <- .resolveGenoCov( rows, studyName, @@ -1512,7 +1499,7 @@ NULL qtlArgs <- c( list( study = studyName, - genotypes = genoHandle, + genotypes = genotypes, phenotypes = phenotypes, genotypeCovariates = genoCov ), @@ -1561,11 +1548,12 @@ NULL .resolveRel(covs[[1L]], base) } -# The genotype handle: an override panel/path, else the study's single -# genotypePath (auto-detecting the format). +# The genotype panel: an override panel (or bare handle) as given, an +# override path, else the study's single genotypePath (auto-detecting the +# format). # @noRd -.resolveGenoHandle <- function(rows, studyName, base, genotypesOverride) { - open <- .openGenotypeHandle(genotypesOverride) +.resolveGenotypes <- function(rows, studyName, base, genotypesOverride) { + open <- .openGenotypeSource(genotypesOverride) if (!is.null(open)) { return(open) } @@ -1582,7 +1570,7 @@ NULL } v[[1L]] } - .detectGenotypeFormat(.resolveRel(spec, base)) + .readGenotypePath(.resolveRel(spec, base)) } # The genotype covariate matrix: an override matrix/path, else the study's @@ -1715,7 +1703,7 @@ loadQtlDatasetFromManifest <- function( nSample = c("nSample", "n_sample"), varY = c("varY", "var_y"), genome = c("genome"), - ldSketchPath = c("ldSketchPath", "ld_sketch_path", "ld_sketch") + ldReferencePath = c("ldReferencePath", "ld_reference_path", "ld_reference") ) .qtlSumStatsManifestAliases <- list( @@ -1738,7 +1726,7 @@ loadQtlDatasetFromManifest <- function( nSample = c("nSample", "n_sample"), varY = c("varY", "var_y"), genome = c("genome"), - ldSketchPath = c("ldSketchPath", "ld_sketch_path", "ld_sketch") + ldReferencePath = c("ldReferencePath", "ld_reference_path", "ld_reference") ) # TRUE if study `i` has usable case/control counts OR a total-N scalar, so a @@ -1758,18 +1746,18 @@ loadQtlDatasetFromManifest <- function( # range. The checks sit between the two steps because containment is only # answerable once the sketch is read, and trimming would hide a miss. # @noRd -.manifestGwasLdSketch <- function( - ldSketchSpec, +.manifestGwasLdRef <- function( + ldReferenceSpec, entries, df, minLdOverlapWarn ) { - materialized <- .materializeLdSketch( - ldSketchSpec, + materialized <- .materializeLdRef( + ldReferenceSpec, .entriesChroms(entries) ) .checkGwasLdContainment(materialized, entries, df, minLdOverlapWarn) - .subsetSketchToRange(materialized, entries) + .subsetLdRefToRange(materialized, entries) } #' @title Load a GwasSumStats collection from a manifest @@ -1782,27 +1770,29 @@ loadQtlDatasetFromManifest <- function( #' \code{study} (required, unique), \code{sumStatsPath} (required), #' \code{columnMapping} (optional), \code{nCase} / \code{nControl} (optional), #' \code{nSample} (optional study-level total N), \code{varY} (optional), and -#' the single-valued \code{genome} / \code{ldSketchPath}. When a row supplies -#' a study-level scalar (\code{nCase} + \code{nControl}, or \code{nSample}), -#' its sumstats file need not carry a per-variant \code{N} column; +#' the single-valued \code{genome} / \code{ldReferencePath}. When a row +#' supplies a study-level scalar (\code{nCase} + \code{nControl}, or +#' \code{nSample}), its sumstats file need not carry a per-variant \code{N} +#' column; #' \code{\link{summaryStatsQc}} fills \code{N} from the scalar. #' @param genome Genome build; reconciled with a \code{genome} column. -#' @param ldSketch A genotype panel (see \code{\link{readGenotypes}}) or a spec -#' (path/prefix/genoMeta); reconciled with an \code{ldSketchPath} column. +#' @param ldReference A genotype panel (see \code{\link{readGenotypes}}) or a +#' spec (path/prefix/genoMeta); reconciled with an \code{ldReferencePath} +#' column. #' @param region Optional \code{chr:start-end} string, GRanges, or one-row #' data.frame restricting the variants read (honoured only for tabix-indexed #' text and bgzipped+tabixed VCF; ignored with a warning otherwise). #' @param minLdOverlapWarn Warn when the fraction of sumstats variants present -#' in the LD sketch falls below this (default 0.5). +#' in the LD reference falls below this (default 0.5). #' @param columnMapping Optional default column mapping (a named list/vector, or #' a path to a YAML file of \code{standardName: sourceName} entries) applied #' when a row has no \code{columnMapping}. #' @param sampleSelect Optional GWAS-VCF FORMAT sample (study) column to read. #' @param formatMapping Optional GWAS-VCF FORMAT tag mapping (canonical stat -> #' FORMAT field), overriding ES/SE/LP/SS/EAF defaults. -#' @param ldBlocks Optional LD-block specification (an \code{LdBlocks}, a -#' \code{GRanges}, a data.frame with \code{chrom}/\code{start}/\code{end}, -#' or a path to such a table). Without it a genome-wide file splits into one +#' @param ldBlocks Optional LD-block specification (a \code{GRanges}, a +#' data.frame with \code{chrom}/\code{start}/\code{end}, or a path to such +#' a table). Without it a genome-wide file splits into one #' element per chromosome; with it, into one element per LD block, which is #' the granularity \code{\link{assembleCtwasInputs}} needs. #' @return A \code{GwasSumStats} object. @@ -1813,12 +1803,12 @@ loadQtlDatasetFromManifest <- function( #' package = "pecotmr"), "protocol_example.LD.chr22") #' manifest <- data.frame(study = "g1", sumStatsPath = gwasTsv) #' loadGwasSumStatsFromManifest(manifest = manifest, genome = "hg38", -#' ldSketch = ldStem, region = "chr22:10000000-19000000") +#' ldReference = ldStem, region = "chr22:10000000-19000000") #' @export loadGwasSumStatsFromManifest <- function( manifest, genome = NULL, - ldSketch = NULL, + ldReference = NULL, region = NULL, minLdOverlapWarn = 0.5, columnMapping = NULL, @@ -1837,7 +1827,7 @@ loadGwasSumStatsFromManifest <- function( abort("GwasSumStats manifest `study` values must be unique.") } genome <- .reconcileScalar(df[["genome"]], genome, "genome") - ldSketchSpec <- .resolveLdSketchInput(df, ldSketch, base) + ldReferenceSpec <- .resolveLdRefInput(df, ldReference, base) ns <- .gwasStudyScalars(df) entries <- map( seq_len(nrow(df)), @@ -1850,8 +1840,8 @@ loadGwasSumStatsFromManifest <- function( formatMapping = formatMapping, ns = ns ) - ldSketch <- .manifestGwasLdSketch( - ldSketchSpec, + ldReference <- .manifestGwasLdRef( + ldReferenceSpec, entries, df, minLdOverlapWarn @@ -1860,7 +1850,7 @@ loadGwasSumStatsFromManifest <- function( # is too coarse for cTWAS; with one, each study becomes one element per LD # block. The constructor does the splitting either way. gwasArgs <- list_assign( - .gwasSumStatsArgs(df, entries, genome, ldSketch, ns), + .gwasSumStatsArgs(df, entries, genome, ldReference, ns), ldBlocks = ldBlocks ) exec(GwasSumStats, !!!gwasArgs) @@ -1926,10 +1916,15 @@ loadGwasSumStatsFromManifest <- function( # Per-study LD-containment checks (deferred until the sketch is materialised). # @noRd -.checkGwasLdContainment <- function(ldSketch, entries, df, minLdOverlapWarn) { +.checkGwasLdContainment <- function( + ldReference, + entries, + df, + minLdOverlapWarn +) { for (i in seq_len(nrow(df))) { .checkLdContainment( - ldSketch, + ldReference, entries[[i]], minLdOverlapWarn, str_c("GwasSumStats[study=", df$study[[i]], "]") @@ -1939,13 +1934,13 @@ loadGwasSumStatsFromManifest <- function( # GwasSumStats() constructor args, including any present study scalars + varY. # @noRd -.gwasSumStatsArgs <- function(df, entries, genome, ldSketch, ns) { +.gwasSumStatsArgs <- function(df, entries, genome, ldReference, ns) { c( list( study = as.character(df$study), entry = entries, genome = genome, - ldSketch = ldSketch + ldReference = ldReference ), compact(list( nCase = ns$nCase, @@ -1998,14 +1993,14 @@ loadGwasSumStatsFromManifest <- function( #' \code{study}, \code{context}, \code{trait} (required), \code{sumStatsPath} #' (required), \code{columnMapping} (optional), \code{nSample} (optional #' tuple-level total N), \code{varY} (optional), and the single-valued -#' \code{genome} / \code{ldSketchPath}. When a row supplies \code{nSample}, +#' \code{genome} / \code{ldReferencePath}. When a row supplies \code{nSample}, #' its sumstats file need not carry a per-variant \code{N} column; #' \code{\link{summaryStatsQc}} fills \code{N} from the scalar. (Unlike the #' GWAS loader, there are no \code{nCase}/\code{nControl} columns: molecular #' QTL traits are quantitative.) #' @param genome Genome build; reconciled with a \code{genome} column. -#' @param ldSketch A genotype panel (see \code{\link{readGenotypes}}) or spec; -#' reconciled with an \code{ldSketchPath} column. +#' @param ldReference A genotype panel (see \code{\link{readGenotypes}}) or +#' spec; reconciled with an \code{ldReferencePath} column. #' @param region,minLdOverlapWarn,columnMapping,sampleSelect,formatMapping As #' for \code{\link{loadGwasSumStatsFromManifest}}. #' @param traitColumn Optional column of the sumstats file naming the trait of @@ -2033,12 +2028,12 @@ loadGwasSumStatsFromManifest <- function( #' manifest <- data.frame(study = "s1", context = "brain", #' trait = "ENSG1", sumStatsPath = tsv) #' loadQtlSumStatsFromManifest(manifest = manifest, genome = "hg38", -#' ldSketch = ldStem, region = "chr22:10000000-19000000") +#' ldReference = ldStem, region = "chr22:10000000-19000000") #' @export loadQtlSumStatsFromManifest <- function( manifest, genome = NULL, - ldSketch = NULL, + ldReference = NULL, region = NULL, minLdOverlapWarn = 0.5, columnMapping = NULL, @@ -2049,14 +2044,9 @@ loadQtlSumStatsFromManifest <- function( ) { variantIdAlleles <- arg_match(variantIdAlleles) base <- .manifestBase(manifest) - df <- .canonManifestCols( - .readManifest(manifest), - .qtlSumStatsManifestAliases, - required = c("study", "context", "trait", "sumStatsPath"), - label = "QtlSumStats" - ) + df <- .qtlSumStatsManifest(manifest) genome <- .reconcileScalar(df[["genome"]], genome, "genome") - ldSketchSpec <- .resolveLdSketchInput(df, ldSketch, base) + ldReferenceSpec <- .resolveLdRefInput(df, ldReference, base) nSample <- .qtlManifestNSample(df) entries <- .loadQtlSumStatsEntries( df, @@ -2069,21 +2059,53 @@ loadQtlSumStatsFromManifest <- function( traitColumn = traitColumn, variantIdAlleles = variantIdAlleles ) - - # Materialise the LD sketch reading only the chromosomes the summary stats - # cover (skips other shards of a chrom-sharded panel), check containment, - # then trim its snpInfo to the summary stats' per-chromosome position span. - materialized <- .materializeLdSketch( - ldSketchSpec, - .entriesChroms(entries) + ldReference <- .manifestQtlLdRef( + ldReferenceSpec, + entries, + df, + minLdOverlapWarn ) - .qtlSumStatsCheckContainment(materialized, entries, df, minLdOverlapWarn) - ldSketch <- .subsetSketchToRange(materialized, entries) - exec( QtlSumStats, - !!!.qtlSumStatsArgs(df, entries, genome, ldSketch, nSample$nSampleCol) + !!!.qtlSumStatsArgs( + df, + entries, + genome, + ldReference, + nSample$nSampleCol + ) + ) +} + +# A QtlSumStats manifest's rows, its columns under their canonical names and +# the required ones present. +# @noRd +.qtlSumStatsManifest <- function(manifest) { + .canonManifestCols( + .readManifest(manifest), + .qtlSumStatsManifestAliases, + required = c("study", "context", "trait", "sumStatsPath"), + label = "QtlSumStats" + ) +} + +# Materialise the LD sketch reading only the chromosomes the summary stats +# cover (skipping the other shards of a chrom-sharded panel), check +# containment, then trim it to the summary stats' per-chromosome position +# span, as .manifestGwasLdRef() does for a GwasSumStats manifest. +# @noRd +.manifestQtlLdRef <- function( + ldReferenceSpec, + entries, + df, + minLdOverlapWarn +) { + materialized <- .materializeLdRef( + ldReferenceSpec, + .entriesChroms(entries) ) + .qtlSumStatsCheckContainment(materialized, entries, df, minLdOverlapWarn) + .subsetLdRefToRange(materialized, entries) } # The tuple-level total-N scalar a manifest may carry, plus the per-row @@ -2106,17 +2128,17 @@ loadQtlSumStatsFromManifest <- function( # Per-row LD-containment check (no-op when the sketch is NULL). # @noRd .qtlSumStatsCheckContainment <- function( - ldSketch, + ldReference, entries, df, minLdOverlapWarn ) { - if (is.null(ldSketch)) { + if (is.null(ldReference)) { return(invisible(NULL)) } for (i in seq_len(nrow(df))) { .checkLdContainment( - ldSketch, + ldReference, entries[[i]], minLdOverlapWarn, str_c( @@ -2135,14 +2157,14 @@ loadQtlSumStatsFromManifest <- function( # Assemble the QtlSumStats() argument list from a canonicalised manifest. # @noRd -.qtlSumStatsArgs <- function(df, entries, genome, ldSketch, nSampleCol) { +.qtlSumStatsArgs <- function(df, entries, genome, ldReference, nSampleCol) { args <- list( study = as.character(df$study), context = as.character(df$context), trait = as.character(df$trait), entry = entries, genome = genome, - ldSketch = ldSketch + ldReference = ldReference ) c( args, @@ -2162,7 +2184,7 @@ loadQtlSumStatsFromManifest <- function( #' \code{study}); one \code{QtlDataset} is built per study group. #' @param sumStatsManifest Optional QtlSumStats manifest (see #' \code{\link{loadQtlSumStatsFromManifest}}). -#' @param genome,ldSketch,region,minLdOverlapWarn Passed to the QtlSumStats +#' @param genome,ldReference,region,minLdOverlapWarn Passed to the QtlSumStats #' loader for \code{sumStatsManifest}. #' @param columnMapping,sampleSelect,formatMapping Passed to the QtlSumStats #' loader for \code{sumStatsManifest}. @@ -2185,7 +2207,7 @@ loadMultiStudyQtlDatasetFromManifest <- function( qtlDatasetsManifest, sumStatsManifest = NULL, genome = NULL, - ldSketch = NULL, + ldReference = NULL, region = NULL, minLdOverlapWarn = 0.5, columnMapping = NULL, @@ -2207,7 +2229,7 @@ loadMultiStudyQtlDatasetFromManifest <- function( sumStats <- .msqLoadSumStats( sumStatsManifest, genome, - ldSketch, + ldReference, region, minLdOverlapWarn, columnMapping, @@ -2257,7 +2279,7 @@ loadMultiStudyQtlDatasetFromManifest <- function( .msqLoadSumStats <- function( sumStatsManifest, genome, - ldSketch, + ldReference, region, minLdOverlapWarn, columnMapping, @@ -2270,7 +2292,7 @@ loadMultiStudyQtlDatasetFromManifest <- function( loadQtlSumStatsFromManifest( sumStatsManifest, genome = genome, - ldSketch = ldSketch, + ldReference = ldReference, region = region, minLdOverlapWarn = minLdOverlapWarn, columnMapping = columnMapping, diff --git a/R/mashPipeline.R b/R/mashPipeline.R index 9c9301fee..055d4c254 100644 --- a/R/mashPipeline.R +++ b/R/mashPipeline.R @@ -285,7 +285,7 @@ mashPipeline <- function( # Every bundle mashPipeline() takes, checked together. # @noRd .mashPipelineAssert <- function(prior, mashDataArgs, mashArgs) { - .assertMethodParam(prior, "MashPriorParam", "prior") + .assertMethodParam(prior, "MashPriorParam") .assertMethodOptions(mashDataArgs, "MashDataOptions", "mashDataArgs") .assertMethodOptions(mashArgs, "MashOptions", "mashArgs") } @@ -652,9 +652,6 @@ mashCovarianceComponents <- function( .mashComponentList(c(built$canonical, built$dataDriven)) } -# `components` and `engine` each take a name or that choice's own -# constructor, so both resolve to a (name, options) pair the same way. -# @noRd # Both arguments arrive already normalized -- a MashComponentParam and an # Options record or NULL -- so this only splits them into the shapes the # cov_* chain downstream expects. NULL engine is "none": no refinement. @@ -669,6 +666,9 @@ mashCovarianceComponents <- function( ) } +# mashPriorCovariances() is exported with the same convenience arguments as +# MashPriorParam(), so it normalizes through the same helpers rather than +# resolving a second time in its own way. #' @title Estimate mash Prior Covariances and Mixture Weights #' @description Build the mash prior covariance list \code{U} and mixture #' weights \code{w} from a \code{{strong[, random, null]}} SumStats @@ -753,17 +753,10 @@ mashPriorCovariances <- function( .assertMethodOptions(mashDataArgs, "MashDataOptions", "mashDataArgs") .assertMethodOptions(mashArgs, "MashOptions", "mashArgs") inputScale <- arg_match(inputScale) - # Exported with the same convenience arguments as MashPriorParam(), so - # it normalizes through the same helpers rather than resolving a second - # time in its own way. choice <- .mashResolvePriorChoice( .mashNormalizeComponents(components), .mashNormalizeEngine(engine) ) - components <- choice$components - componentArgs <- choice$componentArgs - engine <- choice$engine - engineArgs <- choice$engineArgs .mashRequirePriorPackages() if (methods::is(sumStatsList, "SimpleList")) { sumStatsList <- as.list(sumStatsList) @@ -783,11 +776,11 @@ mashPriorCovariances <- function( mashData, priorCovariances = priorCovariances, priorComponents = priorComponents, - components = components, + components = choice$components, nPcs = nPcs, - engine = engine, - engineArgs = engineArgs, - componentArgs = componentArgs, + engine = choice$engine, + engineArgs = choice$engineArgs, + componentArgs = choice$componentArgs, mashArgs = mashArgs ) } @@ -939,25 +932,6 @@ MashComponentParam <- function( # Reject unknown prior-covariance component names. # @noRd -# `components` may be a character vector or a MashComponentParam() record. -# Naming a component in the record selects it, so the two forms carry the -# same information and the record additionally carries per-component options. -# @noRd -.mashResolveComponentChoice <- function(components) { - if (is(components, "MashComponentParam")) { - entries <- slot(components, "components") %||% list() - return(list(names = names(entries), args = entries)) - } - if (!is.character(components)) { - abort(glue( - "mashPriorCovariances: `components` must be a character vector ", - "or a MashComponentParam() record." - )) - } - .mashValidateComponents(components) - list(names = components, args = NULL) -} - .mashValidateComponents <- function( components, caller = "mashPriorCovariances" @@ -1358,9 +1332,6 @@ fitMashContrast <- function( ) } -# Pairwise (all-pairs) contrasts, with grouped conditions' contributions split -# evenly across the group. -# @noRd # One grouped condition's share of a pairwise column: the group's matched # contribution split evenly across its members. # @noRd @@ -1395,6 +1366,9 @@ fitMashContrast <- function( ) } +# Pairwise (all-pairs) contrasts, with grouped conditions' contributions split +# evenly across the group. +# @noRd .mashPairwiseContrast <- function(tested, grouping, pairwiseVector) { twoCombn <- combn(tested, 2) pwNames <- apply(twoCombn, 2, str_flatten, collapse = "_vs_") @@ -2550,7 +2524,7 @@ mergeMashData <- function(resData, oneData) { } msg <- glue( "mashInput: each element of `objects` must be a QtlSumStats, ", - "GwasSumStats, or FineMappingResult; got ", + "GwasSumStats, QtlFineMappingResult or GwasFineMappingResult; got ", "{str_flatten(class(obj), '/')}." ) abort(msg) @@ -2566,9 +2540,9 @@ mergeMashData <- function(resData, oneData) { list(b = mats$b, s = mats$s, strongRows = strongRows) } -# (Bhat, Shat, strongRows) from a FineMappingResult: pivot the marginal effects -# to a variants x contexts matrix pair; strong = each credible set's lead (max -# PIP) variant. +# (Bhat, Shat, strongRows) from a fine-mapping result: pivot the marginal +# effects to a variants x contexts matrix pair; strong = each credible set's +# lead (max PIP) variant. # @noRd .mashFmrMatrices <- function(obj, coverage) { rawMe <- marginalEffects(obj) @@ -2576,7 +2550,7 @@ mergeMashData <- function(resData, oneData) { if (!all(is_in(c("variant_id", "context", "beta", "se"), names(rawMe)))) { msg <- glue( "mashInput: marginalEffects() must return variant_id/context/", - "beta/se columns; a FineMappingResult with >= 2 contexts is ", + "beta/se columns; a fine-mapping result with >= 2 contexts is ", "required." ) abort(msg) @@ -2601,7 +2575,7 @@ mergeMashData <- function(resData, oneData) { ) } -# A multi-method FineMappingResult would duplicate (variant, context) cells; +# A multi-method fine-mapping result would duplicate (variant, context) cells; # pin the first method so the pivot is unambiguous. # @noRd .mashFmrMethodPin <- function(me, cs) { @@ -2610,7 +2584,7 @@ mergeMashData <- function(resData, oneData) { } m1 <- me$method[[1L]] msg <- glue( - "mashInput: FineMappingResult carries multiple methods; using ", + "mashInput: the fine-mapping result carries multiple methods; using ", "'{m1}'." ) warn(msg) @@ -2746,7 +2720,7 @@ mergeMashData <- function(resData, oneData) { #' \describe{ #' \item{strong}{The high-signal variants (deterministic, class-specific). #' \code{QtlSumStats}: the single most significant variant (\eqn{\max|z|}) -#' per condition, unioned. \code{FineMappingResult}: the lead variant +#' per condition, unioned. A fine-mapping result: the lead variant #' (\eqn{\max} PIP) of each credible set in each condition, unioned #' (conditions with no credible set contribute nothing).} #' \item{random}{\code{nRandom} variants sampled uniformly at random -- @@ -2762,21 +2736,21 @@ mergeMashData <- function(resData, oneData) { #' and the strong \code{XtX} cross-product appended. #' #' @param objects A named \code{list} of \code{\link{QtlSumStats}} and/or -#' \code{FineMappingResult} objects, one per region. Names disambiguate +#' fine-mapping result objects, one per region. Names disambiguate #' rownames across regions (defaults to \code{region1}, \code{region2}, ...). #' For \code{QtlSumStats} inputs \code{\link{summaryStatsQc}} must have been #' run (the matrix builder rejects un-QC'd SumStats). #' @param nRandom,nNull Per-object random / null sample sizes (default 10 each). #' @param excludeCondition Character vector of condition (column) names to drop. -#' @param coverage Credible-set coverage for \code{FineMappingResult} strong -#' selection (default 0.95). +#' @param coverage Credible-set coverage for the strong selection on +#' fine-mapping results (default 0.95). #' @param zOnly When \code{TRUE} the returned partitions carry only \code{.z} #' (the \code{.b}/\code{.s} matrices are dropped after z is derived). #' @param sigPCutoff Significance cutoff applied to the strong partition #' (default 1e-6). #' @param inputScale Matrix scale for \code{QtlSumStats} inputs #' (\code{"auto"}/\code{"beta"}/\code{"z"}); ignored for -#' \code{FineMappingResult} (always effect-size scale). +#' fine-mapping results (always effect-size scale). #' @param independentVariants Optional character vector of variant ids (e.g. an #' LD-pruned independent SNP list). When supplied, the \emph{random} and #' \emph{null} background of every object is restricted to variants that match @@ -2835,7 +2809,7 @@ mashInput <- function( if (!is.list(objects) || length(objects) == 0L) { msg <- glue( "mashInput: `objects` must be a non-empty list of QtlSumStats ", - "and/or FineMappingResult objects." + "and/or fine-mapping result objects." ) abort(msg) } @@ -2974,7 +2948,7 @@ mashInput <- function( #' variant ids (ideally \code{chr:pos:A2:A1}); \code{colnames(z)} label the #' conditions. #' @param studyName Study identifier (recycled across conditions). -#' @param ldSketch A genotype panel (see \code{\link{readGenotypes}}) +#' @param ldReference A genotype panel (see \code{\link{readGenotypes}}) #' embedded in the collection, or \code{NULL} (default) -- mash operates #' across #' conditions per variant and needs no LD reference. @@ -2997,13 +2971,13 @@ mashInput <- function( #' system.file("extdata", "toy_ref.bed", package = "pecotmr")) #' z <- matrix(rnorm(6), 2, 3, dimnames = list( #' c("chr22:1:A:G", "chr22:2:A:G"), c("brain", "blood", "muscle"))) -#' qtlSumStatsFromZMatrix(z = z, studyName = "s1", ldSketch = panel, +#' qtlSumStatsFromZMatrix(z = z, studyName = "s1", ldReference = panel, #' context = colnames(z), trait = "g1", genome = "hg38", n = 100) #' @export qtlSumStatsFromZMatrix <- function( z, studyName, - ldSketch = NULL, + ldReference = NULL, context = colnames(z), trait = "mash", genome = "GRCh38", @@ -3024,7 +2998,7 @@ qtlSumStatsFromZMatrix <- function( vids = vids, nCond = ncol(z), studyName = studyName, - ldSketch = ldSketch, + ldReference = ldReference, context = context, trait = trait, genome = genome, @@ -3050,7 +3024,7 @@ qtlSumStatsFromZMatrix <- function( #' @param shat Numeric matrix of standard errors, aligned with \code{bhat} #' (identical dimensions and row/column order). #' @param studyName Study identifier (recycled across conditions). -#' @param ldSketch A genotype panel (see \code{\link{readGenotypes}}) +#' @param ldReference A genotype panel (see \code{\link{readGenotypes}}) #' embedded in the collection, or \code{NULL} (default) -- mash operates #' across #' conditions per variant and needs no LD reference. @@ -3072,14 +3046,14 @@ qtlSumStatsFromZMatrix <- function( #' c("chr22:1:A:G", "chr22:2:A:G"), c("brain", "blood", "muscle"))) #' shat <- matrix(0.1, 2, 3, dimnames = dimnames(bhat)) #' qtlSumStatsFromBetaMatrix(bhat = bhat, shat = shat, studyName = "s1", -#' ldSketch = panel, context = colnames(bhat), trait = "g1", +#' ldReference = panel, context = colnames(bhat), trait = "g1", #' genome = "hg38", n = 100) #' @export qtlSumStatsFromBetaMatrix <- function( bhat, shat, studyName, - ldSketch = NULL, + ldReference = NULL, context = colnames(bhat), trait = "mash", genome = "GRCh38", @@ -3094,7 +3068,7 @@ qtlSumStatsFromBetaMatrix <- function( vids = vids, nCond = ncol(bhat), studyName = studyName, - ldSketch = ldSketch, + ldReference = ldReference, context = context, trait = trait, genome = genome, @@ -3174,7 +3148,7 @@ qtlSumStatsFromBetaMatrix <- function( vids, nCond, studyName, - ldSketch, + ldReference, context, trait, genome, @@ -3202,7 +3176,7 @@ qtlSumStatsFromBetaMatrix <- function( trait = trait, entry = entries, genome = genome, - ldSketch = ldSketch, + ldReference = ldReference, qcInfo = list(role = role, entryAudit = vector("list", nCond)) ) } diff --git a/R/mashWrapper.R b/R/mashWrapper.R index 4caddc888..a36871167 100644 --- a/R/mashWrapper.R +++ b/R/mashWrapper.R @@ -151,16 +151,16 @@ ) } -# Internal: build the requested data-driven covariance components off a prepared -# mashr data object, in a fixed order (canonical, pca, flash, flashNonneg) so a -# given `components` set reproduces the same ordering everywhere. RNG is -# consumed only by cov_flash / cov_flash(nonneg). Shared by mashPriorCovariances -# and the exported mashCovarianceComponents. -# @noRd # Build the requested components, keeping the two roles apart: `canonical` # are fixed structural hypotheses that go to the mash fit unrefined, while # `dataDriven` are the generator output the engine refines. mashr's own eQTL # vignette draws exactly this line -- cov_ed sees only the data-driven set. +# +# The components are built off a prepared mashr data object in a fixed order +# (canonical, pca, flash, flashNonneg), so a given `components` set +# reproduces the same ordering everywhere. RNG is consumed only by cov_flash +# / cov_flash(nonneg). Shared by mashPriorCovariances and the exported +# mashCovarianceComponents. # @noRd .mashBuildComponents <- function( mashData, @@ -169,26 +169,34 @@ componentArgs = NULL ) { npc <- nPcs %||% (ncol(mashData$Bhat) - 1) - argsFor <- function(key) as.list(componentArgs[[key]] %||% list()) canonical <- if (is_in("canonical", components)) { exec( mashr::cov_canonical, mashData, - !!!compact(argsFor("canonical")) + !!!compact(.mashComponentArgs("canonical", componentArgs)) ) } pca <- if (is_in("pca", components)) { - exec(mashr::cov_pca, mashData, npc = npc, !!!compact(argsFor("pca"))) + exec( + mashr::cov_pca, + mashData, + npc = npc, + !!!compact(.mashComponentArgs("pca", componentArgs)) + ) } flash <- if (is_in("flash", components)) { - exec(mashr::cov_flash, mashData, !!!compact(argsFor("flash"))) + exec( + mashr::cov_flash, + mashData, + !!!compact(.mashComponentArgs("flash", componentArgs)) + ) } flashNonneg <- if (is_in("flashNonneg", components)) { exec( mashr::cov_flash, mashData, factors = "nonneg", - !!!compact(argsFor("flashNonneg")) + !!!compact(.mashComponentArgs("flashNonneg", componentArgs)) ) } list( @@ -197,6 +205,12 @@ ) } +# The caller's extra arguments for one component generator, as a list. +# @noRd +.mashComponentArgs <- function(key, componentArgs) { + as.list(componentArgs[[key]] %||% list()) +} + # The covariance list and its mixture weights. A supplied `priorCovariances` # short-circuits the cov_* chain; either way the weights come from a # weights-only mash() fit unless the covariance step already produced them diff --git a/R/pvalCombineWrappers.R b/R/pvalCombineWrappers.R index 2fe643076..10cfbf118 100644 --- a/R/pvalCombineWrappers.R +++ b/R/pvalCombineWrappers.R @@ -132,7 +132,33 @@ pvalPoolr <- function(pvals, method, R, methodArgs = PoolrOptions()) { invchisq = poolr::invchisq, .abortUnknownMethod("poolr", method) ) - exec(fn, pvals, R = R, !!!as.list(methodArgs))$p + args <- as.list(methodArgs) + exec(fn, pvals, R = .poolrDependence(R, method, args), !!!args)$p +} + +# `R` in the form poolr's adjustment reads it. `R` holds the correlations +# among the test statistics. The generalized adjustment wants the covariances +# among each test's transformed statistic (-2 log p for Fisher, z for +# Stouffer, the 1-df chi-square for invchisq), and the effective-number +# adjustments want the correlations among the p-values; mvnconv() converts to +# both. "empirical" simulates from the statistics' own correlations. +# @noRd +.poolrDependence <- function(R, method, args) { + adjust <- args$adjust %||% "none" + if (is.null(R) || is_in(adjust, c("none", "empirical"))) { + return(R) + } + side <- args$side %||% 2 + if (identical(adjust, "generalized")) { + target <- switch( + method, + fisher = "m2lp", + stouffer = "z", + invchisq = "chisq1" + ) + return(poolr::mvnconv(R, side = side, target = target)) + } + poolr::mvnconv(R, side = side, target = "p", cov2cor = TRUE) } pvalGbj <- function(zScores, R, method, methodArgs = GbjOptions()) { @@ -290,8 +316,14 @@ pvalAspu <- function( # Internal: compute one method's combined p-value. Assumes inputs have # already been validity-filtered (positive, finite, < 1) and aligned with R. -.combinePvalSingle <- function(method, pvals, zScores, R, methodArgs = list()) { - args <- .combinePvalMethodArgs(methodArgs, method) +.combinePvalSingle <- function( + method, + pvals, + zScores, + R, + pvalMethodsParam = list() +) { + args <- .combinePvalMethodArgs(pvalMethodsParam, method) switch( method, acat = pvalAcat(pvals), @@ -327,8 +359,8 @@ pvalAspu <- function( # constructor's result rather than list() keeps each engine's pecotmr # defaults -- poolr's `adjust = "generalized"` -- in force either way. # @noRd -.combinePvalMethodArgs <- function(methodArgs, method) { - entries <- .pvalMethodEntriesOf(methodArgs) +.combinePvalMethodArgs <- function(pvalMethodsParam, method) { + entries <- .pvalMethodEntriesOf(pvalMethodsParam) i <- match(method, names(entries)) if (!is.na(i)) { return(entries[[i]]) @@ -340,11 +372,11 @@ pvalAspu <- function( # The per-method entries, whether they arrived in a PvalMethodsParam or as # the bare list() that still means "no settings". # @noRd -.pvalMethodEntriesOf <- function(methodArgs) { - if (is(methodArgs, "PvalMethodsParam")) { - return(slot(methodArgs, "methods") %||% list()) +.pvalMethodEntriesOf <- function(pvalMethodsParam) { + if (is(pvalMethodsParam, "PvalMethodsParam")) { + return(slot(pvalMethodsParam, "methods") %||% list()) } - methodArgs %||% list() + pvalMethodsParam %||% list() } #' Combine P-values via Any of a Menu of Methods @@ -379,11 +411,12 @@ pvalAspu <- function( #' @param zScores Optional numeric vector of signed z-scores. #' @param methods Character vector of combination method names; see above for #' the menu (lowercase). -#' @param R Optional correlation matrix aligned to \code{pvals} / +#' @param R Optional correlation matrix among the test statistics (for +#' variant-level tests, their LD), aligned to \code{pvals} / #' \code{zScores}. Required for the correlation-adjusted methods. #' @param naRm Logical; if \code{TRUE} (default), drop NA p-values before #' combination. -#' @param methodArgs Per-method engine options, built with +#' @param pvalMethodsParam Per-method engine options, built with #' \code{\link{PvalMethodsParam}} -- for example #' \code{PvalMethodsParam(aspu = AspuOptions(n.perm = 5000))}. Configuring #' a method that \code{methods} does not request is an error rather than a @@ -407,12 +440,12 @@ combinePValues <- function( methods, R = NULL, naRm = TRUE, - methodArgs = PvalMethodsParam() + pvalMethodsParam = PvalMethodsParam() ) { assertFlag(naRm) - .assertMethodParam(methodArgs, "PvalMethodsParam", "methodArgs") + .assertMethodParam(pvalMethodsParam, "PvalMethodsParam") methods <- .combinePvalCheckMethods(methods) - .combinePvalCheckMethodArgs(methodArgs, methods) + .combinePvalCheckMethodArgs(pvalMethodsParam, methods) nPvalsIn <- if (is.null(pvals)) 0L else length(pvals) nZScoresIn <- if (is.null(zScores)) 0L else length(zScores) .combinePvalCheckPrereqs(methods, zScores, R) @@ -435,7 +468,7 @@ combinePValues <- function( filt$pvalsK, filt$zScoresK, Raligned, - methodArgs + pvalMethodsParam ) .combinePvalResult(perMethod, input) } @@ -462,14 +495,14 @@ combinePValues <- function( # Options for a method that was not requested would be dropped in silence -- # exactly the failure the constructors exist to prevent -- so say so instead. # @noRd -.combinePvalCheckMethodArgs <- function(methodArgs, methods) { - configured <- names(.pvalMethodEntriesOf(methodArgs)) +.combinePvalCheckMethodArgs <- function(pvalMethodsParam, methods) { + configured <- names(.pvalMethodEntriesOf(pvalMethodsParam)) unused <- setdiff(configured, methods) if (length(unused) == 0L) { return(invisible(NULL)) } msg <- glue( - "combinePValues: `methodArgs` configures ", + "combinePValues: `pvalMethodsParam` configures ", "{str_flatten(unused, ', ')}, which `methods` does not request ", "({str_flatten(methods, ', ')}), so those options would be ignored." ) @@ -584,7 +617,7 @@ combinePValues <- function( pvalsK, zScoresK, Raligned, - methodArgs + pvalMethodsParam ) { set_names( map( @@ -593,7 +626,7 @@ combinePValues <- function( pvalsK = pvalsK, zScoresK = zScoresK, Raligned = Raligned, - methodArgs = methodArgs + pvalMethodsParam = pvalMethodsParam ), methods ) @@ -601,14 +634,20 @@ combinePValues <- function( # @noRd #' @importFrom rlang try_fetch -.combinePvalRunOne <- function(m, pvalsK, zScoresK, Raligned, methodArgs) { +.combinePvalRunOne <- function( + m, + pvalsK, + zScoresK, + Raligned, + pvalMethodsParam +) { p <- try_fetch( .combinePvalSingle( m, pvals = pvalsK, zScores = zScoresK, R = Raligned, - methodArgs = methodArgs + pvalMethodsParam = pvalMethodsParam ), error = function(cnd) { msg <- glue("combinePValues: method '{m}' failed") @@ -740,7 +779,12 @@ AspuOptions <- function(...) { #' \code{m}, \code{size}, \code{threshold}, \code{side}, \code{batchsize} #' and \code{nearpd}. The \pkg{poolr} signatures end in \code{...}, so #' names cannot be checked here and a misspelling is passed through. -#' \code{p} and \code{R} are supplied by pecotmr and refused. +#' \code{p} and \code{R} are supplied by pecotmr and refused: \code{R} is +#' the caller's correlation among the test statistics, which pecotmr +#' converts with \code{poolr::mvnconv()} into the form the adjustment +#' reads (the covariances of each test's transformed statistic for +#' \code{"generalized"}, the correlations among the p-values for the +#' effective-number adjustments, unchanged for \code{"empirical"}). #' @return A \code{MethodOptions} record for #' \code{\link{PvalMethodsParam}}. #' @seealso \code{\link{combinePValues}} diff --git a/R/qtlAssociationPostprocess.R b/R/qtlAssociationPostprocess.R index 33ff5470c..375fb9d1c 100644 --- a/R/qtlAssociationPostprocess.R +++ b/R/qtlAssociationPostprocess.R @@ -283,9 +283,6 @@ QvalueOptions <- function(...) { keep & !is.na(keep) } -# Q-value significance: per-entry mask of variant q-values below the threshold -# for the FDR-significant genes. -# @noRd # Entry `i`'s q-value mask, or the incoming mask when the gene is not # FDR-significant or carries no per-variant q-values. # @noRd @@ -297,6 +294,9 @@ QvalueOptions <- function(...) { !is.na(mc$qvalue) & mc$qvalue < threshold } +# Q-value significance: per-entry mask of variant q-values below the threshold +# for the FDR-significant genes. +# @noRd .qapMaskQvalue <- function(x, masks, threshold) { qCol <- if (!is.null(x$q_beta)) "q_beta" else "q_bonferroni_min_original" if (is.null(.tupleColumn(x, qCol))) { @@ -381,7 +381,7 @@ setMethod( methods::new( "QtlSumStats", grl, - ldSketch = ldSketch(x), + ldReference = ldReference(x), qcInfo = qcInfo ) } @@ -565,10 +565,6 @@ setMethod( as.numeric(x$n_variants) } -# Per-gene min Bonferroni-adjusted p (original + filtered). The upstream p-value -# pre-filter always retains the global-min variant, so the min over the entry is -# the exact per-gene min. Returns list(orig, filt). -# @noRd # One gene's Bonferroni-adjusted minimum p-value, before and after the # variant filter. NA on either side means the gene had nothing to adjust. # @noRd @@ -606,6 +602,10 @@ setMethod( ) } +# Per-gene min Bonferroni-adjusted p (original + filtered). The upstream p-value +# pre-filter always retains the global-min variant, so the min over the entry is +# the exact per-gene min. Returns list(orig, filt). +# @noRd .qapBonferroniPerGene <- function( x, nVar, diff --git a/R/qtlEnrichmentPipeline.R b/R/qtlEnrichmentPipeline.R index fbf510f16..ff8a69d72 100644 --- a/R/qtlEnrichmentPipeline.R +++ b/R/qtlEnrichmentPipeline.R @@ -80,30 +80,33 @@ QtlEnrichmentParam <- function( #' @section Inputs: #' \itemize{ #' \item \code{gwasFineMappingResult}: the genome-wide outcome collection. -#' Each entry's \code{FineMappingRow$trimmedFit} must carry a \code{pip} +#' Each row's stored fit (\code{\link{susieFit}}) must carry a \code{pip} #' vector. One PIP vector is built per outcome trait -- keyed by #' \code{study} for a GWAS collection (one trait per study) and by #' (\code{study}, \code{context}, \code{trait}) for a QTL one, since a #' variant's PIP differs between molecular traits and pooling them would #' collide. -#' \item \code{qtlFineMappingResult}: the annotation collection. Each entry's -#' \code{trimmedFit} must carry \code{alpha}, \code{pip}, and +#' \item \code{qtlFineMappingResult}: the annotation collection. Each row's +#' stored fit must carry \code{alpha}, \code{pip}, and #' prior-variance fields (\code{V}). Its region fits are pooled per #' (\code{study}, \code{context}) -- per \code{study} alone for a GWAS #' collection, which has no context axis. #' } #' -#' @section LD-sketch compatibility check: A GWAS outcome collection must have -#' a non-NULL \code{ldSketch} (it should be RSS-derived). Where both sides -#' carry one, the two must come from the same reference panel --- same -#' samples, same allele orientation on the variants they share --- but need -#' NOT carry the same variants: QC-ing the two sides separately trims each -#' sketch to its own surviving variants, and a partial overlap only warns. A +#' @section LD reference compatibility check: A GWAS outcome collection +#' must have a non-NULL \code{ldReference} (it should be RSS-derived). Where +#' both sides carry one, the two must be the same LD reference. +#' The two must be the same kind of reference. Two genotype panels must +#' share their samples and allele orientation; two \code{LdMatrix} objects +#' must agree on the LD they share; two \code{GenotypeMixture} objects must +#' average the same panels with the same weights. They need NOT carry the +#' same variants: QC-ing the two sides separately trims each reference to +#' its own surviving variants, and a partial overlap only warns. A #' \code{NULL} on either side (an individual-level fit) skips the check. #' #' @param gwasFineMappingResult The outcome side; see above. #' @param qtlFineMappingResult The annotation side; see above. -#' @param methodArgs The estimator's algorithmic settings, built with +#' @param qtlEnrichmentParam The estimator's algorithmic settings, built with #' \code{\link{QtlEnrichmentParam}}: \code{numGwas}, \code{piQtl}, #' \code{lambda}, \code{impN}, \code{doubleShrinkage} and #' \code{besselCorrection}. A bare list is refused, since it cannot be @@ -147,12 +150,12 @@ QtlEnrichmentParam <- function( qtlEnrichmentPipeline <- function( gwasFineMappingResult, qtlFineMappingResult, - methodArgs = QtlEnrichmentParam(), + qtlEnrichmentParam = QtlEnrichmentParam(), numThreads = 1L, seed = NULL, verbose = TRUE ) { - .assertMethodParam(methodArgs, "QtlEnrichmentParam", "methodArgs") + .assertMethodParam(qtlEnrichmentParam, "QtlEnrichmentParam") .enrValidateInputs(gwasFineMappingResult, qtlFineMappingResult) prep <- .enrPrepare(gwasFineMappingResult, qtlFineMappingResult) gwasTuples <- prep$gwasTuples @@ -164,7 +167,7 @@ qtlEnrichmentPipeline <- function( gwasPipByTuple = prep$gwasPipByTuple, qtlRegionsByTuple = prep$qtlRegionsByTuple, alignedByTuple = alignedByTuple, - methodArgs = methodArgs, + qtlEnrichmentParam = qtlEnrichmentParam, numThreads = numThreads, seed = seed, verbose = verbose, @@ -186,22 +189,22 @@ qtlEnrichmentPipeline <- function( fmrClasses <- c("GwasFineMappingResult", "QtlFineMappingResult") assertMultiClass(gwasFineMappingResult, fmrClasses) assertMultiClass(qtlFineMappingResult, fmrClasses) - outcomeLd <- ldSketch(gwasFineMappingResult) + outcomeLd <- ldReference(gwasFineMappingResult) if ( is.null(outcomeLd) && methods::is(gwasFineMappingResult, "GwasFineMappingResult") ) { msg <- glue( - "qtlEnrichmentPipeline: the GWAS FineMappingResult must have a ", - "non-NULL ldSketch (it should be RSS-derived)." + "qtlEnrichmentPipeline: the GwasFineMappingResult must have a ", + "non-NULL ldReference (it should be RSS-derived)." ) abort(msg) } # Lenient rather than qtl-required: a QTL outcome side may be an # individual-level fit carrying no panel, and the requirement above already # covers the RSS-derived GWAS case. - .requireMatchingLdSketches( - ldSketch(qtlFineMappingResult), + .requireMatchingLdRefs( + ldReference(qtlFineMappingResult), outcomeLd, pipelineName = "qtlEnrichmentPipeline", nullPolicy = "lenient" @@ -343,7 +346,7 @@ qtlEnrichmentPipeline <- function( gwasPipByTuple, qtlRegionsByTuple, alignedByTuple = alignedByTuple, - methodArgs, + qtlEnrichmentParam, numThreads, seed, verbose, @@ -369,7 +372,7 @@ qtlEnrichmentPipeline <- function( gwasPip = gwasPip, qtlRegionsByTuple = qtlRegionsByTuple, alignedByTuple = alignedByTuple, - methodArgs = methodArgs, + qtlEnrichmentParam = qtlEnrichmentParam, numThreads = numThreads, seed = seed, verbose = verbose, @@ -389,7 +392,7 @@ qtlEnrichmentPipeline <- function( gwasPip, qtlRegionsByTuple, alignedByTuple = alignedByTuple, - methodArgs, + qtlEnrichmentParam, numThreads, seed, verbose, @@ -412,7 +415,7 @@ qtlEnrichmentPipeline <- function( gwasPip, k, alignedByTuple = alignedByTuple, - methodArgs = methodArgs, + qtlEnrichmentParam = qtlEnrichmentParam, numThreads = numThreads, seed = seed, verbose = verbose, @@ -457,7 +460,7 @@ qtlEnrichmentPipeline <- function( gwasPip, k, alignedByTuple, - methodArgs, + qtlEnrichmentParam, numThreads, seed, verbose, @@ -473,7 +476,7 @@ qtlEnrichmentPipeline <- function( cnd_signal(aligned) } # alignNames = FALSE reuses the per-tuple alignment; it is the - # pipeline's own invariant, which is why `methodArgs` cannot + # pipeline's own invariant, which is why `qtlEnrichmentParam` cannot # carry it -- QtlEnrichmentParam() has no such formal. exec( qtlEnrichment, @@ -483,7 +486,7 @@ qtlEnrichmentPipeline <- function( seed = seed, verbose = verbose, alignNames = FALSE, - !!!methodArgs + !!!qtlEnrichmentParam ) }, error = function(cnd) { @@ -545,14 +548,6 @@ qtlEnrichmentPipeline <- function( map(regions, .enrAlignRegion, unionGwasNames = unionGwasNames) } -# Build a named outcome PIP vector for one trait. Walks every row of the -# collection carrying that identity, extracts the per-row pip from each -# FineMappingRow, and concatenates with variant-id names. Errors if any single -# variant appears with conflicting PIP values across rows -- which is why the -# identity is the full trait tuple: pooling two molecular traits of one study -# would collide on every variant they share. -#' @importFrom dplyr add_count -#' @noRd # One row's PIP vector keyed by variant id, or NULL when the row carries no # fit, no PIPs, or ids that do not line up with them. # @noRd @@ -570,6 +565,14 @@ qtlEnrichmentPipeline <- function( set_names(pip, as.character(ids)) } +# Build a named outcome PIP vector for one trait. Walks every row of the +# collection carrying that identity, extracts the per-row pip from each +# FineMappingRow, and concatenates with variant-id names. Errors if any single +# variant appears with conflicting PIP values across rows -- which is why the +# identity is the full trait tuple: pooling two molecular traits of one study +# would collide on every variant they share. +#' @importFrom dplyr add_count +#' @noRd .enrBuildGwasPipVector <- function(gwasFmr, ident) { idx <- .enrMatchRows(gwasFmr, ident) if (length(idx) == 0L) { @@ -662,16 +665,16 @@ qtlEnrichmentPipeline <- function( # Project qtlEnrichment()'s output onto the columns this pipeline publishes. # # The field names are the estimator's own, written in src/qtl_enrichment.h and -# shared verbatim with upstream fastenloc's enloc.enrich.out, so they are +# shared verbatim with the original fastenloc's enloc.enrich.out, so they are # matched literally rather than guessed at. The shrinkage estimates are the -# ones reported, matching upstream, which likewise switches to the shrunk a1 -# before deriving the coloc priors. +# ones reported, matching the original fastenloc, which likewise switches to +# the shrunk a1 before deriving the coloc priors. # -# `enrichment` is expm1 of the log-odds a1 rather than a1 itself, because -# colocPipeline consumes it as `p12 * (1 + enrichment)`, which is then exactly -# the enloc-adjusted prior `p12 * exp(a1)` -- and leaves p12 untouched for an -# unenriched annotation (a1 = 0). expm1 is bounded below by -1, so a depleted -# annotation shrinks p12 towards 0 rather than past it. +# `enrichment` is expm1 of the log-odds a1 rather than a1 itself: the +# relative change in an annotated variant's odds of being a QTL, 0 for an +# unenriched annotation and bounded below by -1. colocPipeline reports it +# beside each pair; its priors are the coloc p1 / p2 / p12 the estimator +# derives (colocP1, colocP2, colocP12), not a rescaling of this column. # @noRd .enrFlattenEnrichment <- function(enr) { if (!is.list(enr) || !is_in("Enrichment (w/ shrinkage)", names(enr))) { @@ -728,7 +731,7 @@ qtlEnrichmentPipeline <- function( # ----------------------------------------------------------------------------- # Per-(GWAS, QTL-region-list) enrichment estimator. Called per-(gwasStudy, # qtlContext) pair by qtlEnrichmentPipeline above. Uses the fastenloc-style -# C++ kernel (qtlEnrichmentRcpp) under the hood. +# C++ kernel (qtlEnrichmentCpp) under the hood. # ============================================================================= #' @title Implementation of enrichment analysis described in #' https://doi.org/10.1371/journal.pgen.1006646 @@ -785,7 +788,7 @@ qtlEnrichmentPipeline <- function( #' \code{sd (w/ shrinkage)}, \code{Alternative (coloc) p1} / \code{p2} / #' \code{p12} and \code{Effective MI rounds} -- plus #' \code{unused_xqtl_variants}, the QTL variants of each region that no GWAS -#' variant matched. The names are upstream fastenloc's; +#' variant matched. The names are the original fastenloc's; #' \code{\link{qtlEnrichmentPipeline}} is what renames them to a tidy table. #' #' @examples @@ -848,7 +851,7 @@ qtlEnrichment <- function( unmatchedVariants <- map(aligned, "unmatched_variants") susieQtlRegions <- map(aligned, .enrStripUnmatched) # cpp11 requires exact integer types for int parameters. - en <- qtlEnrichmentRcpp( + en <- qtlEnrichmentCpp( rGwasPip = gwasPip, rQtlSusieFit = susieQtlRegions, piGwas = piGwas, diff --git a/R/qtlSumStats.R b/R/qtlSumStats.R index fabac26a1..ac68c7b93 100644 --- a/R/qtlSumStats.R +++ b/R/qtlSumStats.R @@ -3,7 +3,7 @@ # ----------------------------------------------------------------------------- # DFrame-subclass collection keyed by the identity tuple (study, context, # trait). Each row holds a per-tuple GRanges of summary statistics -# (variant_id + per-variant Z/N/MAF mcols). Class-level slots ldSketch +# (variant_id + per-variant Z/N/MAF mcols). Class-level slots ldReference # (a GenotypeHandle for the LD reference) + genome (the genome build) # apply uniformly across rows. Built-in qcInfo slot tracks which # summaryStatsQc() passes have been run. @@ -19,7 +19,7 @@ NULL #' Z / N / MAF mcols. #' @details Required columns: \code{study}, \code{context} and \code{trait}; #' the 3-tuple is unique. The class-level slots inherited from -#' \code{\linkS4class{SumStatsBase}} -- \code{ldSketch}, \code{genome} and +#' \code{\linkS4class{SumStatsBase}} -- \code{ldReference}, \code{genome} and #' \code{qcInfo} -- apply uniformly to every row, and \code{qcInfo} records #' which \code{\link{summaryStatsQc}} passes have run. #' @seealso \code{\link{QtlSumStats}} for the constructor and @@ -97,7 +97,7 @@ NULL #' @title Create a QtlSumStats Collection Object #' @description Construct a \code{QtlSumStats} S4 DFrame-subclass collection #' from per-tuple vectors and a list of \code{GRanges} entries (one per -#' tuple), plus a single LD sketch handle and a single genome build that apply +#' tuple), plus a single LD reference and a single genome build that apply #' to the whole collection. Each \code{GRanges} entry must carry per-variant #' statistics in its mcols (\code{SNP}, \code{A1}, \code{A2}, \code{Z}, #' \code{N}; plus optional \code{MAF}, \code{INFO}, \code{BETA}, \code{SE}, @@ -109,9 +109,10 @@ NULL #' Same length as \code{study}, \code{context}, and \code{trait}. #' @param genome Single character string giving the genome build (e.g., #' \code{"hg19"}, \code{"hg38"}). Uniform across the collection because all -#' entries share the same LD sketch. -#' @param ldSketch A genotype panel (see \code{\link{readGenotypes}}) -#' carrying the LD reference. +#' entries share the same LD reference. +#' @param ldReference The LD reference: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. #' @param varY Optional numeric vector of per-tuple phenotype variances #' (\code{NA_real_} entries allowed). #' @param nSample Optional per-tuple total sample size (numeric; default @@ -141,7 +142,7 @@ NULL #' S4Vectors::mcols(gr) <- S4Vectors::DataFrame(SNP = paste0("rs", 1:3), #' A1 = "A", A2 = "G", Z = rnorm(3), N = 100L) #' QtlSumStats(studyName = "s1", context = "brain", trait = "g1", entry = -#' list(gr), genome = "hg38", ldSketch = panel) +#' list(gr), genome = "hg38", ldReference = panel) #' @export QtlSumStats <- function( studyName, @@ -149,7 +150,7 @@ QtlSumStats <- function( trait, entry, genome, - ldSketch = NULL, + ldReference = NULL, varY = NA_real_, nSample = NULL, qcInfo = list(), @@ -190,7 +191,7 @@ QtlSumStats <- function( GenomicRanges::GRangesList(split$entry), value = md[split$fromIdx, , drop = FALSE] ) - .sumStatsNewValidated("QtlSumStats", grl, ldSketch, genome, qcInfo) + .sumStatsNewValidated("QtlSumStats", grl, ldReference, genome, qcInfo) } # Validate genome / entry / length consistency and recycle varY. Returns the @@ -200,7 +201,7 @@ QtlSumStats <- function( if (length(genome) != 1L) { msg <- glue( "`genome` must be a single character string (one build per ", - "collection, because all entries share the LD sketch)." + "collection, because all entries share the LD reference)." ) abort(msg) } @@ -523,21 +524,17 @@ setMethod("show", "QtlSumStats", function(object) { .trim = FALSE )) } - ld <- ldSketch(object) + ld <- ldReference(object) ldSrc <- if (is.null(ld)) { "none (LD-free)" } else { - .ldSketchLabel(ld) + .ldRefLabel(ld) } - cat(glue(" LD sketch: {ldSrc}\n", .trim = FALSE)) + cat(glue(" LD reference: {ldSrc}\n", .trim = FALSE)) }) # ---- map/apply helpers (lambda-free callbacks) --------------------------- -# Column `cn` of `object`, via `[[` (preserves the required `entry` column that -# `object[, cn]` would drop). -# @noRd - # Append tss_distance / tes_distance mcols to entry `i` from traitPos[i] (no-op # when the entry is empty or the trait position is unset). Existing distance # mcols are preserved. @@ -573,10 +570,6 @@ setMethod("show", "QtlSumStats", function(object) { ) } -# Entry `i` of a SumStats collection restricted to chromosome `chrName`. Shared -# by the QtlSumStats and GwasSumStats subsetChr methods. -# @noRd - #' Combine QtlSumStats collections #' #' Row-bind two or more \code{\link{QtlSumStats}} collections into one -- e.g. @@ -587,14 +580,14 @@ setMethod("show", "QtlSumStats", function(object) { #' The three collection-level slots are merged rather than taken from the first #' input, on the same rules as \code{\link{combineGwasSumStats}}: \code{genome} #' and the \code{\link{summaryStatsQc}} options must agree, the per-element -#' \code{qcInfo$entryAudit} concatenates in element order, and the LD sketches -#' union over the shared genotype handle (all-\code{NULL} stays \code{NULL}, -#' which is the usual case for individual-level QTL data). +#' \code{qcInfo$entryAudit} concatenates in element order, and the LD +#' references combine the same way (all-\code{NULL} stays \code{NULL}, which +#' is the usual case for individual-level QTL data). #' #' @param ... Two or more \code{QtlSumStats} objects, or a single \code{list} #' of them. -#' @param ldSketch Optional genotype panel (see \code{\link{readGenotypes}}) to -#' attach to the combined collection, overriding the unioned one. Default +#' @param ldReference Optional genotype panel (see \code{\link{readGenotypes}}) +#' to attach to the combined collection, overriding the unioned one. Default #' \code{NULL}. #' @return A single combined \code{QtlSumStats}. #' @seealso \code{\link{combineGwasSumStats}}, @@ -603,7 +596,7 @@ setMethod("show", "QtlSumStats", function(object) { #' data(qtlSumStatsExample) #' combineQtlSumStats(qtlSumStatsExample) #' @export -combineQtlSumStats <- function(..., ldSketch = NULL) { +combineQtlSumStats <- function(..., ldReference = NULL) { parts <- .asCombineList( list(...), "QtlSumStats", @@ -612,5 +605,5 @@ combineQtlSumStats <- function(..., ldSketch = NULL) { if (length(parts) == 1L) { return(parts[[1L]]) } - .rbindSumStats(parts, ldSketch, "combineQtlSumStats") + .rbindSumStats(parts, ldReference, "combineQtlSumStats") } diff --git a/R/regularizedRegressionWrappers.R b/R/regularizedRegressionWrappers.R index 4c880976b..33de5fc23 100644 --- a/R/regularizedRegressionWrappers.R +++ b/R/regularizedRegressionWrappers.R @@ -2,23 +2,35 @@ NULL #' Extract weights from mr.ash.rss (susieR) -#' @param stat A list of summary statistics with elements \code{b} (effect -#' sizes), \code{seb} (standard errors) and \code{n} (per-variant sample -#' sizes). -#' @param LD Numeric LD (correlation) matrix aligned to the variants in -#' \code{stat}. -#' @param varY Numeric. Variance of the phenotype. -#' @param sigma2E Numeric. Residual error variance estimate. -#' @param s0 Numeric vector of prior mixture standard deviations (the sigma0 -#' grid). -#' @param w0 Numeric vector of prior mixture weights (summing to 1). -#' @param z Optional numeric vector of z-scores; defaults to \code{numeric(0)} -#' (derived from \code{stat}). +#' @param stat A list of summary statistics: \code{n} (per-variant sample +#' sizes), \code{variantNames}, and the marginal effects as \code{z}, or as +#' \code{b} with \code{seb}. +#' @param ldReference The LD reference the statistics' variants are matched +#' to by \code{stat$variantNames}: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. \code{mr.ash.rss} takes an LD matrix, which is +#' formed from the reference over these variants. +#' @param varY Variance of the phenotype, or \code{NULL} (default) to fit on +#' the standardized scale. Used only with \code{b} and \code{seb}. +#' @param sigma2E Initial residual variance, or \code{NULL} (default) for +#' \code{mr.ash.rss}'s own. +#' @param s0 Prior variances of the mixture components, relative to the +#' residual variance, or \code{NULL} (default) for the grid +#' \code{susieR::mr.ash} uses by default, built for these statistics. +#' @param w0 Prior mixture weights (summing to 1), or \code{NULL} (default) +#' for equal weights. +#' @param fitRetention How much of the fit is kept as the \code{"fit"} +#' attribute of the returned weights: \code{"none"} (default) attaches +#' nothing, \code{"slim"} and \code{"full"} attach the fitted +#' \code{mr.ash.rss} model. This engine keeps no trimmable intermediates, +#' so the two retaining levels behave alike here. #' @param methodArgs Options forwarded to \code{susieR::mr.ash.rss}, built -#' with \code{\link{MrashOptions}}. A bare list is refused: it cannot be +#' with \code{\link{MrashRssOptions}}. A bare list is refused: it cannot be #' checked against the engine, so a misspelled option would be #' silently ignored. -#' @return A numeric vector of the posterior mean of the coefficients. +#' @return A numeric vector of the posterior mean of the coefficients. Unless +#' \code{fitRetention = "none"}, the fitted model is attached as attribute +#' \code{"fit"}. #' @importFrom susieR mr.ash.rss #' @examples #' data(eqtlRegionExample) @@ -28,41 +40,78 @@ NULL #' coef(summary(lm(y ~ X[, j])))[2, 1:2] #' }) #' stat <- list(b = vapply(ss, `[`, numeric(1), 1L), -#' seb = vapply(ss, `[`, numeric(1), 2L), n = rep(nrow(X), ncol(X))) -#' mrashRssWeights(stat, cor(X), varY = var(y), sigma2E = var(y), -#' s0 = c(0, 0.1, 0.5), w0 = c(0.8, 0.1, 0.1)) -#' @importFrom checkmate assertList assertNumeric +#' seb = vapply(ss, `[`, numeric(1), 2L), n = rep(nrow(X), ncol(X)), +#' variantNames = colnames(X)) +#' mrashRssWeights(stat, LdMatrix(cor(X))) +#' @importFrom checkmate assertList #' @export mrashRssWeights <- function( stat, - LD, - varY, - sigma2E, - s0, - w0, - z = numeric(0), + ldReference, + varY = NULL, + sigma2E = NULL, + s0 = NULL, + w0 = NULL, + fitRetention = c("none", "slim", "full"), methodArgs = MrashRssOptions() ) { + fitRetention <- arg_match(fitRetention) .assertMethodOptions(methodArgs, "MrashRssOptions", "methodArgs") assertList(stat) - assertNumeric(z) + R <- .rssLdMatrix(stat, ldReference, "mrashRssWeights") + n <- median(stat$n) + shat <- as.numeric(stat$seb %||% numeric(0)) + z <- .mrashRssZ(stat) + s0 <- s0 %||% .mrashRssDefaultGrid(n, z, shat, varY) callArgs <- list_modify( list( - bhat = stat$b, - shat = stat$seb, + bhat = as.numeric(stat$b %||% numeric(0)), + shat = shat, z = z, - R = LD, + R = R, var_y = varY, - n = median(stat$n), + n = n, sigma2_e = sigma2E, s0 = s0, - w0 = w0 + # A copy: mr.ash.rss() writes its fitted weights over the vector + # it is given, which would otherwise be the caller's. + w0 = (w0 %||% rep(1 / length(s0), length(s0))) + 0 ), !!!methodArgs ) model <- exec(mr.ash.rss, !!!callArgs) + if (identical(fitRetention, "none")) { + return(model$mu1) + } + `attr<-`(model$mu1, "fit", model) +} + +# The z-scores mr.ash.rss() fits: the statistics' own, or b / seb. +# @noRd +.mrashRssZ <- function(stat) { + hasBetaSe <- !is.null(stat$b) && !is.null(stat$seb) + z <- stat$z %||% (if (hasBetaSe) stat$b / stat$seb) + if (is.null(z)) { + abort("mrashRssWeights: `stat` must carry `z`, or `b` with `seb`.") + } + as.numeric(z) +} - return(model$mu1) +# susieR::mr.ash()'s own default prior grid, (2^((k - 1) / 25) - 1)^2 scaled +# by n / median(w) with w the columns' sums of squares, for the sufficient +# statistics mr.ash.rss() builds from these: w = n - 1 on the standardized +# scale it fits without both a finite varY and seb, and otherwise +# varY * adj / seb^2, with adj = (n - 1) / (z^2 + n - 2) the shrinkage it +# applies. +# @noRd +.mrashRssDefaultGrid <- function(n, z, shat, varY) { + unstandardized <- length(shat) > 0L && !is.null(varY) && is.finite(varY) + w <- if (unstandardized) { + varY * (n - 1) / (z^2 + n - 2) / shat^2 + } else { + rep(n - 1, length(z)) + } + (2^((0:24) / 25) - 1)^2 / median(w) * n } #' PRS-CS: a polygenic prediction method that infers posterior SNP effect sizes @@ -73,6 +122,11 @@ mrashRssWeights <- function( #' panel and infers posterior SNP effect sizes using Bayesian regression with #' continuous shrinkage priors. #' +#' Internal: \code{\link{prsCsWeights}} is the public interface. It forwards +#' the arguments below other than \code{bhat}, \code{R} and \code{n} through +#' \code{\link{PrsCsOptions}}, and with \code{fitRetention} returns this +#' function's list as the \code{"fit"} attribute of its weights. +#' #' @param bhat A vector of marginal effect sizes. #' @param R The LD correlation matrix (a single matrix over the analysed #' window), as in \code{susieR::susie_rss()}. @@ -93,48 +147,7 @@ mrashRssWeights <- function( #' estimates of SNP effect sizes. - psiEst: Posterior estimates of psi #' (shrinkage parameters). - sigmaEst: Posterior estimate of the residual #' variance. - phiEst: Posterior estimate of the global shrinkage parameter. -#' @examples -#' # Generate example data -#' set.seed(985115) -#' n <- 350 -#' p <- 16 -#' sigmasq_error <- 0.5 -#' zeroes <- rbinom(p, 1, 0.6) -#' beta.true <- rnorm(p, 1, sd = 4) -#' beta.true[zeroes] <- 0 -#' -#' X <- cbind(matrix(rnorm(n * p), nrow = n)) -#' X <- scale(X, center = TRUE, scale = FALSE) -#' y <- X %*% matrix(beta.true, ncol = 1) + rnorm(n, 0, sqrt(sigmasq_error)) -#' y <- scale(y, center = TRUE, scale = FALSE) -#' -#' # Calculate sufficient statistics -#' XtX <- t(X) %*% X -#' Xty <- t(X) %*% y -#' yty <- t(y) %*% y -#' -#' # Set the prior -#' K <- 9 -#' sigma0 <- c(0.001, .1, .5, 1, 5, 10, 20, 30, .005) -#' omega0 <- rep(1 / K, K) -#' -#' # Calculate summary statistics -#' b.hat <- sapply(1:p, function(j) { -#' summary(lm(y ~ X[, j]))$coefficients[-1, 1] -#' }) -#' s.hat <- sapply(1:p, function(j) { -#' summary(lm(y ~ X[, j]))$coefficients[-1, 2] -#' }) -#' R.hat <- cor(X) -#' var_y <- var(y) -#' sigmasq_init <- 1.5 -#' -#' # Run PRS CS -#' maf <- rep(0.5, length(b.hat)) # fake MAF -#' out <- prsCs(b.hat, R.hat, n, maf = maf) -#' # In sample prediction correlations -#' cor(X %*% out$betaEst, y) # 0.9944553 -#' @export +#' @keywords internal prsCs <- function( bhat, R, @@ -149,29 +162,63 @@ prsCs <- function( verbose = FALSE, seed = NULL ) { - # Shared LD-matrix validation, then the prsCs-specific maf length check. .rssValidateInputs(bhat, R, n) + .prsCsRun( + bhat, + list(R = R), + n, + a, + b, + phi, + maf, + nIter, + nBurnin, + thin, + verbose, + seed + ) +} + +# PRS-CS on an LD source (.ldSourceRows()): the sampler that factors R for a +# matrix, or for a factor the one that never forms R (prscs_mcmc.h), which +# draws differently from the same posterior. +# @noRd +.prsCsRun <- function( + bhat, + ld, + n, + a, + b, + phi, + maf, + nIter, + nBurnin, + thin, + verbose, + seed +) { if (!is.null(maf) && length(bhat) != length(maf)) { abort("The length of 'bhat' must be the same as 'maf'.") } - - # Run PRS-CS # cpp11 requires exact integer types for int parameters - result <- prsCsRcpp( + settings <- list( a = a, b = b, phi = phi, bhat = bhat, maf = maf, n = as.integer(n), - ldBlk = list(blk1 = R), nIter = as.integer(nIter), nBurnin = as.integer(nBurnin), thin = as.integer(thin), verbose = verbose, seed = seed ) - + result <- if (is.null(ld$F)) { + exec(prsCsCpp, !!!settings, ldBlk = list(blk1 = ld$R)) + } else { + exec(prsCsFactorCpp, !!!settings, factorR = ld$F) + } # Return the result as a list (camelCase to match the rest of the package # API). list( @@ -183,15 +230,30 @@ prsCs <- function( } #' Extract weights from prsCs function -#' @param stat A list of summary statistics with elements \code{b} (effect -#' sizes) and \code{n} (per-variant sample sizes). -#' @param LD Numeric LD (correlation) matrix aligned to the variants in -#' \code{stat}. -#' @param methodArgs Options forwarded to \code{prsCs}, built -#' with \code{\link{PrsCsOptions}}. A bare list is refused: it cannot be -#' checked against the engine, so a misspelled option would be +#' @param stat A list of summary statistics: \code{n} (per-variant sample +#' sizes) and the marginal effects as \code{cor} (standardized), \code{z}, +#' \code{b} with \code{seb}, or \code{b} alone, taken as already +#' standardized. The engine receives the standardized effects z / sqrt(n), +#' with n the median of \code{n}. +#' @param ldReference The LD reference the statistics' variants are matched +#' to by \code{stat$variantNames}: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. A genotype reference's LD is used through its +#' standardized genotypes, never formed as a matrix. +#' @param fitRetention How much of the fit is kept as the \code{"fit"} +#' attribute of the returned weights: \code{"none"} (default) attaches +#' nothing, \code{"slim"} and \code{"full"} attach the sampler's posterior +#' estimates: \code{betaEst}, the local shrinkage parameters +#' \code{psiEst}, the residual variance \code{sigmaEst} and the global +#' shrinkage \code{phiEst}. This engine keeps no trimmable intermediates, +#' so the two retaining levels behave alike here. +#' @param methodArgs Options forwarded to the sampler, \code{\link{prsCs}}, +#' built with \code{\link{PrsCsOptions}}. A bare list is refused: it cannot +#' be checked against the engine, so a misspelled option would be #' silently ignored. -#' @return A numeric vector of the posterior SNP coefficients. +#' @return A numeric vector of the posterior SNP coefficients. Unless +#' \code{fitRetention = "none"}, the sampler's estimates are attached as +#' attribute \code{"fit"}. #' @examples #' data(eqtlRegionExample) #' X <- eqtlRegionExample$X[, 1:30] @@ -201,20 +263,51 @@ prsCs <- function( #' stat <- list( #' b = vapply(ss, `[`, numeric(1), 1L), #' seb = vapply(ss, `[`, numeric(1), 2L), -#' n = rep(nrow(X), ncol(X)) +#' n = rep(nrow(X), ncol(X)), +#' variantNames = colnames(X) +#' ) +#' w <- prsCsWeights( +#' stat, +#' LdMatrix(cor(X)), +#' fitRetention = "slim", +#' methodArgs = PrsCsOptions(maf = rep(0.3, ncol(X))) #' ) -#' LD <- cor(X) -#' prsCsWeights(stat, LD, methodArgs = PrsCsOptions(maf = rep(0.3, ncol(X)))) +#' attr(w, "fit")$phiEst #' @export -prsCsWeights <- function(stat, LD, methodArgs = PrsCsOptions()) { +prsCsWeights <- function( + stat, + ldReference, + fitRetention = c("none", "slim", "full"), + methodArgs = PrsCsOptions() +) { + fitRetention <- arg_match(fitRetention) .assertMethodOptions(methodArgs, "PrsCsOptions", "methodArgs") - callArgs <- list_modify( - list(bhat = stat$b, R = LD, n = median(stat$n)), - !!!methodArgs + ld <- .rssLdSource(stat, ldReference, "prsCsWeights") + n <- median(stat$n) + bhat <- .rssMarginalCor(stat, n = n, p = .ldSourceSize(ld)) + .rssValidateSource(bhat, ld, n) + settings <- list_modify( + .formalDefaults( + prsCs, + c( + "a", + "b", + "phi", + "maf", + "nIter", + "nBurnin", + "thin", + "verbose", + "seed" + ) + ), + !!!as.list(methodArgs) ) - model <- exec(prsCs, !!!callArgs) - - return(model$betaEst) + model <- exec(.prsCsRun, bhat = bhat, ld = ld, n = n, !!!settings) + if (identical(fitRetention, "none")) { + return(model$betaEst) + } + `attr<-`(model$betaEst, "fit", model) } #' SDPR (Summary-Statistics-Based Dirichelt Process Regression for Polygenic @@ -224,6 +317,11 @@ prsCsWeights <- function(stat, LD, methodArgs = PrsCsOptions()) { #' Markov Chain Monte Carlo (MCMC) for estimating effect sizes and heritability #' based on summary statistics and reference LD matrices. #' +#' Internal: \code{\link{sdprWeights}} is the public interface. It forwards +#' the arguments below other than \code{bhat}, \code{R} and \code{n} through +#' \code{\link{SdprOptions}}, and with \code{fitRetention} returns this +#' function's list as the \code{"fit"} attribute of its weights. +#' #' @param bhat A vector of marginal beta values for each SNP. #' @param R The LD correlation matrix (a single matrix over the analysed #' window). @@ -251,56 +349,14 @@ prsCsWeights <- function(stat, LD, methodArgs = PrsCsOptions()) { #' @param seed Integer or \code{NULL}. Random seed for the Gibbs sampler; #' \code{NULL} leaves the RNG state unchanged. #' @return A list with \code{betaEst} (the estimated effect sizes) and -#' \code{h2} (the estimated heritability). \code{sdprWeights()} returns -#' only \code{betaEst}; call this directly when you also want \code{h2}. -#' @examples -#' # Generate example data -#' set.seed(985115) -#' n <- 350 -#' p <- 16 -#' sigmasq_error <- 0.5 -#' zeroes <- rbinom(p, 1, 0.6) -#' beta.true <- rnorm(p, 1, sd = 4) -#' beta.true[zeroes] <- 0 -#' -#' X <- cbind(matrix(rnorm(n * p), nrow = n)) -#' X <- scale(X, center = TRUE, scale = FALSE) -#' y <- X %*% matrix(beta.true, ncol = 1) + rnorm(n, 0, sqrt(sigmasq_error)) -#' y <- scale(y, center = TRUE, scale = FALSE) -#' -#' # Calculate sufficient statistics -#' XtX <- t(X) %*% X -#' Xty <- t(X) %*% y -#' yty <- t(y) %*% y -#' -#' # Set the prior -#' K <- 9 -#' sigma0 <- c(0.001, .1, .5, 1, 5, 10, 20, 30, .005) -#' omega0 <- rep(1 / K, K) -#' -#' # Calculate summary statistics -#' b.hat <- sapply(1:p, function(j) { -#' summary(lm(y ~ X[, j]))$coefficients[-1, 1] -#' }) -#' s.hat <- sapply(1:p, function(j) { -#' summary(lm(y ~ X[, j]))$coefficients[-1, 2] -#' }) -#' R.hat <- cor(X) -#' var_y <- var(y) -#' sigmasq_init <- 1.5 -#' -#' # Run SDPR -#' out <- sdpr(b.hat, R.hat, n) -#' # In sample prediction correlations -#' cor(X %*% out$betaEst, y) # -#' +#' \code{h2} (the estimated heritability). #' @note This function wraps a rewritten and adapted version of the SDPR C++ #' implementation. SDPR is described in Zhou G, Zhao H (2021), "A fast and #' robust Bayesian nonparametric method for prediction of complex traits #' using summary statistics", PLoS Genetics 17(7): e1009697. #' \doi{10.1371/journal.pgen.1009697} #' -#' @export +#' @keywords internal sdpr <- function( bhat, R, @@ -320,12 +376,55 @@ sdpr <- function( verbose = TRUE, seed = NULL ) { - .sdprValidate(bhat, R, n, M, perVariantSampleSize, array) - # The C++ backend takes a block list; wrap the single-window matrix R as one - # block. cpp11 requires exact integer types for int params + sexp vectors. - sdprRcpp( + .rssValidateInputs(bhat, R, n) + .sdprRun( + bhat, + list(R = R), + n, + perVariantSampleSize, + array, + a, + c, + M, + a0k, + b0k, + iter, + burn, + thin, + numThreads, + optLlk, + verbose, + seed + ) +} + +# SDPR on an LD source (.ldSourceRows()): the sampler on the matrix, or the +# same sampler on the low-rank form of SDPR's matrices from a factor of it +# (sdpr_mcmc.h), which draws as the matrix route does without forming R. +# @noRd +.sdprRun <- function( + bhat, + ld, + n, + perVariantSampleSize, + array, + a, + c, + M, + a0k, + b0k, + iter, + burn, + thin, + numThreads, + optLlk, + verbose, + seed +) { + .sdprValidate(M, perVariantSampleSize, array) + # cpp11 requires exact integer types for int params + sexp vectors. + settings <- list( bhatR = bhat, - LD = list(blk1 = R), n = as.integer(n), perVariantSampleSize = perVariantSampleSize, array = if (!is.null(array)) as.integer(array) else NULL, @@ -342,13 +441,18 @@ sdpr <- function( verbose = verbose, seed = seed ) + if (is.null(ld$F)) { + # The C++ backend takes a block list; the window is one block. + return(exec(sdprCpp, !!!settings, LD = list(blk1 = ld$R))) + } + exec(sdprFactorCpp, !!!settings, factorR = ld$F) } -# Validate the SDPR inputs: RSS contract, M >= 4 (SDPR uses M-2 indexing in -# sample_V; M < 4 overflows), positive per-variant N, and array in {0, 1, 2}. +# Validate the SDPR settings: M >= 4 (SDPR uses M-2 indexing in sample_V; +# M < 4 overflows), positive per-variant N, and array in {0, 1, 2}. The RSS +# contract (bhat, LD, n) is checked by the callers. # @noRd -.sdprValidate <- function(bhat, R, n, M, perVariantSampleSize, array) { - .rssValidateInputs(bhat, R, n) +.sdprValidate <- function(M, perVariantSampleSize, array) { if (M < 4) { abort("'M' must be at least 4.") } @@ -366,15 +470,31 @@ sdpr <- function( } #' Extract weights from sdpr function -#' @param stat A list of summary statistics with elements \code{b} (effect -#' sizes) and \code{n} (per-variant sample sizes). -#' @param LD Numeric LD (correlation) matrix aligned to the variants in -#' \code{stat}. -#' @param methodArgs Options forwarded to \code{sdpr}, built -#' with \code{\link{SdprOptions}}. A bare list is refused: it cannot be -#' checked against the engine, so a misspelled option would be +#' @param stat A list of summary statistics: \code{n} (per-variant sample +#' sizes) and the marginal effects as \code{cor} (standardized), \code{z}, +#' \code{b} with \code{seb}, or \code{b} alone, taken as already +#' standardized. The engine receives the standardized effects z / sqrt(n), +#' with n the median of \code{n}. +#' @param ldReference The LD reference the statistics' variants are matched +#' to by \code{stat$variantNames}: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. A genotype reference's LD is used through its +#' standardized genotypes and not formed as a matrix, except under +#' \code{optLlk = 2} when the per-variant sample sizes and arrays are +#' too varied for its low-rank form to pay. +#' @param fitRetention How much of the fit is kept as the \code{"fit"} +#' attribute of the returned weights: \code{"none"} (default) attaches +#' nothing, \code{"slim"} and \code{"full"} attach the sampler's +#' estimates, \code{betaEst} and the heritability \code{h2}. This engine +#' keeps no trimmable intermediates, so the two retaining levels behave +#' alike here. +#' @param methodArgs Options forwarded to the sampler, \code{\link{sdpr}}, +#' built with \code{\link{SdprOptions}}. A bare list is refused: it cannot +#' be checked against the engine, so a misspelled option would be #' silently ignored. -#' @return A numeric vector of the posterior SNP coefficients. +#' @return A numeric vector of the posterior SNP coefficients. Unless +#' \code{fitRetention = "none"}, the sampler's estimates, the heritability +#' among them, are attached as attribute \code{"fit"}. #' @examples #' data(eqtlRegionExample) #' X <- eqtlRegionExample$X[, 1:30] @@ -384,20 +504,51 @@ sdpr <- function( #' stat <- list( #' b = vapply(ss, `[`, numeric(1), 1L), #' seb = vapply(ss, `[`, numeric(1), 2L), -#' n = rep(nrow(X), ncol(X)) +#' n = rep(nrow(X), ncol(X)), +#' variantNames = colnames(X) #' ) -#' LD <- cor(X) -#' sdprWeights(stat, LD) +#' w <- sdprWeights(stat, LdMatrix(cor(X)), fitRetention = "slim") +#' attr(w, "fit")$h2 #' @export -sdprWeights <- function(stat, LD, methodArgs = SdprOptions()) { +sdprWeights <- function( + stat, + ldReference, + fitRetention = c("none", "slim", "full"), + methodArgs = SdprOptions() +) { + fitRetention <- arg_match(fitRetention) .assertMethodOptions(methodArgs, "SdprOptions", "methodArgs") - callArgs <- list_modify( - list(bhat = stat$b, R = LD, n = median(stat$n)), - !!!methodArgs + ld <- .rssLdSource(stat, ldReference, "sdprWeights") + n <- median(stat$n) + bhat <- .rssMarginalCor(stat, n = n, p = .ldSourceSize(ld)) + .rssValidateSource(bhat, ld, n) + settings <- list_modify( + .formalDefaults( + sdpr, + c( + "perVariantSampleSize", + "array", + "a", + "c", + "M", + "a0k", + "b0k", + "iter", + "burn", + "thin", + "numThreads", + "optLlk", + "verbose", + "seed" + ) + ), + !!!as.list(methodArgs) ) - model <- exec(sdpr, !!!callArgs) - - return(model$betaEst) + model <- exec(.sdprRun, bhat = bhat, ld = ld, n = n, !!!settings) + if (identical(fitRetention, "none")) { + return(model$betaEst) + } + `attr<-`(model$betaEst, "fit", model) } @@ -537,10 +688,15 @@ mrmashWeights <- function( #' \code{\link{computeCovDiag}}, or \code{mashr::cov_flash}) to add #' data-driven covariance components alongside the canonical mixture. #' -#' @param stat A list with \code{z} (variants x conditions matrix) and \code{n} -#' (per-context numeric vector or scalar). May also include \code{Bhat}, -#' \code{Shat} matrices. -#' @param LD LD correlation matrix. +#' @param stat A list with \code{z} (variants x conditions matrix), +#' \code{n} (per-context numeric vector or scalar) and \code{variantNames}. +#' May also include \code{Bhat}, \code{Shat} matrices. +#' @param ldReference The LD reference the statistics' variants are matched +#' to by \code{stat$variantNames}: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. \code{mr.mash.rss} takes an LD matrix, which is +#' formed from the reference over these variants; it is not read when +#' \code{mrmashRssFit} is supplied. #' @param mrmashRssFit Optional pre-fitted \code{mr.mash.rss} object; skips #' fitting when supplied. #' @param dataDrivenPriorMatrices Optional list with element \code{U} (list of @@ -579,15 +735,16 @@ mrmashWeights <- function( #' cond <- c("brain", "blood", "muscle") #' zmat <- matrix(rnorm(30 * 3), 30, 3, #' dimnames = list(colnames(X), cond)) -#' stat <- list(z = zmat, n = matrix(nrow(X), 30, 3)) -#' mrmashRssWeights(stat, cor(X), +#' stat <- list(z = zmat, n = matrix(nrow(X), 30, 3), +#' variantNames = colnames(X)) +#' mrmashRssWeights(stat, LdMatrix(cor(X)), #' dataDrivenPriorMatrices = multiTraitData$priorMatrices, #' canonicalPriorMatrices = TRUE) #' @export #' @importFrom checkmate assertFlag assertList mrmashRssWeights <- function( stat, - LD, + ldReference, mrmashRssFit = NULL, dataDrivenPriorMatrices = NULL, canonicalPriorMatrices = TRUE, @@ -606,7 +763,7 @@ mrmashRssWeights <- function( if (is.null(mrmashRssFit)) { mrmashRssFit <- .mrmashRssComputeFit( stat, - LD, + .rssLdMatrix(stat, ldReference, "mrmashRssWeights"), dataDrivenPriorMatrices, canonicalPriorMatrices, S0, @@ -630,11 +787,7 @@ mrmashRssWeights <- function( # @noRd .mrmashRssRequirePackage <- function() { if (!requireNamespace("mr.mashr", quietly = TRUE)) { - msg <- glue( - "Package 'mr.mashr' is required. ", - "is required." - ) - abort(msg) + abort("Package 'mr.mashr' is required for mrmashRssWeights().") } } @@ -713,18 +866,6 @@ mrmashRssWeights <- function( } -# Get a reasonable setting for the standard deviations of the mixture -# components in the mixture-of-normals prior based on the data (X, y). -# Input se is an estimate of the residual *variance*, and n is the -# number of standard deviations to return. This code is adapted from -# the autoselect.mixsd function in the ashr package. -#' @importFrom susieR univariate_regression -initPriorSd <- function(X, y, n = 30) { - res <- univariate_regression(X, y) - smax <- 3 * max(res$betahat) - seq(0, smax, length.out = n) -} - # Identify zero-variance columns of X and warn the caller before they are # dropped. Returns a logical vector of length ncol(X) where TRUE indicates a # column to keep. The downstream solvers in pecotmr's regression wrappers @@ -1002,16 +1143,16 @@ MrashRssOptions <- function(...) { c( bhat = "supplied from the data by the pipeline", shat = "supplied from the data by the pipeline", + z = "supplied from the data by the pipeline", R = "supplied from the data by the pipeline", n = "supplied from the data by the pipeline", - # The engine writes these five in snake_case while the wrapper - # spells them camelCase, so they read as unrelated names; every - # one is still fed straight from a formal. - z = "the wrapper's `z`", - var_y = "the wrapper's `varY`", - sigma2_e = "the wrapper's `sigma2E`", s0 = "the wrapper's `s0`", - w0 = "the wrapper's `w0`" + w0 = "the wrapper's `w0`", + # The engine writes these two in snake_case while the wrapper + # spells them camelCase, so they read as unrelated names; both + # are still fed straight from a formal. + var_y = "the wrapper's `varY`", + sigma2_e = "the wrapper's `sigma2E`" ), "MrashRssOptions" ) @@ -1338,9 +1479,10 @@ lassoWeights <- function(X, y, methodArgs = GlmnetOptions()) { #' Compute Weights Using mr.ash Shrinkage #' #' This function fits the `mr.ash` model (adaptive shrinkage regression) to -#' estimate weights for a given set of predictors and response. It uses optional -#' prior standard deviation initialization and can accept custom initial beta -#' values. +#' estimate weights for a given set of predictors and response. The prior is +#' \code{susieR::mr.ash}'s own default mixture grid unless \code{sa2} is +#' supplied through \code{methodArgs}, and custom initial beta values are +#' accepted. #' #' @examples #' set.seed(1) @@ -1351,14 +1493,11 @@ lassoWeights <- function(X, y, methodArgs = GlmnetOptions()) { #' @importFrom stats predict #' @param X Numeric genotype / design matrix (samples x variants). #' @param y Numeric response (phenotype) vector of length \code{nrow(X)}. -#' @param initPriorSd Logical. Initialize the prior standard-deviation grid -#' from the data. Default \code{TRUE}. #' @param fitRetention How much of the fit is kept as the \code{"fit"} #' attribute of the returned weights: \code{"none"} (default) attaches #' nothing, \code{"slim"} and \code{"full"} attach the fitted model. This #' engine keeps no trimmable intermediates, so the two retaining levels #' behave alike here. -#' Default \code{FALSE}. #' @param methodArgs Options forwarded to \code{susieR::mr.ash}, built #' with \code{\link{MrashOptions}}. A bare list is refused: it cannot be #' checked against the engine, so a misspelled option would be @@ -1367,17 +1506,14 @@ lassoWeights <- function(X, y, methodArgs = GlmnetOptions()) { #' zero-variance columns receive weight 0. Unless #' \code{fitRetention = "none"} the fitted \code{mr.ash} object is #' attached as attribute \code{"fit"}. -#' @importFrom checkmate assertFlag #' @export mrashWeights <- function( X, y, - initPriorSd = TRUE, fitRetention = c("none", "slim", "full"), methodArgs = MrashOptions() ) { .assertMethodOptions(methodArgs, "MrashOptions", "methodArgs") - assertFlag(initPriorSd) fitRetention <- arg_match(fitRetention) keep <- .dropZeroVariance(X, "mrashWeights") XKeep <- X[, keep, drop = FALSE] @@ -1387,15 +1523,7 @@ mrashWeights <- function( as.list(methodArgs), !!!.mrashBetaInit(methodArgs, XKeep, y, X, keep) ) - MrashOptions <- c( - list( - X = XKeep, - y = y, - sa2 = if (initPriorSd) initPriorSd(XKeep, y)^2 else NULL - ), - argsList - ) - fit.mr.ash <- exec(mr.ash, !!!MrashOptions) + fit.mr.ash <- exec(mr.ash, X = XKeep, y = y, !!!argsList) # Zero-variance columns were never fitted and keep a zero weight. eff.wgt <- replace( rep(0, ncol(X)), @@ -2153,6 +2281,12 @@ bayesRWeights <- function( #' Based on Mak et al (2017) "Polygenic scores via penalized regression on #' summary statistics", Genetic Epidemiology 41(6):469-480. #' +#' Internal: \code{\link{lassosumRssWeights}} is the public interface. It +#' runs this over a grid of LD shrinkage values, forwards \code{lambda}, +#' \code{thr} and \code{maxiter} through \code{\link{LassosumOptions}}, and +#' with \code{fitRetention} returns the paths as the \code{"fit"} attribute +#' of its weights. +#' #' @param bhat A vector of marginal effect sizes. #' @param R The LD correlation matrix (a single matrix over the analysed #' window), as in \code{susieR::susie_rss()}. If shrinkage is desired, apply @@ -2172,18 +2306,7 @@ bayesRWeights <- function( #' \item{fbeta}{Full objective value at each lambda.} #' \item{nparams}{Number of non-zero coefficients at each lambda.} #' -#' @examples -#' set.seed(42) -#' p <- 10 -#' n <- 100 -#' bhat <- rnorm(p, sd = 0.1) -#' R <- diag(p) -#' for (i in 1:(p - 1)) { -#' R[i, i + 1] <- 0.3 -#' R[i + 1, i] <- 0.3 -#' } -#' out <- lassosumRss(bhat, R, n) -#' @export +#' @keywords internal #' @importFrom checkmate assertCount assertNumber assertNumeric lassosumRss <- function( bhat, @@ -2193,14 +2316,19 @@ lassosumRss <- function( thr = 1e-4, maxiter = 10000 ) { + .lassosumRssPath(bhat, list(R = R), n, lambda, thr, maxiter) +} + +# lassosumRss() on an LD source: the matrix, or a factor of it with its +# shrinkage, which the factor solver applies without forming R. +# @noRd +.lassosumRssPath <- function(bhat, ld, n, lambda, thr, maxiter) { assertNumeric(lambda, lower = 0, any.missing = FALSE) assertNumber(thr, lower = 0, finite = TRUE) assertCount(maxiter, positive = TRUE) - # cpp11 requires exact integer types; the C++ backend takes a block list, so - # the single-window matrix R is wrapped as one block here. .rssSolvePath( bhat, - R, + ld, n, lambda, .rssLassosumSolve, @@ -2208,25 +2336,49 @@ lassosumRss <- function( ) } -.lassosumCorFromStat <- function(stat, n, p) { +# .lassosumRssPath() with lassosumRss()'s own defaults under `args`, the +# caller's options. A matrix takes exactly the path lassosumRss() does. +# @noRd +.lassosumRssOn <- function(bhat, ld, n, args) { + settings <- list_modify( + .formalDefaults(lassosumRss, c("lambda", "thr", "maxiter")), + !!!args + ) + exec(.lassosumRssPath, bhat = bhat, ld = ld, n = n, !!!settings) +} + +# The values `fn` declares as the defaults of its formals `fmlNames`, +# evaluated as the function itself would evaluate them. The internal LD-source +# paths run lassosumRss(), penalizedRss(), prsCs() and sdpr() on a factor of +# the LD, and take those functions' defaults from here rather than restating +# them. +# @noRd +#' @importFrom rlang fn_fmls fn_env eval_bare +.formalDefaults <- function(fn, fmlNames) { + map(fn_fmls(fn)[fmlNames], eval_bare, env = fn_env(fn)) +} + +# The standardized marginal effects, z / sqrt(n): the scale lassosum, PRS-CS +# and SDPR all fit. Read from `cor` as given, else from `z`, else from +# `b / seb`, which is a z-score whatever scale `b` is on, else from `b` alone, +# taken as already standardized. +.rssMarginalCor <- function(stat, n, p) { corInput <- if (!is.null(stat$cor)) { as.numeric(stat$cor) } else if (!is.null(stat$z)) { as.numeric(stat$z) / sqrt(n) + } else if (!is.null(stat$b) && !is.null(stat$seb)) { + as.numeric(stat$b) / as.numeric(stat$seb) / sqrt(n) } else if (!is.null(stat$b)) { as.numeric(stat$b) } else { - msg <- glue( - "stat must contain one of 'cor', 'z', or 'b' for lassosum ", - "selection." - ) - abort(msg) + abort("stat must contain one of 'cor', 'z', or 'b'.") } if (length(corInput) != p) { nInput <- length(corInput) msg <- glue( - "The length of lassosum input statistics ({nInput}) must ", - "equal nrow(LD) ({p})." + "The length of the input statistics ({nInput}) must equal the ", + "number of LD variants ({p})." ) abort(msg) } @@ -2254,10 +2406,9 @@ lassosumRss <- function( ) } -.lassosumSelectLdQuadratic <- function(candidateBeta, corInput, LD) { - ldBeta <- LD %*% candidateBeta +.lassosumSelectLdQuadratic <- function(candidateBeta, corInput, ld) { bxy <- as.numeric(crossprod(corInput, candidateBeta)) - bxxb <- colSums(candidateBeta * ldBeta) + bxxb <- .ldQuadForms(ld, candidateBeta) positive <- is.finite(bxxb) & bxxb > 0 scores <- replace( rep(-Inf, length(bxy)), @@ -2276,9 +2427,9 @@ lassosumRss <- function( # penalizedRss via .rssSolvePath, prsCs, and sdpr). R is a single LD correlation # matrix over one cis-window, matching susieR::susie_rss; bhat must match # nrow(R). missing() guards turn an omitted R / n into a clear message instead -# of an "argument is missing" error, and propagate through the public wrappers -# (verified two levels deep). Method-specific checks -- prsCs's maf length, -# sdpr's M / perVariantSampleSize / array -- stay in the caller. +# of an "argument is missing" error, and propagate through the solvers' +# callers (verified two levels deep). Method-specific checks -- prsCs's maf +# length, sdpr's M / perVariantSampleSize / array -- stay in the caller. #' @importFrom checkmate assertVector .rssValidateInputs <- function(bhat, R, n) { if (missing(R) || !is.matrix(R)) { @@ -2291,19 +2442,35 @@ lassosumRss <- function( invisible(NULL) } -# Validate (bhat, LD, n), run a decreasing-lambda coordinate-descent sweep via -# `solveFn` (the penalty-specific Rcpp backend), then reorder back to the input +# .rssValidateInputs() for an LD source (.ldSourceRows()): the matrix +# checks for a matrix, and for a factor the same checks against its columns. +# @noRd +.rssValidateSource <- function(bhat, ld, n) { + if (!is.null(ld$R)) { + return(.rssValidateInputs(bhat, ld$R, n)) + } + if (missing(n) || n <= 0) { + abort("Please provide a valid sample size using 'n'.") + } + assertVector(bhat, len = ncol(ld$F)) + invisible(NULL) +} + +# Validate (bhat, ld, n), run a decreasing-lambda coordinate-descent sweep via +# `solveFn` (the penalty-specific C++ solver), then reorder back to the input # lambda order via the inverse permutation and assemble the standard result -# list. Shared by lassosumRss and penalizedRss, which differ only in which Rcpp +# list. Shared by lassosumRss and penalizedRss, which differ only in which C++ # solver they pass as `solveFn` (and penalizedRss's per-penalty gamma default). -.rssSolvePath <- function(bhat, R, n, lambda, solveFn, solveArgs = list()) { - .rssValidateInputs(bhat, R, n) +# `ld` is an LD source: the matrix, or a factor of it with the shrinkage the +# factor solver applies itself. +.rssSolvePath <- function(bhat, ld, n, lambda, solveFn, solveArgs = list()) { + .rssValidateSource(bhat, ld, n) z <- bhat / sqrt(n) order <- order(lambda, decreasing = TRUE) # `solveArgs` rather than `...`: the two solvers take different fixed # argument sets, and both callers know theirs statically, so an unknown # solver argument should be an error here rather than reaching the solver. - solved <- exec(solveFn, z, lambda[order], R, !!!solveArgs) + solved <- exec(solveFn, z, lambda[order], ld, !!!solveArgs) # Reorder back to original lambda order via the inverse permutation. invOrder <- order(order) beta <- solved$beta[, invOrder, drop = FALSE] @@ -2324,44 +2491,42 @@ lassosumRss <- function( # fields apply). Returns list(beta, meta); l0learn's inner lambda0 sweep is # here. # @noRd -.rssFitOne <- function(method, solverInput, LDs, n, sVal, config) { +.rssFitOne <- function(method, solverInput, ld, n, sVal, config) { switch( method, - lassosum = .rssFitLassosum(solverInput, LDs, n, sVal, config), - penalized = .rssFitPenalized(solverInput, LDs, n, sVal, config), - l0learn = .rssFitL0learn(solverInput, LDs, n, sVal, config) + lassosum = .rssFitLassosum(solverInput, ld, n, sVal, config), + penalized = .rssFitPenalized(solverInput, ld, n, sVal, config), + l0learn = .rssFitL0learn(solverInput, ld, n, sVal, config) ) } -# Shared (s, lambda, fbeta) path-metadata frame for a single-path model. +# Shared per-candidate frame for a single-path model: the shrinkage s and +# each lambda, with the solver's objective, loss, convergence flag and number +# of non-zero coefficients there. # @noRd .rssPathMeta <- function(sVal, model) { tibble( s = rep(sVal, length(model$lambda)), lambda = model$lambda, - fbeta = model$fbeta + fbeta = model$fbeta, + loss = model$loss, + conv = model$conv, + nparams = model$nparams ) } -# lassosum shrinkage path for one s. +# lassosum shrinkage path for one s, against `ld` shrunk to that s. # @noRd -.rssFitLassosum <- function(solverInput, LDs, n, sVal, config) { - lsArgs <- c( - list(bhat = solverInput, R = LDs, n = n), - as.list(config$dotArgs) - ) - model <- exec(lassosumRss, !!!lsArgs) +.rssFitLassosum <- function(solverInput, ld, n, sVal, config) { + model <- .lassosumRssOn(solverInput, ld, n, as.list(config$dotArgs)) list(beta = model$beta, meta = .rssPathMeta(sVal, model)) } -# ncvreg-penalized shrinkage path for one s. +# ncvreg-penalized shrinkage path for one s, against `ld` shrunk to that s. # @noRd -.rssFitPenalized <- function(solverInput, LDs, n, sVal, config) { - penArgs <- c( +.rssFitPenalized <- function(solverInput, ld, n, sVal, config) { + settings <- c( list( - bhat = solverInput, - R = LDs, - n = n, penalty = config$penalty, gamma = config$gamma, alpha = config$alpha, @@ -2370,18 +2535,18 @@ lassosumRss <- function( ), as.list(config$dotArgs) ) - model <- exec(penalizedRss, !!!penArgs) + model <- .penalizedRssOn(solverInput, ld, n, settings) list(beta = model$beta, meta = .rssPathMeta(sVal, model)) } # L0Learn path for one s: one penalizedRss fit per lambda0, column-bound. # @noRd -.rssFitL0learn <- function(solverInput, LDs, n, sVal, config) { +.rssFitL0learn <- function(solverInput, ld, n, sVal, config) { fits <- map( config$lambda0, .rssL0LambdaFit, solverInput = solverInput, - LDs = LDs, + ld = ld, n = n, sVal = sVal, config = config @@ -2428,57 +2593,147 @@ lassosumRss <- function( `attr<-`(bestBeta, attrName, selection) } -# Shared scaffold for the RSS shrinkage-grid weight functions -# (lassosumRssWeights / .penalizedRssWeights / l0learnRssWeights). Standardizes -# the stat -> solverInput conversion, the outer LD-shrinkage grid over `s`, the -# candidate accumulation, and the ldQuadratic / minFbeta selection. `method` + -# `config` pick the per-`s` solver (.rssFitOne) and the finalizer -# (.rssFinalize). -# One shrinkage level's fit against the correspondingly shrunk LD. +# The LD the RSS weight methods read, matched to the statistics' variants +# (`stat$variantNames`): a factor of it for a genotype reference, which the +# factor solvers use without forming the p x p matrix, or the matrix an +# LdMatrix holds. Matching, allele flips, standardizing and padding are the +# LD reference layer's own (.ldFactorFromRef(), .ldFromRef()). # @noRd -.rssFitAtS <- function(sVal, method, solverInput, LD, n, p, config) { - .rssFitOne( - method, - solverInput, - (1 - sVal) * LD + sVal * diag(p), - n, - sVal, - config - ) +.rssLdSource <- function(stat, ldReference, label) { + ids <- .rssStatVariantIds(stat, label) + if (.ldIsGenotypeRef(ldReference)) { + return(list(F = .ldFactorFromRef(ldReference, ids, label))) + } + list(R = .ldFromRef(ldReference, ids, label)) +} + +# The LD matrix itself, for the engines that take nothing else (mr.ash.rss, +# mr.mash.rss). +# @noRd +.rssLdMatrix <- function(stat, ldReference, label) { + .ldFromRef(ldReference, .rssStatVariantIds(stat, label), label) +} + +# The variant ids an LD reference is matched on. +# @noRd +.rssStatVariantIds <- function(stat, label) { + ids <- stat$variantNames + if (is.null(ids)) { + abort(glue( + "{label}: `stat` must carry `variantNames`, the variant ids ", + "`ldReference` is matched on." + )) + } + ids +} + +# How many variants the statistics describe, for the weight extractors that +# check a fit against them: their variant ids, all of which must be in +# `ldReference` when one is given, or else their z-scores or effects (rows, +# for a multi-condition matrix). NA when the statistics carry none of these. +# @noRd +.rssVariantCount <- function(stat, ldReference, label) { + if (!is.null(ldReference)) { + ids <- .rssStatVariantIds(stat, label) + .ldFromRefValidate(ldReference, label) + .ldFromRefMatch(ldReference, ids, label, "error") + return(length(ids)) + } + described <- stat$variantNames %||% stat$z %||% stat$b + if (is.null(described)) NA_integer_ else NROW(described) +} + +# One shrinkage level's fit against R_s = (1 - s) R + s I. +# @noRd +.rssFitAtS <- function(sVal, method, solverInput, ld, n, config) { + .rssFitOne(method, solverInput, .rssShrunkLd(ld, sVal), n, sVal, config) +} + +# An LD source shrunk to R_s = (1 - s) R + s I: the source carries s to the +# solver, which applies it to the matrix or the factor without forming R_s, +# so the grid over s shares one LD matrix rather than copying it per s. +# @noRd +.rssShrunkLd <- function(ld, s) { + list_assign(ld, shrink = s) } +# Shared scaffold for the RSS shrinkage-grid weight functions +# (lassosumRssWeights / .penalizedRssWeights / l0learnRssWeights), run for the +# statistics `stat` against the LD source `ld` (see .rssLdSource()). +# Standardizes the stat -> solverInput conversion, the outer LD-shrinkage grid +# over `s`, the candidate accumulation, and the ldQuadratic / minFbeta +# selection. `method` + `config` pick the per-`s` solver (.rssFitOne) and the +# finalizer (.rssFinalize); `fitRetention` how much of the search is kept. +# @noRd .rssShrinkGridWeights <- function( stat, - LD, + ld, s, method, config, - selection = c("ldQuadratic", "minFbeta") + selection = c("ldQuadratic", "minFbeta"), + fitRetention = c("none", "slim", "full") ) { selection <- arg_match(selection) + fitRetention <- arg_match(fitRetention) n <- median(stat$n) - p <- nrow(LD) - corInput <- .lassosumClampCor(.lassosumCorFromStat(stat, n = n, p = p)) + p <- .ldSourceSize(ld) + corInput <- .lassosumClampCor(.rssMarginalCor(stat, n = n, p = p)) solverInput <- corInput * sqrt(n) fits <- map( s, .rssFitAtS, method = method, solverInput = solverInput, - LD = LD, + ld = ld, n = n, - p = p, config = config ) candidateBeta <- exec(cbind, !!!map(fits, "beta")) candidateMeta <- bind_rows(map(fits, "meta")) selectorResult <- if (selection == "ldQuadratic") { - .lassosumSelectLdQuadratic(candidateBeta, corInput, LD) + .lassosumSelectLdQuadratic(candidateBeta, corInput, ld) } else { .lassosumSelectMinFbeta(candidateBeta, candidateMeta) } bestBeta <- as.numeric(selectorResult$beta) - .rssFinalize(method, bestBeta, selectorResult, candidateMeta, config) + weights <- .rssFinalize( + method, + bestBeta, + selectorResult, + candidateMeta, + config + ) + .rssAttachGridFit( + weights, + candidateBeta, + candidateMeta, + selectorResult$index, + fitRetention + ) +} + +# The search behind the grid weights as their "fit" attribute: `path`, one +# row per candidate fit with the solver's diagnostics and `selected` marking +# the one returned, and at fitRetention = "full" also `beta`, the candidates' +# coefficients (variants x candidates, in path's row order). Returns `weights` +# unchanged at fitRetention = "none". +# @noRd +.rssAttachGridFit <- function( + weights, + candidateBeta, + candidateMeta, + index, + fitRetention +) { + if (identical(fitRetention, "none")) { + return(weights) + } + fit <- c( + list(path = mutate(candidateMeta, selected = row_number() == index)), + compact(list(beta = if (fitRetention == "full") candidateBeta)) + ) + `attr<-`(weights, "fit", fit) } #' Extract weights from lassosumRss with shrinkage grid search @@ -2501,20 +2756,36 @@ lassosumRss <- function( #' This implementation therefore uses the supplied LD matrix directly for #' selection. \code{min(fbeta)} is retained only as an explicit debug option. #' -#' @param stat A list with \code{$b} (effect sizes) and \code{$n} (per-variant -#' sample sizes). -#' @param LD LD correlation matrix R (single matrix, NOT pre-shrunk). +#' @param stat A list of summary statistics: \code{n} (per-variant sample +#' sizes) and the marginal effects as \code{cor} (standardized), \code{z}, +#' \code{b} with \code{seb}, or \code{b} alone, taken as already +#' standardized. The engine receives the standardized effects z / sqrt(n), +#' with n the median of \code{n}. +#' @param ldReference The LD reference the statistics' variants are matched +#' to by \code{stat$variantNames}: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. A genotype reference's LD is used through its +#' standardized genotypes, never formed as a matrix. #' @param s Numeric vector of shrinkage parameters to search over. Default: #' \code{c(0.2, 0.5, 0.9, 1.0)} following Mak et al (2017) and OTTERS. #' @param selection Selection strategy. Default \code{"ldQuadratic"} uses #' \eqn{c^T \beta / \sqrt{\beta^T R \beta}} on the supplied LD matrix. #' \code{"minFbeta"} is retained as an explicit alternative for debugging. -#' @param methodArgs Options forwarded to \code{lassosumRss}, built -#' with \code{\link{LassosumOptions}}. A bare list is refused: it cannot be -#' checked against the engine, so a misspelled option would be -#' silently ignored. +#' @param fitRetention How much of the search is kept as the \code{"fit"} +#' attribute of the returned weights. \code{"none"} (default) attaches +#' nothing. \code{"slim"} keeps \code{path}, a table with one row per +#' candidate (\code{s}, \code{lambda}) giving the solver's objective +#' \code{fbeta}, \code{loss}, convergence flag \code{conv} and number of +#' non-zero coefficients \code{nparams}, with \code{selected} marking the +#' candidate returned. \code{"full"} also keeps \code{beta}, the +#' candidates' coefficients (variants x candidates, in the rows' order). +#' @param methodArgs Options forwarded to the solver, +#' \code{\link{lassosumRss}}, built with \code{\link{LassosumOptions}}. A +#' bare list is refused: it cannot be checked against the engine, so a +#' misspelled option would be silently ignored. #' @return A numeric vector of the posterior SNP coefficients at the best (s, -#' lambda). +#' lambda). Unless \code{fitRetention = "none"}, the search is attached as +#' attribute \code{"fit"}. #' @examples #' data(eqtlRegionExample) #' X <- eqtlRegionExample$X[, 1:30] @@ -2524,27 +2795,31 @@ lassosumRss <- function( #' stat <- list( #' b = vapply(ss, `[`, numeric(1), 1L), #' seb = vapply(ss, `[`, numeric(1), 2L), -#' n = rep(nrow(X), ncol(X)) +#' n = rep(nrow(X), ncol(X)), +#' variantNames = colnames(X) #' ) -#' LD <- cor(X) -#' lassosumRssWeights(stat, LD) +#' w <- lassosumRssWeights(stat, LdMatrix(cor(X)), fitRetention = "slim") +#' head(attr(w, "fit")$path) #' @export lassosumRssWeights <- function( stat, - LD, + ldReference, s = c(0.2, 0.5, 0.9, 1.0), selection = c("ldQuadratic", "minFbeta"), + fitRetention = c("none", "slim", "full"), methodArgs = LassosumOptions() ) { .assertMethodOptions(methodArgs, "LassosumOptions", "methodArgs") selection <- arg_match(selection) + fitRetention <- arg_match(fitRetention) .rssShrinkGridWeights( stat, - LD, + .rssLdSource(stat, ldReference, "lassosumRssWeights"), s, "lassosum", list(dotArgs = methodArgs), - selection + selection, + fitRetention ) } @@ -2556,6 +2831,13 @@ lassosumRssWeights <- function( #' where \eqn{R} is a (possibly pre-shrunk) LD matrix and \eqn{z = \hat\beta / #' \sqrt{n}}. #' +#' Internal: \code{\link{scadRssWeights}}, \code{\link{mcpRssWeights}} and +#' \code{\link{l0learnRssWeights}} are the public interface, with +#' \code{\link{lassosumRssWeights}} for the lasso. They run this over a grid +#' of LD shrinkage values, forward its options through +#' \code{\link{PenalizedRssOptions}}, and with \code{fitRetention} return +#' the paths as the \code{"fit"} attribute of their weights. +#' #' @param bhat Numeric vector of marginal effect estimates (length p). #' @param R The LD correlation matrix (a single matrix over the analysed #' window), as in \code{lassosumRss()}. @@ -2590,19 +2872,7 @@ lassosumRssWeights <- function( #' \item{betaEst}{Coefficient vector at the lambda minimizing fbeta.} #' } #' -#' @examples -#' set.seed(42) -#' p <- 10; n <- 100 -#' bhat <- rnorm(p, sd = 0.1) -#' R <- diag(p) -#' # MCP -#' penalizedRss(bhat, R, n, penalty = "MCP") -#' # SCAD -#' penalizedRss(bhat, R, n, penalty = "SCAD") -#' # L0 -#' penalizedRss(bhat, R, n, penalty = "L0", lambda0 = 0.01, -#' lambda = c(0)) -#' @export +#' @keywords internal penalizedRss <- function( bhat, R, @@ -2617,16 +2887,44 @@ penalizedRss <- function( maxiter = 10000, maxSwaps = 100 ) { - penalty <- arg_match(penalty) + .penalizedRssPath( + bhat, + list(R = R), + n, + arg_match(penalty), + lambda, + gamma, + alpha, + lambda0, + lambda2, + thr, + maxiter, + maxSwaps + ) +} + +# penalizedRss() on an LD source: the matrix, or a factor of it with its +# shrinkage, which the factor solver applies without forming R. +# @noRd +.penalizedRssPath <- function( + bhat, + ld, + n, + penalty, + lambda, + gamma, + alpha, + lambda0, + lambda2, + thr, + maxiter, + maxSwaps +) { # Default gamma per penalty - if (is.null(gamma)) { - gamma <- switch(penalty, SCAD = 3.7, MCP = 3.0, 0.0) - } - # C++ backend takes a block list; wrap the single-window matrix R as one - # block. + gamma <- gamma %||% switch(penalty, SCAD = 3.7, MCP = 3.0, 0.0) .rssSolvePath( bhat, - R, + ld, n, lambda, .rssPenalizedSolve, @@ -2643,6 +2941,30 @@ penalizedRss <- function( ) } +# .penalizedRssPath() with penalizedRss()'s own defaults under `args`, the +# caller's settings, which always name the penalty. A matrix takes exactly +# the path penalizedRss() does. +# @noRd +.penalizedRssOn <- function(bhat, ld, n, args) { + settings <- list_modify( + .formalDefaults( + penalizedRss, + c( + "lambda", + "gamma", + "alpha", + "lambda0", + "lambda2", + "thr", + "maxiter", + "maxSwaps" + ) + ), + !!!args + ) + exec(.penalizedRssPath, bhat = bhat, ld = ld, n = n, !!!settings) +} + #' RSS Weights Helper for Penalized Methods #' #' Shared implementation for \code{scadRssWeights()} and \code{mcpRssWeights()}. @@ -2652,15 +2974,17 @@ penalizedRss <- function( #' sI}) and selects the best candidate via LD-quadratic pseudovalidation or #' minimum penalized objective. #' -#' @param stat,LD,s,selection,penalty,gamma,alpha,lambda0,lambda2 See the -#' public wrappers for details. +#' @param stat,ldReference,s,selection,penalty,gamma,alpha,lambda0,lambda2 See +#' the public wrappers for details. +#' @param fitRetention How much of the search is kept as the \code{"fit"} +#' attribute; see \code{\link{scadRssWeights}}. #' @param methodArgs Options forwarded to \code{penalizedRss}, built with #' \code{\link{PenalizedRssOptions}}. -#' @return Numeric weight vector of length \code{nrow(LD)}. +#' @return Numeric weight vector, one weight per variant of \code{stat}. #' @keywords internal .penalizedRssWeights <- function( stat, - LD, + ldReference, penalty, s = c(0.2, 0.5, 0.9, 1.0), gamma = NULL, @@ -2668,13 +2992,19 @@ penalizedRss <- function( lambda0 = 0, lambda2 = 0, selection = c("ldQuadratic", "minFbeta"), + fitRetention = c("none", "slim", "full"), methodArgs = PenalizedRssOptions() ) { .assertMethodOptions(methodArgs, "PenalizedRssOptions", "methodArgs") selection <- arg_match(selection) + fitRetention <- arg_match(fitRetention) .rssShrinkGridWeights( stat, - LD, + .rssLdSource( + stat, + ldReference, + str_c(str_to_lower(penalty), "RssWeights") + ), s, "penalized", list( @@ -2685,7 +3015,8 @@ penalizedRss <- function( lambda2 = lambda2, dotArgs = methodArgs ), - selection + selection, + fitRetention ) } @@ -2695,20 +3026,31 @@ penalizedRss <- function( #' shrinkage grid \code{s} and lambda path. Model selection uses LD-quadratic #' pseudovalidation by default. #' -#' @param stat A list with \code{$b} (effect sizes) and \code{$n} (per-variant -#' sample sizes). -#' @param LD LD correlation matrix R (single matrix, NOT pre-shrunk). +#' @param stat A list of summary statistics: \code{n} (per-variant sample +#' sizes) and the marginal effects as \code{cor} (standardized), \code{z}, +#' \code{b} with \code{seb}, or \code{b} alone, taken as already +#' standardized. The engine receives the standardized effects z / sqrt(n), +#' with n the median of \code{n}. +#' @param ldReference The LD reference the statistics' variants are matched +#' to by \code{stat$variantNames}: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. A genotype reference's LD is used through its +#' standardized genotypes, never formed as a matrix. #' @param s Numeric vector of LD shrinkage parameters. Default: \code{c(0.2, #' 0.5, 0.9, 1.0)}. #' @param gamma SCAD concavity parameter. Default 3.7. #' @param alpha Elastic-net mixing (1 = pure L1). Default 1. #' @param selection Selection strategy: \code{"ldQuadratic"} (default) or #' \code{"minFbeta"}. -#' @param methodArgs Options forwarded to \code{penalizedRss}, built -#' with \code{\link{PenalizedRssOptions}}. A bare list is refused: it -#' cannot be checked against the engine, so a misspelled option would -#' be silently ignored. -#' @return A numeric vector of SNP coefficient weights. +#' @param methodArgs Options forwarded to the solver, +#' \code{\link{penalizedRss}}, built with +#' \code{\link{PenalizedRssOptions}}. A bare list is refused: it cannot +#' be checked against the engine, so a misspelled option would be +#' silently ignored. +#' @inheritParams lassosumRssWeights +#' @return A numeric vector of SNP coefficient weights. Unless +#' \code{fitRetention = "none"}, the search is attached as attribute +#' \code{"fit"}. #' @examples #' data(eqtlRegionExample) #' X <- eqtlRegionExample$X[, 1:30] @@ -2718,34 +3060,37 @@ penalizedRss <- function( #' stat <- list( #' b = vapply(ss, `[`, numeric(1), 1L), #' seb = vapply(ss, `[`, numeric(1), 2L), -#' n = rep(nrow(X), ncol(X)) +#' n = rep(nrow(X), ncol(X)), +#' variantNames = colnames(X) #' ) -#' LD <- cor(X) -#' scadRssWeights(stat, LD) +#' scadRssWeights(stat, LdMatrix(cor(X))) #' @importFrom checkmate assertList assertNumeric assertNumber #' @export scadRssWeights <- function( stat, - LD, + ldReference, s = c(0.2, 0.5, 0.9, 1.0), gamma = 3.7, alpha = 1.0, selection = c("ldQuadratic", "minFbeta"), + fitRetention = c("none", "slim", "full"), methodArgs = PenalizedRssOptions() ) { .assertMethodOptions(methodArgs, "PenalizedRssOptions", "methodArgs") + fitRetention <- arg_match(fitRetention) assertList(stat) assertNumeric(s, lower = 0, any.missing = FALSE) assertNumber(gamma, finite = TRUE) assertNumber(alpha, finite = TRUE) .penalizedRssWeights( stat = stat, - LD = LD, + ldReference = ldReference, penalty = "SCAD", s = s, gamma = gamma, alpha = alpha, selection = selection, + fitRetention = fitRetention, methodArgs = methodArgs ) } @@ -2756,20 +3101,31 @@ scadRssWeights <- function( #' shrinkage grid \code{s} and lambda path. Model selection uses LD-quadratic #' pseudovalidation by default. #' -#' @param stat A list with \code{$b} (effect sizes) and \code{$n} (per-variant -#' sample sizes). -#' @param LD LD correlation matrix R (single matrix, NOT pre-shrunk). +#' @param stat A list of summary statistics: \code{n} (per-variant sample +#' sizes) and the marginal effects as \code{cor} (standardized), \code{z}, +#' \code{b} with \code{seb}, or \code{b} alone, taken as already +#' standardized. The engine receives the standardized effects z / sqrt(n), +#' with n the median of \code{n}. +#' @param ldReference The LD reference the statistics' variants are matched +#' to by \code{stat$variantNames}: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. A genotype reference's LD is used through its +#' standardized genotypes, never formed as a matrix. #' @param s Numeric vector of LD shrinkage parameters. Default: \code{c(0.2, #' 0.5, 0.9, 1.0)}. #' @param gamma MCP concavity parameter. Default 3. #' @param alpha Elastic-net mixing (1 = pure L1). Default 1. #' @param selection Selection strategy: \code{"ldQuadratic"} (default) or #' \code{"minFbeta"}. -#' @param methodArgs Options forwarded to \code{penalizedRss}, built -#' with \code{\link{PenalizedRssOptions}}. A bare list is refused: it -#' cannot be checked against the engine, so a misspelled option would -#' be silently ignored. -#' @return A numeric vector of SNP coefficient weights. +#' @param methodArgs Options forwarded to the solver, +#' \code{\link{penalizedRss}}, built with +#' \code{\link{PenalizedRssOptions}}. A bare list is refused: it cannot +#' be checked against the engine, so a misspelled option would be +#' silently ignored. +#' @inheritParams lassosumRssWeights +#' @return A numeric vector of SNP coefficient weights. Unless +#' \code{fitRetention = "none"}, the search is attached as attribute +#' \code{"fit"}. #' @examples #' data(eqtlRegionExample) #' X <- eqtlRegionExample$X[, 1:30] @@ -2779,34 +3135,37 @@ scadRssWeights <- function( #' stat <- list( #' b = vapply(ss, `[`, numeric(1), 1L), #' seb = vapply(ss, `[`, numeric(1), 2L), -#' n = rep(nrow(X), ncol(X)) +#' n = rep(nrow(X), ncol(X)), +#' variantNames = colnames(X) #' ) -#' LD <- cor(X) -#' mcpRssWeights(stat, LD) +#' mcpRssWeights(stat, LdMatrix(cor(X))) #' @importFrom checkmate assertList assertNumeric assertNumber #' @export mcpRssWeights <- function( stat, - LD, + ldReference, s = c(0.2, 0.5, 0.9, 1.0), gamma = 3.0, alpha = 1.0, selection = c("ldQuadratic", "minFbeta"), + fitRetention = c("none", "slim", "full"), methodArgs = PenalizedRssOptions() ) { .assertMethodOptions(methodArgs, "PenalizedRssOptions", "methodArgs") + fitRetention <- arg_match(fitRetention) assertList(stat) assertNumeric(s, lower = 0, any.missing = FALSE) assertNumber(gamma, finite = TRUE) assertNumber(alpha, finite = TRUE) .penalizedRssWeights( stat = stat, - LD = LD, + ldReference = ldReference, penalty = "MCP", s = s, gamma = gamma, alpha = alpha, selection = selection, + fitRetention = fitRetention, methodArgs = methodArgs ) } @@ -2821,9 +3180,16 @@ mcpRssWeights <- function( #' converges, non-zero coefficients are tested for swaps with zero ones to #' escape local optima. #' -#' @param stat A list with \code{$b} (effect sizes) and \code{$n} (per-variant -#' sample sizes). -#' @param LD LD correlation matrix R (single matrix, NOT pre-shrunk). +#' @param stat A list of summary statistics: \code{n} (per-variant sample +#' sizes) and the marginal effects as \code{cor} (standardized), \code{z}, +#' \code{b} with \code{seb}, or \code{b} alone, taken as already +#' standardized. The engine receives the standardized effects z / sqrt(n), +#' with n the median of \code{n}. +#' @param ldReference The LD reference the statistics' variants are matched +#' to by \code{stat$variantNames}: a genotype panel (see +#' \code{\link{readGenotypes}}), a \code{\link{GenotypeMixture}} or an +#' \code{\link{LdMatrix}}. A genotype reference's LD is used through its +#' standardized genotypes, never formed as a matrix. #' @param penalty L0 variant: \code{"L0"}, \code{"L0L1"}, or \code{"L0L2"}. #' Default \code{"L0"}. #' @param s Numeric vector of LD shrinkage parameters. Default: \code{c(0.2, @@ -2836,11 +3202,22 @@ mcpRssWeights <- function( #' @param selection Selection strategy: \code{"ldQuadratic"} (default) or #' \code{"minFbeta"}. #' @param maxSwaps Maximum swap rounds per lambda. Default 100. -#' @param methodArgs Options forwarded to \code{penalizedRss}, built -#' with \code{\link{PenalizedRssOptions}}. A bare list is refused: it -#' cannot be checked against the engine, so a misspelled option would -#' be silently ignored. -#' @return A numeric vector of SNP coefficient weights. +#' @param fitRetention How much of the search is kept as the \code{"fit"} +#' attribute of the returned weights. \code{"none"} (default) attaches +#' nothing. \code{"slim"} keeps \code{path}, a table with one row per +#' candidate (\code{s}, \code{lambda0}, \code{lambda}) giving the solver's +#' objective \code{fbeta}, \code{loss}, convergence flag \code{conv} and +#' number of non-zero coefficients \code{nparams}, with \code{selected} +#' marking the candidate returned. \code{"full"} also keeps \code{beta}, +#' the candidates' coefficients (variants x candidates, in the rows' order). +#' @param methodArgs Options forwarded to the solver, +#' \code{\link{penalizedRss}}, built with +#' \code{\link{PenalizedRssOptions}}. A bare list is refused: it cannot be +#' checked against the engine, so a misspelled option would be silently +#' ignored. +#' @return A numeric vector of SNP coefficient weights. Unless +#' \code{fitRetention = "none"}, the search is attached as attribute +#' \code{"fit"}. #' @examples #' data(eqtlRegionExample) #' X <- eqtlRegionExample$X[, 1:30] @@ -2850,14 +3227,14 @@ mcpRssWeights <- function( #' stat <- list( #' b = vapply(ss, `[`, numeric(1), 1L), #' seb = vapply(ss, `[`, numeric(1), 2L), -#' n = rep(nrow(X), ncol(X)) +#' n = rep(nrow(X), ncol(X)), +#' variantNames = colnames(X) #' ) -#' LD <- cor(X) -#' l0learnRssWeights(stat, LD) +#' l0learnRssWeights(stat, LdMatrix(cor(X))) #' @export l0learnRssWeights <- function( stat, - LD, + ldReference, penalty = c("L0", "L0L1", "L0L2"), s = c(0.2, 0.5, 0.9, 1.0), lambda0 = exp(seq(log(0.001), log(1), length.out = 10)), @@ -2865,11 +3242,13 @@ l0learnRssWeights <- function( lambda2 = 0, selection = c("ldQuadratic", "minFbeta"), maxSwaps = 100, + fitRetention = c("none", "slim", "full"), methodArgs = PenalizedRssOptions() ) { .assertMethodOptions(methodArgs, "PenalizedRssOptions", "methodArgs") penalty <- arg_match(penalty) selection <- arg_match(selection) + fitRetention <- arg_match(fitRetention) # Default lambda (L1 component) depends on variant if (is.null(lambda)) { @@ -2882,7 +3261,7 @@ l0learnRssWeights <- function( .rssShrinkGridWeights( stat, - LD, + .rssLdSource(stat, ldReference, "l0learnRssWeights"), s, "l0learn", list( @@ -2893,7 +3272,8 @@ l0learnRssWeights <- function( maxSwaps = maxSwaps, dotArgs = methodArgs ), - selection + selection, + fitRetention ) } @@ -3259,7 +3639,6 @@ bLassoWeights <- function( #' nothing, \code{"slim"} and \code{"full"} attach the fitted model. This #' engine keeps no trimmable intermediates, so the two retaining levels #' behave alike here. -#' Default \code{FALSE}. #' @param nK Integer. Number of variational mixture components for the VB fit #' (\code{dprVbWeights}). Default \code{8}. #' @param sStep Integer. Number of Gibbs sampling steps for the Gibbs fit @@ -3422,7 +3801,7 @@ dprAdaptiveGibbsWeights <- function( numThreads ) { .mrmashRequirePackages() - .assertMethodParam(prior, "MrmashPriorParam", "prior") + .assertMethodParam(prior, "MrmashPriorParam") .assertMethodOptions(methodArgs, "MrmashOptions", "methodArgs") priorOpts <- as.list(prior) .mrmashValidateWrapper( @@ -3658,6 +4037,7 @@ mrmashWrapper <- function( Y, alpha = 0.5, standardize = standardize, + numThreads = numThreads, Xnew = NULL ) } @@ -3701,12 +4081,11 @@ mrmashWrapper <- function( ) } -# Run mr.mash with the resolved prior / init / V. -# @noRd -# The mr.mash call. Everything pecotmr derives -- the prior, the residual -# covariance, the coefficient initialisation -- is supplied here and cannot be -# overridden; the rest of mr.mash's arguments come from `methodArgs` under -# mr.mash's own names, with pecotmr's defaults underneath. +# Run mr.mash with the resolved prior / init / V. Everything pecotmr derives -- +# the prior, the residual covariance, the coefficient initialisation -- is +# supplied here and cannot be overridden; the rest of mr.mash's arguments come +# from `methodArgs` under mr.mash's own names, with pecotmr's defaults +# underneath. # @noRd .mrmashFit <- function( S0, @@ -3860,17 +4239,34 @@ computeCoefficientsGlasso <- function( return(res) } -### Function to compute coefficients for univariate glmnet +# Initial mr.mash coefficients from one cv.glmnet per response. The fits are +# independent, so with `numThreads` >= 2 they run in parallel, each worker on +# its own seeded RNG stream (cv.glmnet draws its folds at random); one thread +# runs them in turn on the session RNG, as before. +# @noRd computeCoefficientsUnivGlmnet <- function( X, Y, alpha, standardize, + numThreads = 1L, Xnew = NULL ) { r <- ncol(Y) - - out <- map(seq_len(r), .linreg, X, Y, alpha, standardize, Xnew) + out <- if (numThreads >= 2L) { + bplapply( + seq_len(r), + .linreg, + X = X, + Y = Y, + alpha = alpha, + standardize = standardize, + Xnew = Xnew, + BPPARAM = .bpSeedParam(numThreads) + ) + } else { + map(seq_len(r), .linreg, X, Y, alpha, standardize, Xnew) + } bhatList <- map(out, "bhat") Bhat <- exec(cbind, !!!bhatList) @@ -3952,10 +4348,11 @@ rescaleCovW0 <- function(w0) { # ceiling(log2(gmax/gmin)/log2(mult)), carried fewer points than mr.mash # would have used. # -# `mult = sqrt(2)` (finer than upstream's default 2) and the square are -# pecotmr's own choices: this grid scales variances, not standard -# deviations. autoselect.mixsd() drops Shat that is zero or non-finite, -# which covers NA, so the port's extra is.na() term was redundant. +# `mult = sqrt(2)` and the square follow mr.mashr's own documented recipe +# (the mr.mash() and mr.mash.rss() examples build their grids exactly so): +# this grid scales variances, not standard deviations. autoselect.mixsd() +# drops Shat that is zero or non-finite, which covers NA, so the port's extra +# is.na() term was redundant. # @noRd computeGrid <- function(bhat, sbhat) { mr.mashr::autoselect.mixsd( @@ -4192,22 +4589,12 @@ buildMrmashPriorMatrices <- function( if (is.null(priorGrid)) { priorGrid <- computeGrid(bhat = Bhat, sbhat = Shat) } - - if (isTRUE(canonicalPriorMatrices)) { - canonical <- exec( - mr.mashr::compute_canonical_covs, - K, - !!!as.list(canonicalCovs) - ) - S0_raw <- if (!is.null(dataDrivenPriorMatrices)) { - c(canonical, dataDrivenPriorMatrices$U) - } else { - canonical - } - } else { - S0_raw <- dataDrivenPriorMatrices$U - } - + S0_raw <- .mrmashRawPriorCovs( + K, + dataDrivenPriorMatrices, + canonicalPriorMatrices, + canonicalCovs + ) S0 <- exec( mr.mashr::expand_covs, S0_raw, @@ -4217,26 +4604,62 @@ buildMrmashPriorMatrices <- function( list(S0 = S0, priorGrid = priorGrid) } +# The unexpanded prior covariances: the canonical set (when requested) plus +# any data-driven matrices, or the data-driven matrices alone. +# @noRd +.mrmashRawPriorCovs <- function( + K, + dataDrivenPriorMatrices, + canonicalPriorMatrices, + canonicalCovs +) { + if (!isTRUE(canonicalPriorMatrices)) { + return(dataDrivenPriorMatrices$U) + } + canonical <- exec( + mr.mashr::compute_canonical_covs, + K, + !!!as.list(canonicalCovs) + ) + if (is.null(dataDrivenPriorMatrices)) { + return(canonical) + } + c(canonical, dataDrivenPriorMatrices$U) +} + # ---- map/apply helpers (lambda-free callbacks) --------------------------- -# `.rssSolvePath` solver for lassosum: RSS lasso over the ordered lambda path. +# `.rssSolvePath` solver for lassosum: RSS lasso over the ordered lambda path, +# on the LD matrix or on a factor of it (never forming R). # @noRd -.rssLassosumSolve <- function(z, lam, R, thr, maxiter) { - lassosumRssRcpp( +.rssLassosumSolve <- function(z, lam, ld, thr, maxiter) { + if (!is.null(ld$F)) { + return(lassosumRssFactorCpp( + zR = z, + factorR = ld$F, + shrink = ld$shrink %||% 0, + lambdaR = lam, + thr = thr, + maxiter = as.integer(maxiter) + )) + } + lassosumRssCpp( zR = z, - LD = list(blk1 = R), + LD = list(blk1 = ld$R), + shrink = ld$shrink %||% 0, lambdaR = lam, thr = thr, maxiter = as.integer(maxiter) ) } -# `.rssSolvePath` solver for the penalized (MCP/SCAD/L0...) RSS path. +# `.rssSolvePath` solver for the penalized (MCP/SCAD/L0...) RSS path, on the +# LD matrix or on a factor of it (never forming R). # @noRd .rssPenalizedSolve <- function( z, lam, - R, + ld, penalty, gamma, alpha, @@ -4246,9 +4669,7 @@ buildMrmashPriorMatrices <- function( maxiter, maxSwaps ) { - penalizedRssRcpp( - zR = z, - LD = list(blk1 = R), + settings <- list( lambdaR = lam, penaltyStr = penalty, gamma = gamma, @@ -4259,16 +4680,29 @@ buildMrmashPriorMatrices <- function( maxiter = as.integer(maxiter), maxSwaps = as.integer(maxSwaps) ) + if (!is.null(ld$F)) { + return(exec( + penalizedRssFactorCpp, + zR = z, + factorR = ld$F, + shrink = ld$shrink %||% 0, + !!!settings + )) + } + exec( + penalizedRssCpp, + zR = z, + LD = list(blk1 = ld$R), + shrink = ld$shrink %||% 0, + !!!settings + ) } # One L0Learn fit at lambda0 = `l0Val`: penalizedRss over the lambda path. # @noRd -.rssL0LambdaFit <- function(l0Val, solverInput, LDs, n, sVal, config) { - penArgs <- c( +.rssL0LambdaFit <- function(l0Val, solverInput, ld, n, sVal, config) { + settings <- c( list( - bhat = solverInput, - R = LDs, - n = n, penalty = config$penalty, lambda = config$lambda, lambda0 = l0Val, @@ -4277,15 +4711,10 @@ buildMrmashPriorMatrices <- function( ), as.list(config$dotArgs) ) - model <- exec(penalizedRss, !!!penArgs) + model <- .penalizedRssOn(solverInput, ld, n, settings) list( beta = model$beta, - meta = tibble( - s = rep(sVal, length(model$lambda)), - lambda0 = rep(l0Val, length(model$lambda)), - lambda = model$lambda, - fbeta = model$fbeta - ) + meta = mutate(.rssPathMeta(sVal, model), lambda0 = l0Val, .after = "s") ) } diff --git a/R/relatednessQc.R b/R/relatednessQc.R index 8a398a229..54e690eee 100644 --- a/R/relatednessQc.R +++ b/R/relatednessQc.R @@ -233,8 +233,8 @@ filterRelatedness <- function( verbose = FALSE ) { .relatednessRequirePackages() - .assertMethodParam(columns, "RelatednessColumnsParam", "columns") - .assertMethodParam(pruning, "RelatednessPruningParam", "pruning") + .assertMethodParam(columns, "RelatednessColumnsParam") + .assertMethodParam(pruning, "RelatednessPruningParam") .assertMethodOptions(plinkQcArgs, "PlinkQcOptions", "plinkQc") analysisType <- arg_match(analysisType) relatedness <- as_tibble(relatedness) diff --git a/R/resultReaders.R b/R/resultReaders.R index b42ebca4f..5b9a3f870 100644 --- a/R/resultReaders.R +++ b/R/resultReaders.R @@ -64,6 +64,7 @@ NULL SE = "marginal_se", WGT = "posterior_mean", WGTSE = "posterior_sd", + THETA = "posterior_theta", PIP = "pip", LBF = "logBF", LFSR = "lfsr" @@ -1216,13 +1217,7 @@ readTwasWeights <- function( label <- "readTwasWeights" src <- .prdRead(path, region, format, sampleSelect, label) df <- src$table - if (!is_in("WGT", names(df))) { - abort(glue( - "{label}: no `WGT` column. The weight is the whole content of a ", - "TwasWeights. For a fine-mapping table use ", - "readFineMappingResult()." - )) - } + .prdRequireWgt(df, label) .prdRequireStandardized(df, weightStandardized, label) supplied <- compact(list( study = studyName, @@ -1239,6 +1234,24 @@ readTwasWeights <- function( supplied = supplied, label = label ) + .prdTwFromRows(rows, label) +} + +# @noRd +.prdRequireWgt <- function(df, label) { + if (!is_in("WGT", names(df))) { + abort(glue( + "{label}: no `WGT` column. The weight is the whole content of a ", + "TwasWeights. For a fine-mapping table use ", + "readFineMappingResult()." + )) + } +} + +# Assemble the TwasWeights collection from the per-group `.prdTwRow()` +# records; an empty file reads back as an empty collection. +# @noRd +.prdTwFromRows <- function(rows, label) { if (length(rows) == 0L) { return(TwasWeights( studyName = character(0), diff --git a/R/resultWriters.R b/R/resultWriters.R index e8b5e6e0f..0bb61c8fd 100644 --- a/R/resultWriters.R +++ b/R/resultWriters.R @@ -7,10 +7,11 @@ # exportToVcf(x, path, ...) GWAS-VCF-style: one record per variant, one # SAMPLE per study / fitted model, the statistics # as FORMAT fields. GwasSumStats, every -# FineMappingResult, TwasWeights. +# FineMappingResultBase, TwasWeights. # exportToBed(x, path, ...) the published long-format tables, one row per # published observation, bgzipped + tabix-indexed -# on request. FineMappingResult, TwasWeights. +# on request. FineMappingResultBase, +# TwasWeights. # # One file because they are one problem -- project an S4 collection onto a # FIXED published schema, join the posterior view to the marginal one, sort @@ -170,8 +171,9 @@ NULL return(combineFineMappingResults(x)) } abort(glue( - "`x` must be a TwasWeights or FineMappingResult object, or a list ", - "of one of those; got a list of {class(x[[1L]])[[1L]]}." + "`x` must be a TwasWeights, QtlFineMappingResult or ", + "GwasFineMappingResult object, or a list of one of those; got a ", + "list of {class(x[[1L]])[[1L]]}." )) } @@ -250,15 +252,16 @@ NULL # Refuse to export a collection whose rows lack a FULL-DATA fit. # # Cross-validation alone is not publishable. A CV-only run -- which is what -# `twasWeightsPipeline(fitFullData = FALSE)` produces, and which is legal only -# WITH cross-validation -- stores each row as an empty element with no weights -# and nothing but its `cvResult`. All it holds is fold-level results, and those -# cannot go in a published file: fold membership is sample-level information -# and sample names can be protected. So an export needs the full-data fit, and -# its absence is an ERROR rather than a silently empty output file. +# twasWeightsPipeline() produces under `CrossValidationParam(fitFullData = +# FALSE)`, legal only WITH cross-validation -- stores each row as an empty +# element with no weights and nothing but its `cvResult`. All it holds is +# fold-level results, and those cannot go in a published file: fold +# membership is sample-level information and sample names can be protected. +# So an export needs the full-data fit, and its absence is an ERROR rather +# than a silently empty output file. # -# The check is on the OBJECT, not on the flag: `fitFullData` is a pipeline -# argument that may not have been the thing that emptied a row, and an export +# The check is on the OBJECT, not on the flag: `fitFullData` is a run setting +# that may not have been the thing that emptied a row, and an export # has to be safe whatever produced its input. # # It also keeps genomicRegion() honest. That derives a row's analysis window @@ -275,7 +278,8 @@ NULL "full-data fit, only cross-validation results -- ", "{str_flatten(.pubRowLabels(x, incomplete), ', ')}. Fold-level ", "results cannot be published (fold membership is sample-level ", - "information), so refit with `fitFullData = TRUE` and export that. ", + "information), so refit with ", + "`CrossValidationParam(fitFullData = TRUE)` and export that. ", "The cross-validation record stays available through cvResult()." )) } @@ -293,10 +297,9 @@ NULL # @noRd .twbRowHasWeights <- function(i, x) !is.null(mcols(x[[i]])$weight) -# Rows of a FineMappingResult with no fitted variants. Fine-mapping has no -# CV-only mode today -- .fmAttachCv() rebuilds the row around its existing -# susieFit and topLoci, so CV is strictly additive -- but the export checks -# rather than assumes it. +# Rows of a fine-mapping result with no fitted variants: what a +# cross-validation only run (CrossValidationParam(fitFullData = FALSE)) leaves +# in each row. # @noRd .fmbIncompleteRows <- function(x) { which(lengths(x) == 0L) @@ -409,10 +412,10 @@ genomicOrder <- function(chrom, start, end = NULL) { # # utils::write.table(), not vroom_write(): the two differ in how they format a # float (1e-06 vs 1e-6) and in how many digits they keep. -# @noRd # `emptySchema` is the header an empty result still documents -- the schema of # whichever table was being written, since the fsusie table's vocabulary is not # the per-variant one. +# @noRd .pubWriteTable <- function( out, path, @@ -788,6 +791,12 @@ setMethod( # susie posterior mean IS the TWAS coefficient. # @noRd .vcfCoreSpecs <- function(base, m, nSnps) { + c(.vcfMarginalSpecs(m, nSnps), .vcfFitSpecs(base, nSnps)) +} + +# The marginal-association FORMAT fields, from the joined marginals `m`. +# @noRd +.vcfMarginalSpecs <- function(m, nSnps) { p <- suppressWarnings(as.numeric(.pubCol(m, "p", nSnps))) list( .vcfSpec( @@ -825,7 +834,15 @@ setMethod( "Variant-specific number of samples/individuals with called ", "genotypes used to test association with specified trait" ) - ), + ) + ) +} + +# The per-variant FORMAT fields read off the fit itself (`base`): allele +# frequency and the posterior effect / prediction weight. +# @noRd +.vcfFitSpecs <- function(base, nSnps) { + list( .vcfSpec( "AF", .pubCol(base, "af", nSnps), @@ -843,6 +860,15 @@ setMethod( .pubCol(base, "posterior_se", nSnps), "Float", "Standard error of the posterior effect estimate" + ), + .vcfSpec( + "THETA", + .pubCol(base, "theta", nSnps), + "Float", + str_c( + "Infinitesimal component of WGT (susieInf / susieAsh); ", + "already included in WGT, 0 for methods without one" + ) ) ) } @@ -1247,7 +1273,6 @@ setMethod( ) } -# @noRd # One mcols cell as a string. `[[row]]` then pack, rather than # as.character() on the column: several of these are List columns (a # TwasWeights `dataType` is one) and as.character() on a CompressedList @@ -1290,7 +1315,7 @@ setMethod( } # The same rule the BED path applies, and for the same reason: a # fine-mapping method's "weights" are the posterior re-expressed, so - # the FineMappingResult says strictly more. This path used to publish + # the fine-mapping result says strictly more. This path used to publish # them, which made the two formats disagree about what a weights file # contains. rows <- intersect(rows, .twbExportableRows(x, "exportToVcf")) @@ -1298,7 +1323,7 @@ setMethod( abort(str_c( "exportToVcf: every selected row is fine-mapping-derived, so ", "there is nothing to write as weights. Export the ", - "FineMappingResult." + "fine-mapping result." )) } .twvWriteSamples( @@ -1699,12 +1724,12 @@ setMethod( "grid_band_halfwidth" ) -# @noRd # The trait-level group. `joint_traits` rather than a new `trait_names`: these # ARE the joint members (fsusieTraitEffects() reads them from the row's # `jointTraits` column), so they use the vocabulary the rest of the schema # already has for joint membership. "epi_mark" also presumed epigenomic data, # which a functional fit does not require. +# @noRd .FMB_FSUSIE_TRAIT_COLUMNS <- c( "trait_positions", "trait_effects", @@ -1773,6 +1798,15 @@ setMethod( # One (row, credible set) block of the fSuSiE table. `v` is that CS's # credibleSets() rows; `band` / `marks` are the matching curve views, already # narrowed to the same row. +# +# The "chr" prefix is kept, as in the other two schemas and in the protocol's +# own phenotype BEDs; stripping it here was unique to this table and made one +# reader rule impossible. AF, not MAF, is published: the same `af` the other +# table publishes. Writing pmin(af, 1 - af) here lost which allele the +# frequency referred to, so one export of an object was recoverable and the +# other was not. Membership is written at EVERY coverage the fit carries, not +# just the one keying the rows -- a secondary coverage would otherwise be +# silently dropped. # @noRd .fmbFsusieBlock <- function( v, @@ -1788,41 +1822,26 @@ setMethod( key <- .pubIdentityKey(v, .PUB_ROW_COLS)[[1L]] cs <- v[[str_c("cs_", primary)]][[1L]] context <- v$context[[1L]] - bKey <- .pubIdentityKey(band, .PUB_ROW_COLS) - mKey <- .pubIdentityKey(marks, .PUB_ROW_COLS) - b <- band[bKey == key & band$cs == cs, , drop = FALSE] + b <- .fmbCurveRows(band, key, cs) if (nrow(b) == 0L) { return(NULL) } - m <- marks[mKey == key & marks$cs == cs, , drop = FALSE] + m <- .fmbCurveRows(marks, key, cs) i <- match(key, names(rowRegionIds)) regionId <- rowRegionIds[[i]] rg <- rowRegions[i] csId <- str_c(context, regionId, str_remove(cs, "^fsusie_"), sep = ":") out <- tibble( - # The "chr" prefix is kept, as in the other two schemas and in the - # protocol's own phenotype BEDs; stripping it here was unique to this - # table and made one reader rule impossible. `#chr` = as.character(v$chrom), start = v$pos - 1L, end = v$pos, ID = v$variant_id, REF = .pubCol(v, "A2"), ALT = .pubCol(v, "A1"), - # AF, not MAF: the same `af` the other table publishes. Writing - # pmin(af, 1 - af) here lost which allele the frequency referred to, - # so one export of an object was recoverable and the other was not. AF = .pubCol(v, "af"), PIP = v$pip, cs_id = csId, cs_root = csId, - grid_resolution = nrow(b), - grid_positions = .pubPack(b$pos), - grid_effects = .pubPack(b$effect), - grid_band_halfwidth = .pubPack(b$halfwidth), - trait_positions = .pubPack(m$traitPos), - trait_effects = .pubPack(m$effect), - joint_traits = .pubPack(m$jointTrait), study = .pubCol(v, "study"), context = context, trait = .pubCol(v, "trait"), @@ -1831,15 +1850,46 @@ setMethod( region_start = start(rg) - 1L, region_end = end(rg) ) - # Membership at EVERY coverage the fit carries, not just the one keying - # the rows -- a secondary coverage would otherwise be silently dropped. - out <- bind_cols(out, .pubFrameCs(v, csCols)) + out <- bind_cols(out, .fmbFsusieGrid(b, m), .pubFrameCs(v, csCols)) bind_cols( select(out, all_of(.fmbFsusieColumns(levels, csCols))), select(v, all_of(added)) ) } +# The rows of a curve view (credible band or trait marks) belonging to one +# (row identity, credible set) pair. +# @noRd +.fmbCurveRows <- function(view, key, cs) { + keep <- .pubIdentityKey(view, .PUB_ROW_COLS) == key & view$cs == cs + view[keep, , drop = FALSE] +} + +# The packed functional-grid columns of one fSuSiE block: the credible band +# `b` over the grid and the per-trait marks `m`. +# @noRd +.fmbFsusieGrid <- function(b, m) { + tibble( + grid_resolution = nrow(b), + grid_positions = .pubPack(b$pos), + grid_effects = .pubPack(b$effect), + grid_band_halfwidth = .pubPack(b$halfwidth), + trait_positions = .pubPack(m$traitPos), + trait_effects = .pubPack(m$effect), + joint_traits = .pubPack(m$jointTrait) + ) +} + +# The fSuSiE BED table: one block per (row, credible set) of the fsusie rows. +# +# genomicRegion() is the window each row was ANALYSED over (the requested +# region, or the trait's cis window), which is what a publication table means +# by its region -- not the narrower span of whichever variants survived QC. +# +# Splitting the views on (context, cs) alone is only safe for a single +# region: once several regions are combined into one table, two regions can +# each contribute context "c1" / "fsusie_1", and they must stay separate rows +# with their own windows. # @noRd .fmbFsusieTable <- function(x, columns, levels, coverage) { fs <- x[as.character(.tupleColumn(x, "method")) == "fsusie"] @@ -1858,19 +1908,11 @@ setMethod( primary <- .fmbPrimaryCsLevel(csTbl, coverage) csCols <- .pubCsColumns(csTbl, coverage) marks <- as.data.frame(fsusieTraitEffects(fs)) - # genomicRegion() is the window each row was ANALYSED over (the requested - # region, or the trait's cis window), which is what a publication table - # means by its region -- not the narrower span of whichever variants - # survived QC. rowRegions <- genomicRegion(fs) rowRegionIds <- set_names( .pubRegionIds(rowRegions), .pubCollectionKey(fs, .PUB_ROW_COLS) ) - # Splitting the views on (context, cs) alone is only safe for a single - # region: once several regions are combined into one table, two regions can - # each contribute context "c1" / "fsusie_1", and they must stay separate - # rows with their own windows. pieces <- compact(map( split( csTbl, @@ -1946,6 +1988,11 @@ setMethod( # columns that are both here. "WGT", "WGTSE", + # The infinitesimal half of WGT. susieInf / susieAsh split the posterior + # into a sparse and an infinitesimal term and coef.susie() sums them, so + # WGT is right either way -- this is what makes the split visible, which + # is otherwise unrecoverable from the published file. 0 elsewhere. + "THETA", # Fine-mapping posterior. CS/PUR are spliced in after these. "PIP", "LBF", @@ -1959,7 +2006,8 @@ setMethod( "block_id", # Provenance. `data_type` is the assay/modality; `data_source` is # individual-level vs summary-statistics, which `data_type` was being - # mistaken for and which is derivable from whether an ldSketch is present. + # mistaken for and which is derivable from whether an ldReference is + # present. "data_type", "data_source", "weight_standardized", @@ -2114,6 +2162,14 @@ setMethod( "p" ) +# The topLoci BED table. +# +# `tl$beta`/`tl$se` are the POSTERIOR mean and sd -- topLoci() is the +# posterior view -- while `mm$beta`/`mm$se` are the MARGINAL estimates. Same +# column name, different quantity, which is why they land in different +# published fields. Per-CS variant-level columns from a fullFit build ride +# after the fixed schema; `columns` adds whatever else the caller asked for +# after those. # @noRd .fmbTopLociTable <- function(x, signalCutoff, minPurity, columns, coverage) { tl <- as.data.frame( @@ -2122,10 +2178,6 @@ setMethod( if (nrow(tl) == 0L) { return(NULL) } - # `tl$beta`/`tl$se` are the POSTERIOR mean and sd -- topLoci() is the - # posterior view -- while `mm$beta`/`mm$se` are the MARGINAL estimates. - # Same column name, different quantity, which is why they land in - # different published fields. mm <- .pubJoinMarginals(tl, as.data.frame(marginalEffects(x))) win <- .fmbRowWindows(x, tl) pos <- .pubCol(tl, "pos") @@ -2147,6 +2199,7 @@ setMethod( LP = if_else(is.na(p) | p <= 0, NA_real_, -log10(p)), WGT = .pubCol(tl, "posterior_effect"), WGTSE = .pubCol(tl, "posterior_se"), + THETA = .pubCol(tl, "theta"), PIP = .pubCol(tl, "pip"), LBF = .pubCol(tl, "logBF"), LFSR = .pubCol(tl, "lfsr") @@ -2160,8 +2213,6 @@ setMethod( ) ) out <- .pubFrame(cols, .pubSchema(csCols)) - # Per-CS variant-level columns from a fullFit build ride after the fixed - # schema; `columns` adds whatever else the caller asked for after those. added <- union( .pubFullFitColumns(tl), .pubViewColumns(tl, columns, .FMB_TOPLOCI_SOURCE) @@ -2211,8 +2262,9 @@ setMethod( } # The identity and provenance values shared by both record kinds. `data_source` -# is derived rather than stored: a collection's ldSketch is the LD reference the -# fit was run against, so its absence means the fit saw individual-level data. +# is derived rather than stored: a collection's ldReference is the LD reference +# the fit was run against, so its absence means the fit saw individual-level +# data. # @noRd .pubIdentityValues <- function(x, view) { list( @@ -2230,19 +2282,15 @@ setMethod( ) } -# The collection-level fallback for `data_source`: an ldSketch is the LD +# The collection-level fallback for `data_source`: an ldReference is the LD # reference a fit was run against, so its absence means individual-level data. # Only a fallback -- a row that recorded its own wins, because a combined -# collection can hold rows of both kinds and has only one ldSketch. +# collection can hold rows of both kinds and has only one ldReference. # @noRd .pubDataSource <- function(x) { - if (is.null(ldSketch(x))) "individual" else "sumstat" + if (is.null(ldReference(x))) "individual" else "sumstat" } -# @noRd -# Absent OR entirely empty both mean "the rows did not record this", because -# .fmrRowMetadata() NA-fills a column the collection lacks -- so testing only -# for absence would return the NA column and never reach the fallback. # `EZ` only when the SOURCE supplied z. The spec defines the field as the z # "provided if it was used to derive the ES and SE fields", so publishing a z # that pecotmr itself computed as beta/se would invert its meaning -- and a @@ -2262,6 +2310,9 @@ setMethod( replace(z, is.na(supplied) | !supplied, NA_real_) } +# Absent OR entirely empty both mean "the rows did not record this", because +# .fmrRowMetadata() NA-fills a column the collection lacks -- so testing only +# for absence would return the NA column and never reach the fallback. # @noRd .pubRowOrDerived <- function(view, nm, fallback) { v <- .pubCol(view, nm) @@ -2292,11 +2343,6 @@ setMethod( ) } -# The analysis window of the collection row each long-view row came from, -# matched on the row identity so a folded multi-row table keeps each row's own -# window rather than the first one's. -# @noRd - # ---- BED: fine-mapping entry point ------------------------------------------ #' @rdname exportToBed @@ -2465,7 +2511,7 @@ setMethod( } # Fine-mapping methods have no business in a weights export: their "weights" -# are the fine-mapping posterior re-expressed, so the FineMappingResult says +# are the fine-mapping posterior re-expressed, so the fine-mapping result says # strictly more. Those rows are skipped with a message rather than written. # @noRd .TWB_FINE_MAPPING_METHODS <- c( @@ -2485,8 +2531,8 @@ setMethod( "{label}: skipping {length(skip)} fine-mapping-derived ", "row(s) ({str_flatten(unique(methods[skip]), ', ')}). Those ", "weights are the fine-mapping posterior re-expressed -- export ", - "the FineMappingResult instead, which carries the posterior, its ", - "standard error and the credible sets." + "the fine-mapping result instead, which carries the posterior, ", + "its standard error and the credible sets." )) } setdiff(seq_len(nrow(x)), skip) @@ -2574,24 +2620,21 @@ setMethod( )) } -# Per-variant allele frequency, from the collection's LD panel when it has one. -# A TwasWeights element records only alleles and a weight, so without a panel -# there is no frequency to publish -- and `AF` is what the standardized-to-raw -# weight conversion needs, so its absence is worth seeing rather than faking. +# Per-variant allele frequency, from the collection's LD reference when it +# records one (an LdMatrix with an AF column, or a stochastic sketch, whose +# real frequencies are recorded from its sidecar). A TwasWeights element +# records only alleles and a weight, so otherwise there is no frequency to +# publish -- and `AF` is what the standardized-to-raw weight conversion needs, +# so its absence is worth seeing rather than faking. # @noRd .twbVariantAf <- function(x, gr) { - panel <- ldSketch(x) - if (is.null(panel)) { - return(NA_real_) - } - rr <- try_fetch( - SummarizedExperiment::rowRanges(panel), - error = function(cnd) NULL - ) - if (is.null(rr) || !is_in("af", colnames(mcols(rr)))) { + ref <- ldReference(x) + af <- if (is.null(ref)) NULL else .ldRefAf(ref) + if (is.null(af)) { return(NA_real_) } - as.numeric(mcols(rr)$af)[match(.grVariantIds(gr), .grVariantIds(rr))] + rr <- .ldRefRanges(ref) + as.numeric(af)[match(.grVariantIds(gr), .grVariantIds(rr))] } # @noRd @@ -2600,7 +2643,7 @@ setMethod( if (length(rows) == 0L) { abort(str_c( "exportToBed: every row is fine-mapping-derived, so there is ", - "nothing to write as weights. Export the FineMappingResult." + "nothing to write as weights. Export the fine-mapping result." )) } schema <- .pubSchema(.pubCsColumns(tibble(), coverage)) diff --git a/R/sumstatsQc.R b/R/sumstatsQc.R index be92e1649..48ea8998a 100644 --- a/R/sumstatsQc.R +++ b/R/sumstatsQc.R @@ -26,7 +26,7 @@ NULL # Allele harmonization (harmonizeAlleles) now lives in R/variantId.R, alongside # the other variant-matching primitives (parseVariantId, matchVariants). It is -# package-internal and called from here (.matchAgainstSketch), ctwasPipeline, +# package-internal and called from here (.matchAgainstLdRef), ctwasPipeline, # and the pipeline join sites. # Standardize a variant set (GRanges or data.frame) to a chrom/pos/alt/ref @@ -140,12 +140,7 @@ resolveLdInput <- function( needNSample = FALSE, ldMethod = "sample" ) { - if (is.null(R) && is.null(X)) { - abort("Either R (LD matrix) or X (genotype matrix) must be provided.") - } - if (!is.null(R) && !is.null(X)) { - abort("Provide either R or X, not both.") - } + .ldRequireOneSource(R, X) if (!is.null(X)) { if (!is.matrix(X)) { X <- as.matrix(X) @@ -161,6 +156,18 @@ resolveLdInput <- function( list(R = R, nSample = nSample) } +# Require exactly one of R (LD matrix) or X (genotype matrix). +# @noRd +.ldRequireOneSource <- function(R, X) { + if (is.null(R) && is.null(X)) { + abort("Either R (LD matrix) or X (genotype matrix) must be provided.") + } + if (!is.null(R) && !is.null(X)) { + abort("Provide either R or X, not both.") + } + invisible(NULL) +} + # --- dentist helpers -------------------------------------------------------- # Validate/rename the pos and z columns (accepting position/zscore) + sort. @@ -196,16 +203,19 @@ resolveLdInput <- function( } # Run DENTIST on a single window, unpacking the shared tuning parameters. -.dentistCallSingle <- function(zScore, ldMat, nSample, methodArgs) { +# `ld` is .dentistLdInput()'s list(R) or list(F); a factor goes in as X, which +# dentistSingleWindow() reads without forming the LD. +.dentistCallSingle <- function(zScore, ld, nSample, dentistParam) { # dentistSingleWindow is what DentistParam() is built for -- ldMismatchQc # splices the same bundle into it -- so the tunables travel as one value # rather than being named again at every hop down from dentist(). exec( dentistSingleWindow, zScore, - R = ldMat, + R = ld$R, + X = ld$F, nSample = nSample, - !!!methodArgs + !!!dentistParam ) } @@ -215,10 +225,10 @@ resolveLdInput <- function( .dentistWindowAt <- function( k, sumStat, - ldMat, + ld, nSample, windowDividedRes, - methodArgs + dentistParam ) { # windowEndIdx is 1-based exclusive; convert to an inclusive range. idxRange <- windowDividedRes$windowStartIdx[ @@ -226,20 +236,52 @@ resolveLdInput <- function( ]:(windowDividedRes$windowEndIdx[k] - 1L) .dentistCallSingle( sumStat$z[idxRange], - ldMat[idxRange, idxRange], + .dentistLdSlice(ld, idxRange), nSample, - methodArgs + dentistParam + ) +} + +# One window's LD: the matrix's diagonal block, or the factor's columns. +# @noRd +.dentistLdSlice <- function(ld, idx) { + if (is.null(ld$F)) { + return(list(R = ld$R[idx, idx, drop = FALSE])) + } + list(F = ld$F[, idx, drop = FALSE]) +} + +# The LD a DENTIST run reads, with the sample size it needs. Genotypes under +# the sample estimator give their factor (.ldFactorFromDosage()), which +# DENTIST's factor kernel reads without forming the p x p LD; an LD matrix, +# or genotypes under another estimator, give the matrix (resolveLdInput()). +# Without `nSample`, genotypes count their own rows, as resolveLdInput() +# does. +# @noRd +.dentistLdInput <- function(R, X, nSample, ldMethod) { + .ldRequireOneSource(R, X) + if (is.null(R) && identical(ldMethod, "sample")) { + X <- as.matrix(X) + return(list(F = .ldFactorFromDosage(X), nSample = nSample %||% nrow(X))) + } + ld <- resolveLdInput( + R = R, + X = X, + nSample = nSample, + needNSample = TRUE, + ldMethod = ldMethod ) + list(R = ld$R, nSample = ld$nSample) } .dentistWindows <- function( sumStat, - ldMat, + ld, nSample, windowMode, windowSize, minDim, - methodArgs + dentistParam ) { if (windowMode == "distance") { windowDividedRes <- segmentByDist( @@ -254,10 +296,10 @@ resolveLdInput <- function( seq_len(nrow(windowDividedRes)), .dentistWindowAt, sumStat = sumStat, - ldMat = ldMat, + ld = ld, nSample = nSample, windowDividedRes = windowDividedRes, - methodArgs = methodArgs + dentistParam = dentistParam ) mergeWindows(dentistResultByWindow, windowDividedRes) } @@ -274,8 +316,10 @@ resolveLdInput <- function( #' @param sumStat A data frame containing summary statistics, including 'pos' or #' 'position' and 'z' or 'zscore' columns. #' @param R Square LD correlation matrix. Provide either \code{R} or \code{X}. -#' @param X Genotype matrix (samples x SNPs). If provided, LD is computed via -#' \code{computeLd(X)} and \code{nSample} defaults to \code{nrow(X)}. +#' @param X Genotype matrix (samples x SNPs). With \code{ldMethod = "sample"} +#' DENTIST works on the standardized genotypes and never forms the LD +#' matrix; any other \code{ldMethod} computes LD via \code{computeLd(X)}. +#' \code{nSample} defaults to \code{nrow(X)}. #' @param nSample The number of samples in the LD reference panel (NOT the GWAS #' sample size). This controls the SVD truncation rank K = min(idx_size, #' nSample) * propSVD. Required when \code{R} is provided; inferred from @@ -287,7 +331,7 @@ resolveLdInput <- function( #' \code{"distance"} (default) creates windows by physical distance using #' \code{segmentByDist} (C++ \code{--wind-dist}), and \code{"count"} creates #' windows by variant count using \code{segmentByCount} (C++ \code{--wind}). -#' @param methodArgs The DENTIST algorithm settings, built with +#' @param dentistParam The DENTIST algorithm settings, built with #' \code{\link{DentistParam}}: \code{pValueThreshold}, \code{propSVD}, #' \code{gcControl}, \code{nIter}, \code{gPvalueThreshold}, #' \code{duprThreshold}, \code{numThreads}, \code{correctChenEtAlBug} @@ -299,9 +343,9 @@ resolveLdInput <- function( #' 2000). In count mode: the number of variants per window (i.e., the window #' size). #' @param ldMethod Character string specifying the LD computation method when -#' \code{X} is provided. Passed to \code{computeLd}. One of \code{"sample"} -#' (default), \code{"population"}, or \code{"gcta"}. Ignored when \code{R} is -#' provided directly. +#' \code{X} is provided: \code{"sample"} (default), which uses the +#' genotypes directly, or \code{"population"} or \code{"gcta"}, which go +#' through \code{computeLd}. Ignored when \code{R} is provided directly. #' #' @return A data frame containing the imputed result and detected outliers. #' @@ -367,42 +411,38 @@ dentist <- function( windowMode = c("distance", "count"), minDim = 2000, ldMethod = "sample", - methodArgs = DentistParam() + dentistParam = DentistParam() ) { # The tunables arrive as the record rather than as nine formals of their # own: DentistParam() is already what ldMismatchQc(method =) takes and # what every helper below is spliced with, and duplicating its defaults # here left two places to change one number. - .assertMethodParam(methodArgs, "DentistParam", "methodArgs") - resolved <- resolveLdInput( - R = R, - X = X, - nSample = nSample, - needNSample = TRUE, - ldMethod = ldMethod - ) - ldMat <- resolved$R - nSample <- resolved$nSample + .assertMethodParam(dentistParam, "DentistParam") + # Genotypes become one factor of the LD here, and each window reads its + # columns; the region-wide LD is never formed. + ld <- .dentistLdInput(R, X, nSample, ldMethod) + nSample <- ld$nSample sumStat <- .dentistResolveColumns(sumStat) windowMode <- arg_match(windowMode) if (nrow(sumStat) < minDim) { - return(.dentistCallSingle(sumStat$z, ldMat, nSample, methodArgs)) + return(.dentistCallSingle(sumStat$z, ld, nSample, dentistParam)) } .dentistWindows( sumStat, - ldMat, + ld, nSample, windowMode, windowSize, minDim, - methodArgs + dentistParam ) } # --- dentistSingleWindow helpers ------------------------------------------- -# Warn on small windows; validate the LD matrix shape against zScore. -.dentistValidateInput <- function(zScore, ldMat) { +# Warn on small windows; validate the LD's shape against zScore: a square +# matrix over the variants, or a factor with one column per variant. +.dentistValidateInput <- function(zScore, ld) { if (length(zScore) < 2000) { nZ <- length(zScore) msg <- glue( @@ -414,6 +454,16 @@ dentist <- function( ) warn(msg) } + if (!is.null(ld$F)) { + if (ncol(ld$F) != length(zScore)) { + abort(glue( + "The genotypes must have one column per z-score ", + "({ncol(ld$F)} columns, {length(zScore)} z-scores)." + )) + } + return(invisible(NULL)) + } + ldMat <- ld$R if ( !is.matrix(ldMat) || nrow(ldMat) != ncol(ldMat) || @@ -428,12 +478,16 @@ dentist <- function( } # Optionally deduplicate near-perfectly-correlated variants before imputation. -.dentistDedup <- function(zScore, ldMat, duprThreshold) { +.dentistDedup <- function(zScore, ld, duprThreshold) { if (duprThreshold >= 1.0) { - return(list(zScore = zScore, ldMat = ldMat, dedupRes = NULL)) + return(list(zScore = zScore, ld = ld, dedupRes = NULL)) } rThreshold <- round(sqrt(duprThreshold) * 1000) / 1000 - dedupRes <- .findDuplicateVariants(zScore, ldMat, rThreshold) + dedupRes <- if (is.null(ld$F)) { + .findDuplicateVariants(zScore, ld$R, rThreshold) + } else { + .findDuplicateVariantsFactor(zScore, ld$F, rThreshold) + } numDup <- sum(dedupRes$dupBearer != -1) if (numDup > 0) { nZ <- length(zScore) @@ -445,7 +499,11 @@ dentist <- function( } list( zScore = dedupRes$filteredZ, - ldMat = dedupRes$filteredLD, + ld = if (is.null(ld$F)) { + list(R = dedupRes$filteredLD) + } else { + list(F = dedupRes$filteredFactor) + }, dedupRes = dedupRes ) } @@ -453,11 +511,17 @@ dentist <- function( # Run the C++ iterative imputation and snake_case the output. The C++ returns # any rsq values it capped at 1.0 in `rsqExceed`; summarize them into a single # warning here (no warning handler / shared env needed). -.dentistRunImpute <- function(ldMat, nSample, zScore, p) { +.dentistRunImpute <- function(ld, nSample, zScore, p) { verboseIter <- getOption("pecotmr.dentist.verbose", FALSE) - raw <- dentistIterativeImpute( + # The factor kernel imputes from the LD's factor without forming it. + kernel <- if (is.null(ld$F)) { + dentistIterativeImpute + } else { + dentistIterativeImputeFactor + } + raw <- kernel( # cpp11 requires exact integer types for int parameters - ldMat, + ld$R %||% ld$F, as.integer(nSample), zScore, p$pValueThreshold, @@ -515,8 +579,10 @@ dentist <- function( #' #' @param zScore Numeric vector of z-scores. #' @param R Square LD correlation matrix. Provide either \code{R} or \code{X}. -#' @param X Genotype matrix (samples x SNPs). If provided, LD is computed via -#' \code{computeLd(X)} and \code{nSample} defaults to \code{nrow(X)}. +#' @param X Genotype matrix (samples x SNPs). With \code{ldMethod = "sample"} +#' DENTIST works on the standardized genotypes and never forms the LD +#' matrix; any other \code{ldMethod} computes LD via \code{computeLd(X)}. +#' \code{nSample} defaults to \code{nrow(X)}. #' @param nSample Number of samples in the LD reference panel (NOT the GWAS #' sample size). Controls the SVD truncation rank. Required when \code{R} is #' provided; inferred from \code{X} when \code{X} is provided. @@ -531,9 +597,9 @@ dentist <- function( #' @param correctChenEtAlBug Correct the original DENTIST operator! bug. Default #' is TRUE. #' @param ldMethod Character string specifying the LD computation method when -#' \code{X} is provided. Passed to \code{computeLd}. One of \code{"sample"} -#' (default), \code{"population"}, or \code{"gcta"}. Ignored when \code{R} is -#' provided directly. +#' \code{X} is provided: \code{"sample"} (default), which uses the +#' genotypes directly, or \code{"population"} or \code{"gcta"}, which go +#' through \code{computeLd}. Ignored when \code{R} is provided directly. #' @param seed Integer or \code{NULL}. Random seed for the iterative #' variant-partitioning RNG. \code{NULL} (default) preserves the original #' DENTIST hard-coded seeds (\code{10} for the initial partition, @@ -565,16 +631,9 @@ dentistSingleWindow <- function( ldMethod = "sample", seed = NULL ) { - ld <- resolveLdInput( - R = R, - X = X, - nSample = nSample, - needNSample = TRUE, - ldMethod = ldMethod - ) + ld <- .dentistLdInput(R, X, nSample, ldMethod) nSample <- ld$nSample - ldMat <- ld$R - .dentistValidateInput(zScore, ldMat) + .dentistValidateInput(zScore, ld) p <- list( pValueThreshold = pValueThreshold, propSVD = propSVD, @@ -586,8 +645,8 @@ dentistSingleWindow <- function( seed = seed ) orgZscore <- zScore - dedup <- .dentistDedup(zScore, ldMat, duprThreshold) - imputed <- .dentistRunImpute(dedup$ldMat, nSample, dedup$zScore, p) + dedup <- .dentistDedup(zScore, ld, duprThreshold) + imputed <- .dentistRunImpute(dedup$ld, nSample, dedup$zScore, p) res <- if (duprThreshold < 1.0) { addDupsBackDentist(orgZscore, imputed, dedup$dedupRes) } else { @@ -596,21 +655,6 @@ dentistSingleWindow <- function( .dentistOutlierStat(res, pValueThreshold) } -#' Add duplicates back to DENTIST output -#' -#' This function takes the output from the DENTIST algorithm and adds back the -#' duplicated variants based on the output from the `findDuplicateVariants` -#' function. -#' @param zScore The original zScore -#' @param dentistOutput A data frame containing the output from the DENTIST -#' algorithm. -#' @param findDupOutput A list containing the output from the -#' `findDuplicateVariants` function. -#' -#' @return A data frame with duplicated variants added back and an additional -#' column indicating duplicates. -#' -#' @noRd # --- addDupsBackDentist helpers --------------------------------------------- # Validate DENTIST output vs the duplicate-bearer bookkeeping. @@ -666,6 +710,21 @@ dentistSingleWindow <- function( ) } +#' Add duplicates back to DENTIST output +#' +#' This function takes the output from the DENTIST algorithm and adds back the +#' duplicated variants based on the output from the `findDuplicateVariants` +#' function. +#' @param zScore The original zScore +#' @param dentistOutput A data frame containing the output from the DENTIST +#' algorithm. +#' @param findDupOutput A list containing the output from the +#' `findDuplicateVariants` function. +#' +#' @return A data frame with duplicated variants added back and an additional +#' column indicating duplicates. +#' +#' @noRd addDupsBackDentist <- function(zScore, dentistOutput, findDupOutput) { dupBearer <- findDupOutput$dupBearer nrowsDup <- length(dupBearer) @@ -764,37 +823,6 @@ buildSegmentResult <- function( ) } -#' Sliding Window Loop for Genomic Segmentation -#' -#' Core overlapping-window loop shared by both distance-based and count-based -#' segmentation strategies. Iterates over contiguous blocks (separated by gaps), -#' creates overlapping windows within each block using mode-specific callbacks, -#' and assembles the result. -#' -#' @param allGaps Integer vector of 1-based block boundaries from -#' \code{\link{detectGaps}}. -#' @param n Total number of positions. -#' @param ctx Named list bundling the caller's mode-specific segmentation state; -#' passed as the final argument to every callback below (so they can be -#' top-level functions rather than closures). -#' @param minBlockFn Function(blockSize, ctx) -> logical; returns TRUE if the -#' block is large enough to process. -#' @param initEndFn Function(startIdx, blockEnd, ctx) -> integer; computes the -#' initial window end index for the first window in a block. -#' @param fillFn Function(startIdx, endIdx, notStartInterval, notLastInterval, -#' ctx) -> list(start, end); computes fill boundaries for each window. -#' @param stepFn Function(startIdx, blockEnd, ctx) -> list(startIdx, endIdx); -#' advances to the next window. -#' @param adjustLastFn Optional function(startIdx, oldStartIdx, endIdx, -#' blockEnd, ctx) -> integer; adjusts startIdx when the last interval is -#' detected. Used by distance mode for small-last-interval correction. Default -#' is NULL (no adjustment). -#' @param verbose Logical; print interval info. Default is FALSE. -#' -#' @return A data frame with columns: windowIdx, windowStartIdx, windowEndIdx, -#' fillStartIdx, fillEndIdx. -#' -#' @noRd # --- slidingWindowLoop helpers ---------------------------------------------- # Generate the window tuples for one block via the mode-specific callbacks in @@ -932,6 +960,37 @@ buildSegmentResult <- function( ) } +#' Sliding Window Loop for Genomic Segmentation +#' +#' Core overlapping-window loop shared by both distance-based and count-based +#' segmentation strategies. Iterates over contiguous blocks (separated by gaps), +#' creates overlapping windows within each block using mode-specific callbacks, +#' and assembles the result. +#' +#' @param allGaps Integer vector of 1-based block boundaries from +#' \code{\link{detectGaps}}. +#' @param n Total number of positions. +#' @param ctx Named list bundling the caller's mode-specific segmentation state; +#' passed as the final argument to every callback below (so they can be +#' top-level functions rather than closures). +#' @param minBlockFn Function(blockSize, ctx) -> logical; returns TRUE if the +#' block is large enough to process. +#' @param initEndFn Function(startIdx, blockEnd, ctx) -> integer; computes the +#' initial window end index for the first window in a block. +#' @param fillFn Function(startIdx, endIdx, notStartInterval, notLastInterval, +#' ctx) -> list(start, end); computes fill boundaries for each window. +#' @param stepFn Function(startIdx, blockEnd, ctx) -> list(startIdx, endIdx); +#' advances to the next window. +#' @param adjustLastFn Optional function(startIdx, oldStartIdx, endIdx, +#' blockEnd, ctx) -> integer; adjusts startIdx when the last interval is +#' detected. Used by distance mode for small-last-interval correction. Default +#' is NULL (no adjustment). +#' @param verbose Logical; print interval info. Default is FALSE. +#' +#' @return A data frame with columns: windowIdx, windowStartIdx, windowEndIdx, +#' fillStartIdx, fillEndIdx. +#' +#' @noRd slidingWindowLoop <- function( allGaps, n, @@ -974,9 +1033,6 @@ slidingWindowLoop <- function( ) } -# Apply the quarter-distance index map `quaterIdx` `n` times to `x` (n = 1..4 -# gives the 1st..4th quarter boundary from x). Used by segmentByDist. -# @noRd # One quarter-index hop; the step index is unused, `reduce` just applies it # `n` times. # @noRd @@ -984,53 +1040,17 @@ slidingWindowLoop <- function( quaterIdx[x] } +# Apply the quarter-distance index map `quaterIdx` `n` times to `x` (n = 1..4 +# gives the 1st..4th quarter boundary from x). Used by segmentByDist. +# @noRd .nthQuaterIdx <- function(x, n, quaterIdx) { reduce(seq_len(n), .nthQuaterStep, quaterIdx = quaterIdx, .init = x) } -#' Segment Genomic Region by Distance (Original DENTIST Algorithm) -#' -#' Implements the same windowing/segmentation algorithm as the original DENTIST -#' C++ binary's \code{segmentingByDist} function. Windows are created using -#' quarter-distance SNP index lookups, with gap detection for centromeres and -#' large gaps. -#' -#' @param pos Integer vector of base pair positions (must be sorted). -#' @param maxDist Maximum distance (bp) between SNPs for windowing. Default is -#' 2000000. -#' @param minDim Minimum number of SNPs per window. Default is 2000. -#' @param verbose Logical; print segmentation info. Default is FALSE. -#' -#' @return A data frame with columns: windowIdx, windowStartIdx, windowEndIdx, -#' fillStartIdx, fillEndIdx. Start indices are 1-based inclusive; end indices -#' (windowEndIdx, fillEndIdx) are 1-based exclusive (one past last element), -#' matching the C++ convention. Use \code{startIdx:(endIdx - 1)} for R -#' inclusive ranges. -#' -#' @details -#' This is a faithful R translation of the C++ \code{segmentingByDist} function. -#' The algorithm: -#' \enumerate{ -#' \item Precomputes for each SNP: the index of the farthest SNP within -#' \code{maxDist}, -#' and the index of the SNP at \code{maxDist/4} distance. -#' \item Detects gaps > \code{maxDist/4} in the position vector (e.g., -#' centromeres). -#' \item Creates overlapping windows that slide by half the distance cutoff, -#' with fill -#' regions covering the inner three-quarters of each window. -#' \item The first window's fill starts at the window start; the last -#' window's fill -#' ends at the window end. -#' } -#' -#' @seealso \code{\link{dentistSingleWindow}}, \code{\link{dentist}} -#' -#' @noRd # --- segmentByDist helpers -------------------------------------------------- -# For each SNP, the last SNP index within cutoff/4 distance (clamped to [1, n]). -# For each SNP, the index of the last SNP within a quarter of `cutoff`. +# For each SNP, the index of the last SNP within a quarter of `cutoff`, +# clamped to [1, n]. # # The C++ original walks a second pointer forward, which is the same thing as # counting how many positions fall strictly below each target -- that is @@ -1087,6 +1107,45 @@ slidingWindowLoop <- function( ) } +#' Segment Genomic Region by Distance (Original DENTIST Algorithm) +#' +#' Implements the same windowing/segmentation algorithm as the original DENTIST +#' C++ binary's \code{segmentingByDist} function. Windows are created using +#' quarter-distance SNP index lookups, with gap detection for centromeres and +#' large gaps. +#' +#' @param pos Integer vector of base pair positions (must be sorted). +#' @param maxDist Maximum distance (bp) between SNPs for windowing. Default is +#' 2000000. +#' @param minDim Minimum number of SNPs per window. Default is 2000. +#' @param verbose Logical; print segmentation info. Default is FALSE. +#' +#' @return A data frame with columns: windowIdx, windowStartIdx, windowEndIdx, +#' fillStartIdx, fillEndIdx. Start indices are 1-based inclusive; end indices +#' (windowEndIdx, fillEndIdx) are 1-based exclusive (one past last element), +#' matching the C++ convention. Use \code{startIdx:(endIdx - 1)} for R +#' inclusive ranges. +#' +#' @details +#' This is a faithful R translation of the C++ \code{segmentingByDist} function. +#' The algorithm: +#' \enumerate{ +#' \item Precomputes for each SNP: the index of the farthest SNP within +#' \code{maxDist}, +#' and the index of the SNP at \code{maxDist/4} distance. +#' \item Detects gaps > \code{maxDist/4} in the position vector (e.g., +#' centromeres). +#' \item Creates overlapping windows that slide by half the distance cutoff, +#' with fill +#' regions covering the inner three-quarters of each window. +#' \item The first window's fill starts at the window start; the last +#' window's fill +#' ends at the window end. +#' } +#' +#' @seealso \code{\link{dentistSingleWindow}}, \code{\link{dentist}} +#' +#' @noRd segmentByDist <- function( pos, maxDist = 2000000, @@ -1171,6 +1230,22 @@ segmentByCount <- function(pos, maxCount, gapDist = 1e6, verbose = FALSE) { ) } +# Window `k`'s rows, indexed globally and trimmed to that window's fill range. +# @noRd +.dentistMergeWindowAt <- function(k, dentistResultByWindow, windowDividedRes) { + imputedK <- dentistResultByWindow[[k]] + offset <- windowDividedRes$windowStartIdx[k] - 1 + imputedK |> + mutate( + index_within_window = seq_len(nrow(imputedK)), + index_global = .data$index_within_window + offset + ) |> + filter( + .data$index_global >= windowDividedRes$fillStartIdx[k] & + .data$index_global < windowDividedRes$fillEndIdx[k] + ) +} + #' Merge dentist Results by Window #' #' This function merges DENTIST results by window into a single data frame. @@ -1188,22 +1263,6 @@ segmentByCount <- function(pos, maxCount, gapDist = 1e6, verbose = FALSE) { #' within the fillers and combines them into a single data frame. #' #' @noRd -# Window `k`'s rows, indexed globally and trimmed to that window's fill range. -# @noRd -.dentistMergeWindowAt <- function(k, dentistResultByWindow, windowDividedRes) { - imputedK <- dentistResultByWindow[[k]] - offset <- windowDividedRes$windowStartIdx[k] - 1 - imputedK |> - mutate( - index_within_window = seq_len(nrow(imputedK)), - index_global = .data$index_within_window + offset - ) |> - filter( - .data$index_global >= windowDividedRes$fillStartIdx[k] & - .data$index_global < windowDividedRes$fillEndIdx[k] - ) -} - mergeWindows <- function(dentistResultByWindow, windowDividedRes) { if (length(dentistResultByWindow) != nrow(windowDividedRes)) { abort("Different number of windows and imputed results!") @@ -1253,12 +1312,22 @@ mergeWindows <- function(dentistResultByWindow, windowDividedRes) { # --- slalom helpers --------------------------------------------------------- -# Require exactly one of R (LD matrix) or X (genotype matrix). -# Lead-variant LD column. `R` has already been resolved from `X` where the -# caller gave genotypes, so there is one source here and `ldMethod` has -# already been applied. +# Lead-variant LD column. From genotypes under the sample estimator it is read +# off X's LD factor -- crossprod(F, F[, lead]) / (n - 1), computeLd()'s column +# exactly -- without forming the p x p matrix. The population and GCTA +# estimators treat missing calls their own way, so they still build it +# through resolveLdInput(), as dentist does. #' @importFrom checkmate assertMatrix -.slalomLeadR <- function(zScore, R, leadIdx) { +.slalomLeadR <- function(zScore, R, X, leadIdx, ldMethod) { + if (is.null(R) && identical(ldMethod, "sample")) { + factor <- .ldFactorFromDosage(as.matrix(X)) + assertMatrix(factor, ncols = length(zScore)) + lead <- crossprod(factor, factor[, leadIdx, drop = FALSE]) + # The lead's correlation with itself is 1 exactly, as on the + # matrix's unit diagonal, not 1 give or take rounding. + return(replace(drop(lead) / (nrow(factor) - 1), leadIdx, 1)) + } + R <- resolveLdInput(R = R, X = X, ldMethod = ldMethod)$R assertMatrix(R, nrows = length(zScore), ncols = length(zScore)) R[, leadIdx] } @@ -1326,12 +1395,15 @@ mergeWindows <- function(dentistResultByWindow, windowDividedRes) { #' p-values from z-scores assuming a two-sided standard normal distribution. #' #' Provide either an LD correlation matrix \code{R} or a genotype matrix -#' \code{X} (from which LD is derived automatically via \code{computeLd}). +#' \code{X}. SLALOM needs only the lead variant's LD with the others, so with +#' \code{ldMethod = "sample"} it computes that one column from the genotypes +#' and never forms the LD matrix. #' #' @param zScore Numeric vector of z-scores corresponding to each variant. #' @param R Square LD correlation matrix. Provide either \code{R} or \code{X}. -#' @param X Genotype matrix (samples x SNPs). If provided, LD is computed via -#' \code{computeLd(X)}. +#' @param X Genotype matrix (samples x SNPs). With \code{ldMethod = "sample"} +#' only the lead variant's LD column is computed from it; any other +#' \code{ldMethod} computes the LD matrix via \code{computeLd(X)}. #' @param standardError Optional numeric vector of standard errors corresponding #' to each z-score. If not provided, a default value of 1 is assumed for all #' variants. @@ -1344,9 +1416,9 @@ mergeWindows <- function(dentistResultByWindow, windowDividedRes) { #' @param leadVariantChoice Character, method to choose the lead variant, either #' "pvalue" or "abf", with default "pvalue". #' @param ldMethod Character string specifying the LD computation method when -#' \code{X} is provided. Passed to \code{computeLd}. One of \code{"sample"} -#' (default), \code{"population"}, or \code{"gcta"}. Ignored when \code{R} is -#' provided directly. +#' \code{X} is provided: \code{"sample"} (default), which uses the +#' genotypes directly, or \code{"population"} or \code{"gcta"}, which go +#' through \code{computeLd}. Ignored when \code{R} is provided directly. #' @return A list containing the annotated LD matrix with ABF results, credible #' sets, lead variant, and DENTIST-S statistics; and a summary dataframe with #' aggregate statistics. @@ -1373,13 +1445,7 @@ slalom <- function( leadVariantChoice = "pvalue", ldMethod = "sample" ) { - # Resolve the LD the same way dentist does, so `ldMethod` selects the - # estimator it advertises. This used to correlate the lead column of `X` - # with cor() directly, which skipped computeLd entirely: "population" - # and "gcta" were accepted and silently given sample LD. Building the - # full matrix is the cost of honouring the choice, and is what the - # sibling QC method already pays on the same input. - R <- resolveLdInput(R = R, X = X, ldMethod = ldMethod)$R + .ldRequireOneSource(R, X) # One-sided p-value matching the original Python (stats.norm.cdf): lead is # the most negative z-score when leadVariantChoice == "pvalue". pvalue <- pnorm(zScore) @@ -1396,7 +1462,7 @@ slalom <- function( } else { which.max(prob) } - rLead <- .slalomLeadR(zScore, R, leadIdx) + rLead <- .slalomLeadR(zScore, R, X, leadIdx, ldMethod) ds <- .slalomDentistS( zScore, rLead, @@ -1492,28 +1558,6 @@ autoDecision <- function(df, highCorrCols) { # RAISS: regression-based sumstats imputation # ============================================================================= -#' Core RAISS implementation for a single LD matrix -#' -#' @param refPanel A data frame containing 'chrom', 'pos', 'variant_id', 'A1', -#' and 'A2'. -#' @param knownZscores A data frame containing 'chrom', 'pos', 'variant_id', -#' 'A1', 'A2', and 'z' values. -#' @param ldMatrix A square matrix of dimension equal to the number of rows in -#' refPanel. -#' @param lamb Regularization term added to the diagonal of the ldMatrix. -#' @param rcond Threshold for filtering eigenvalues in the pseudo-inverse -#' computation. -#' @param r2Threshold R square threshold below which SNPs are filtered from the -#' output. -#' @param minimumLd Minimum LD score threshold for SNP filtering. -#' @param verbose Logical indicating whether to print progress information. -#' -#' @return A list containing filtered and unfiltered results, and filtered LD -#' matrix. -#' @importFrom MASS ginv -#' @importFrom dplyr arrange -#' @noRd -# Drop LD-matrix rows/cols for variants filtered out of the imputation result. # Panel rows that are unsafe to impute, over and above being already known. # # Both cases come from a reference panel that carries a variant and its own @@ -1614,15 +1658,18 @@ autoDecision <- function(df, highCorrCols) { if (is.null(refPanel$chrom) || is.null(knownZ$chrom) || n == 0L) { return(rep(FALSE, n)) } - key <- function(chrom, pos) { - str_c(canonChrom(as.character(chrom)), ":", as.character(pos)) - } is_in( - key(refPanel$chrom, refPanel$pos), - key(knownZ$chrom, knownZ$pos) + .raissPositionKey(refPanel$chrom, refPanel$pos), + .raissPositionKey(knownZ$chrom, knownZ$pos) ) } +# Canonical "chrom:pos" keys, so a "chr1"/"1" difference is one site. +# @noRd +.raissPositionKey <- function(chrom, pos) { + str_c(canonChrom(as.character(chrom)), ":", as.character(pos)) +} + # Imputation targets: panel rows the sumstats do not carry, minus the rows no # imputed value could be trusted for and the rows sitting at a position the # GWAS already typed. @@ -1634,6 +1681,8 @@ autoDecision <- function(df, highCorrCols) { which(candidate & !unsafe & !observed) } +# Drop LD-matrix rows/cols for variants filtered out of the imputation result. +# @noRd .raissFilterLd <- function(ldMatrix, refPanel, resultFilter) { filteredOutVariant <- setdiff(refPanel$variant_id, resultFilter$variant_id) if (length(filteredOutVariant) > 0) { @@ -1644,6 +1693,27 @@ autoDecision <- function(df, highCorrCols) { } } +#' Core RAISS implementation for a single LD matrix +#' +#' @param refPanel A data frame containing 'chrom', 'pos', 'variant_id', 'A1', +#' and 'A2'. +#' @param knownZscores A data frame containing 'chrom', 'pos', 'variant_id', +#' 'A1', 'A2', and 'z' values. +#' @param ldMatrix A square matrix of dimension equal to the number of rows in +#' refPanel. +#' @param lamb Regularization term added to the diagonal of the ldMatrix. +#' @param rcond Threshold for filtering eigenvalues in the pseudo-inverse +#' computation. +#' @param r2Threshold R square threshold below which SNPs are filtered from the +#' output. +#' @param minimumLd Minimum LD score threshold for SNP filtering. +#' @param verbose Logical indicating whether to print progress information. +#' +#' @return A list containing filtered and unfiltered results, and filtered LD +#' matrix. +#' @importFrom MASS ginv +#' @importFrom dplyr arrange +#' @noRd raissSingleMatrix <- function( refPanel, knownZscores, @@ -1684,33 +1754,6 @@ raissSingleMatrix <- function( ) } -#' Core RAISS implementation from a genotype matrix X (SVD-based) -#' -#' Performs the same imputation as \code{raissSingleMatrix} but works directly -#' with the genotype matrix X instead of the LD correlation matrix R. This -#' avoids forming the p x p LD matrix, saving O(p^2) memory and O(np^2) compute. -#' -#' The reformulation is mathematically exact: using the thin SVD of Xt (the -#' known variant columns), all RAISS quantities (mu, var, ld_score) are computed -#' in the SVD basis without ever forming R = X'X/(n-1). -#' -#' @param refPanel A data frame containing 'chrom', 'pos', 'variant_id', 'A1', -#' and 'A2'. -#' @param knownZscores A data frame containing 'chrom', 'pos', 'variant_id', -#' 'A1', 'A2', and 'z' values. -#' @param X Centered and scaled genotype matrix (nSamples x pVariants). Column -#' order must match the variant order in refPanel. -#' @param lamb Regularization term (same role as in the LD-based path). -#' @param svdTol Relative tolerance for filtering small singular values in the -#' SVD of Xt. -#' @param r2Threshold R square threshold below which SNPs are filtered from the -#' output. -#' @param minimumLd Minimum LD score threshold for SNP filtering. -#' @param verbose Logical indicating whether to print progress information. -#' -#' @return A list containing filtered and unfiltered results, and ldMat = NULL. -#' @importFrom dplyr arrange -#' @noRd # --- raissSingleMatrixFromX helpers ---------------------------------------- # Early-return cases: no known variants (NULL) or nothing to impute (knowns). @@ -1745,7 +1788,24 @@ raissSingleMatrix <- function( # SVD-based imputation from the genotype matrix (avoids forming R). Computes # X' %*% [w | U] in a single BLAS call to skip an O(n*m) copy of X_unknown. -.raissSvdImpute <- function(X, knowns, unknowns, zt, lamb, svdTol, nSamples) { +# +# The LD route pseudo-inverts the ridged LD of the known variants with +# MASS::ginv(tol = rcond), which drops every eigen-direction whose eigenvalue +# is below `rcond` times the largest. Here those eigenvalues are +# (d^2 + cReg) / (nSamples - 1), so the same directions are dropped and the +# two routes impute identically. Directions outside the genotypes' span carry +# no LD with the variants being imputed, so they change neither route. The LD +# score inverts nothing and keeps every direction. +.raissSvdImpute <- function( + X, + knowns, + unknowns, + zt, + lamb, + rcond, + svdTol, + nSamples +) { # Thin SVD of the known columns (n x k -> U: n x r, d: r, V: k x r). Xt <- X[, knowns, drop = FALSE] svdResult <- .safeSvd(Xt, tol = svdTol) @@ -1756,9 +1816,11 @@ raissSingleMatrix <- function( cReg <- lamb * (nSamples - 1) d2 <- d^2 d2PlusC <- d2 + cReg - # w = U %*% diag(d / (d^2 + c)) %*% V' zt (projection of zt through SVD). + inverted <- d2PlusC > rcond * d2PlusC[[1L]] + # w = U %*% diag(d / (d^2 + c)) %*% V' zt (projection of zt through SVD), + # over the directions the pseudo-inverse keeps. VtZt <- crossprod(V, zt) - w <- U %*% (d / d2PlusC * VtZt) + w <- U %*% (inverted * d / d2PlusC * VtZt) # Single dgemm X' %*% [w | U]: col 1 (rows unknowns) -> mu; rest -> A. XtWU <- crossprod(X, cbind(w, U)) muRaw <- as.numeric(XtWU[unknowns, 1]) @@ -1767,33 +1829,59 @@ raissSingleMatrix <- function( # Variance and LD score in one pass over A^2. ASq <- A^2 rm(A) - dWeights <- cbind(d2 / d2PlusC, d2) + dWeights <- cbind(inverted * d2 / d2PlusC, d2) scores <- ASq %*% dWeights rm(ASq) nm1 <- nSamples - 1 varRaw <- (1 + lamb) - scores[, 1] / nm1 raissLdScore <- scores[, 2] / nm1^2 rm(scores) - conditionNumber <- rep(d[1] / d[length(d)], length(unknowns)) - correctInversion <- rep(TRUE, length(unknowns)) - # R2 correction (same as raissModel). - varNorm <- varInBoundaries(varRaw, lamb) - R2 <- (1 + lamb) - varNorm - mu <- muRaw / sqrt(R2) - list( - var = varNorm, - mu = mu, + .raissResult( + varRaw = varRaw, + muRaw = muRaw, + lamb = lamb, raissLdScore = raissLdScore, - conditionNumber = conditionNumber, - correctInversion = correctInversion + conditionNumber = rep(d[1] / d[length(d)], length(unknowns)), + correctInversion = rep(TRUE, length(unknowns)) ) } +#' Core RAISS implementation from a genotype matrix X (SVD-based) +#' +#' Performs the same imputation as \code{raissSingleMatrix} but works directly +#' with the genotype matrix X instead of the LD correlation matrix R. This +#' avoids forming the p x p LD matrix, saving O(p^2) memory and O(np^2) compute. +#' +#' The reformulation is mathematically exact: using the thin SVD of Xt (the +#' known variant columns), all RAISS quantities (mu, var, ld_score) are computed +#' in the SVD basis without ever forming R = X'X/(n-1). +#' +#' @param refPanel A data frame containing 'chrom', 'pos', 'variant_id', 'A1', +#' and 'A2'. +#' @param knownZscores A data frame containing 'chrom', 'pos', 'variant_id', +#' 'A1', 'A2', and 'z' values. +#' @param X Centered and scaled genotype matrix (nSamples x pVariants). Column +#' order must match the variant order in refPanel. +#' @param lamb Regularization term (same role as in the LD-based path). +#' @param rcond Eigenvalue cutoff for the pseudo-inverse, as on the LD-based +#' path: directions whose ridged eigenvalue is below \code{rcond} times the +#' largest are dropped. +#' @param svdTol Relative tolerance below which singular values of Xt are +#' treated as numerically zero. +#' @param r2Threshold R square threshold below which SNPs are filtered from the +#' output. +#' @param minimumLd Minimum LD score threshold for SNP filtering. +#' @param verbose Logical indicating whether to print progress information. +#' +#' @return A list containing filtered and unfiltered results, and ldMat = NULL. +#' @importFrom dplyr arrange +#' @noRd raissSingleMatrixFromX <- function( refPanel, knownZscores, X, lamb = 0.01, + rcond = 0.01, svdTol = 1e-8, r2Threshold = 0.6, minimumLd = 5, @@ -1814,9 +1902,10 @@ raissSingleMatrixFromX <- function( knowns, unknowns, knownZscores$z, - lamb, - svdTol, - nrow(X) + lamb = lamb, + rcond = rcond, + svdTol = svdTol, + nSamples = nrow(X) ) results <- formatRaissDf(imp, refPanel, unknowns) |> filterRaissOutput(r2Threshold, minimumLd, verbose) @@ -1831,255 +1920,34 @@ raissSingleMatrixFromX <- function( ) } -# Sequentially append RAISS block results, resolving the shared boundary variant -# (duplicated across adjacent blocks) by keeping the higher-R2 imputation. -# @noRd -.combineWithBoundaryCheck <- function(combinedResult, newResult) { - # If either is empty, simply return the non-empty one or empty data frame - if (is.null(combinedResult)) { - return(newResult) - } - if (is.null(newResult)) { - return(combinedResult) - } - - # Check if the last variant of combined matches the first of new - lastVar <- combinedResult$variant_id[nrow(combinedResult)] - firstVar <- newResult$variant_id[1] - - if (lastVar != firstVar) { - # No overlap - combine all rows - return(bind_rows(combinedResult, newResult)) - } - # The shared boundary variant is kept from whichever side imputed it - # better; two NAs, a tie, or an NA on the new side keep the existing row. - newR2 <- newResult$raissR2[1] - oldR2 <- combinedResult$raissR2[nrow(combinedResult)] - preferNew <- !is.na(newR2) && (is.na(oldR2) || newR2 > oldR2) - resolved <- if (preferNew) { - bind_rows(combinedResult[-nrow(combinedResult), ], newResult[1, ]) - } else { - combinedResult - } - # Every row of `newResult` past the boundary variant is new. - bind_rows(resolved, newResult[-1, ]) -} - -# --- raiss: genotype-matrix and LD-matrix path helpers --------------------- +# --- raiss: the genotype and LD-matrix routes -------------------------------- -# List of genotype-matrix blocks: impute each via SVD, then row-bind. -# One genotype block's imputation, or NULL when the block yields nothing. +# The genotype route: SVD-based imputation from a standardized genotype +# matrix, which never forms the p x p LD matrix. # @noRd -.raissGenotypeBlockAt <- function( - i, - refPanel, - knownZscores, - genotypeMatrix, - p -) { - if (p$verbose) { - nBlocks <- length(genotypeMatrix) - msg <- glue("Processing block {i} of {nBlocks}") - inform(msg) - } - raissSingleMatrixFromX( - refPanel, - knownZscores, - genotypeMatrix[[i]], - p$lamb, - p$svdTol, - p$r2Threshold, - p$minimumLd, - verbose = FALSE - ) -} - -.raissGenotypeBlocks <- function(refPanel, knownZscores, genotypeMatrix, p) { - if (p$verbose) { - msg <- glue( - "Processing multiple genotype matrix blocks via SVD-based ", - "imputation..." - ) - inform(msg) - } - resultsList <- compact(map( - seq_along(genotypeMatrix), - .raissGenotypeBlockAt, - refPanel = refPanel, - knownZscores = knownZscores, - genotypeMatrix = genotypeMatrix, - p = p - )) - if (length(resultsList) == 0) { - if (p$verbose) { - inform("No blocks could be processed.") - } - return(NULL) - } - combinedNofilter <- bind_rows(map(resultsList, "resultNofilter")) - combinedFilter <- bind_rows(map(resultsList, "resultFilter")) - list( - resultNofilter = combinedNofilter |> arrange(.data$pos), - resultFilter = combinedFilter |> arrange(.data$pos), - ldMat = NULL - ) -} - -# Genotype-matrix imputation path: single matrix, list of blocks, or error. .raissGenotypePath <- function(refPanel, knownZscores, genotypeMatrix, p) { - if (is.matrix(genotypeMatrix)) { - if (p$verbose) { - inform("Processing genotype matrix via SVD-based imputation...") - } - return(raissSingleMatrixFromX( - refPanel, - knownZscores, - genotypeMatrix, - p$lamb, - p$svdTol, - p$r2Threshold, - p$minimumLd, - p$verbose - )) - } - if (is.list(genotypeMatrix)) { - return(.raissGenotypeBlocks(refPanel, knownZscores, genotypeMatrix, p)) - } - abort("genotypeMatrix must be a matrix or a list of matrices.") -} - -# Impute one LD block against its reference-panel subset. -.raissLdBlockOne <- function( - refPanel, - knownZscores, - ldMatrix, - variantIndices, - blockId, - p -) { - blockVariantIds <- variantIndices$variant_id[ - variantIndices$blockId == blockId - ] - blockIndices <- match(blockVariantIds, refPanel$variant_id) - blockRefPanel <- refPanel[blockIndices, ] - blockLdMatrix <- ldMatrix$ldMatrices[[blockId]] - blockKnownZscores <- knownZscores |> - filter(is_in(.data$variant_id, blockVariantIds)) - if (nrow(blockLdMatrix) != nrow(blockRefPanel)) { - msg <- glue( - "Block {blockId} : LD matrix dimension does not match number ", - "of variants in reference panel" - ) - abort(msg) - } - raissSingleMatrix( - blockRefPanel, - blockKnownZscores, - blockLdMatrix, - p$lamb, - p$rcond, - p$r2Threshold, - p$minimumLd, - verbose = FALSE - ) -} - -# Combine per-block imputation results (boundary-dedup) + rebuild LD matrix. -.raissLdBlocksCombine <- function(resultsList) { - combinedNofilter <- reduce( - map(resultsList, "resultNofilter"), - .combineWithBoundaryCheck - ) - combinedFilter <- reduce( - map(resultsList, "resultFilter"), - .combineWithBoundaryCheck - ) - ldFilteredList <- map(resultsList, "ldMat") - variantList <- map(ldFilteredList, .ldVariantsDf) - ldMatrix <- createLdMatrix( - ldMatrices = ldFilteredList, - variants = variantList - ) - list( - resultNofilter = combinedNofilter, - resultFilter = combinedFilter, - ldMat = ldMatrix - ) -} - -# LD-block imputation path: impute each block then combine. -# One LD block's imputation, or NULL when the block yields nothing. -# @noRd -.raissLdBlockAt <- function( - blockId, - refPanel, - knownZscores, - ldMatrix, - variantIndices, - blockIds, - p -) { if (p$verbose) { - nBlocks <- length(blockIds) - msg <- glue("Processing block {blockId} of {nBlocks}") - inform(msg) + inform("Processing genotype matrix via SVD-based imputation...") } - .raissLdBlockOne( + raissSingleMatrixFromX( refPanel, knownZscores, - ldMatrix, - variantIndices, - blockId, - p + genotypeMatrix, + lamb = p$lamb, + rcond = p$rcond, + svdTol = p$svdTol, + r2Threshold = p$r2Threshold, + minimumLd = p$minimumLd, + verbose = p$verbose ) } -.raissLdBlocksPath <- function(refPanel, knownZscores, ldMatrix, p) { - if (p$verbose) { - inform("Processing multiple LD blocks...") - } - variantIndices <- ldMatrix$variantIndices - blockIds <- unique(variantIndices$blockId) - perBlock <- map( - blockIds, - .raissLdBlockAt, - refPanel = refPanel, - knownZscores = knownZscores, - ldMatrix = ldMatrix, - variantIndices = variantIndices, - blockIds = blockIds, - p = p - ) - # `resultsList[[blockId]] <- ...` named the entries only when blockIds are - # character, so keep that distinction rather than inventing names. - blockResults <- if (is.character(blockIds)) { - set_names(perBlock, blockIds) - } else { - perBlock - } - resultsList <- compact(blockResults) - if (length(resultsList) == 0) { - if (p$verbose) { - msg <- glue( - "No blocks could be processed. Check that knownZscores ", - "overlap with variants in the blocks." - ) - inform(msg) - } - return(NULL) - } - .raissLdBlocksCombine(resultsList) -} - -# Single LD-matrix imputation path (extracts matrix from a 1-block list). -.raissSingleLdPath <- function(refPanel, knownZscores, ldMatrix, p) { +# The LD-matrix route: imputation from the correlation matrix of refPanel's +# variants. +# @noRd +.raissLdPath <- function(refPanel, knownZscores, ldMatrix, p) { if (p$verbose) { - fromList <- if (!is.matrix(ldMatrix)) " from list" else "" - msg <- glue("Processing single LD matrix{fromList}...") - inform(msg) - } - if (!is.matrix(ldMatrix)) { - ldMatrix <- ldMatrix$ldMatrices[[1]] + inform("Processing LD matrix...") } raissSingleMatrix( refPanel, @@ -2093,6 +1961,65 @@ raissSingleMatrixFromX <- function( ) } +# The LD reference a raiss() call names, if any: an LdMatrix passed as +# `ldMatrix`, or the genotype panel (or mixture of panels) passed as +# `genotypes`. Genotypes are taken only that way, never as a bare matrix: +# centring, scaling, column order and allele coding are all the caller's to get +# right in a bare matrix, and every one of them fails silently. A reference +# passed through the other argument is refused rather than read the wrong way. +# @noRd +.raissReferenceOf <- function(ldMatrix, genotypes) { + if (.ldIsGenotypeRef(ldMatrix)) { + abort("Pass genotypes as `genotypes`, not `ldMatrix`.") + } + if (!is.null(genotypes) && !.ldIsGenotypeRef(genotypes)) { + abort(c( + "`genotypes` must be a genotype panel or a GenotypeMixture.", + i = "Read one with readGenotypes(), or pass LD as `ldMatrix`." + )) + } + if (.ldIsMatrixRef(ldMatrix) || !is.null(genotypes)) { + return(ldMatrix %||% genotypes) + } + if (!is.null(ldMatrix) && !is.matrix(ldMatrix)) { + abort("`ldMatrix` must be a square LD matrix or an LdMatrix.") + } + NULL +} + +# An LD reference brings its own variant table, which refPanel defaults to. +# Known and imputed rows are merged on chrom, and the table labels +# chromosomes canonically ("22"), so knownZscores has to as well; say so here +# rather than in a failed join. +# @noRd +.raissRefPanelOf <- function(ref, knownZscores) { + panel <- .refVariantsFromLdRef(ref) + sameLabels <- is.character(knownZscores$chrom) && + any(is_in(knownZscores$chrom, panel$chrom)) + if (!sameLabels) { + abort(c( + str_c( + "`knownZscores$chrom` must use the LD reference's chromosome ", + "labels." + ), + i = glue( + "They are canonical character labels, like ", + "'{panel$chrom[[1L]]}'." + ) + )) + } + panel +} + +# The genotypes' LD factor for refPanel's variants (see .ldRefFactor()), the +# way summaryStatsQc() hands it to the SVD route, re-signed into refPanel's +# allele coding. +# @noRd +.raissScaledDosage <- function(genotypes, refPanel) { + m <- .ldFromRefMatch(genotypes, refPanel$variant_id, "raiss", "error") + sweep(.ldRefFactor(genotypes, m$idx), 2L, m$sign, `*`) +} + #' Impute Summary Statistics Using LD (RAISS) #' #' An R port of the RAISS summary-statistic imputation library, described in @@ -2105,34 +2032,43 @@ raissSingleMatrixFromX <- function( #' statistics enhances evidence of functional enrichment", Bioinformatics #' 30(20):2906-2914. \doi{10.1093/bioinformatics/btu416} #' -#' This function can process either a single LD matrix or a list of LD matrices -#' for different blocks. For a list of matrices, it processes each block -#' separately and combines the results. Alternatively, it can accept a genotype -#' matrix X directly, avoiding the need to form the p x p LD matrix (memory and +#' One call imputes one region, from either of the two kinds of LD reference. +#' The LD can be given as a square correlation matrix or an +#' \code{\link{LdMatrix}}, and the imputation then inverts it. Or a genotype +#' panel from \code{\link{readGenotypes}} can be given instead, and the +#' imputation then works from the singular value decomposition of its +#' standardized dosages, never forming the p x p LD matrix (memory and #' compute savings when n << p). #' #' @param refPanel A data frame containing 'chrom', 'pos', 'variant_id', 'A1', -#' and 'A2'. +#' and 'A2'. Optional when the LD comes as an \code{LdMatrix} or a genotype +#' panel, whose own variants are used; \code{knownZscores} must then label +#' chromosomes canonically, as character (e.g. \code{"22"}). Required +#' with a plain \code{ldMatrix}, whose rows and columns follow it. #' @param knownZscores A data frame containing 'chrom', 'pos', 'variant_id', #' 'A1', 'A2', and 'z' values. -#' @param ldMatrix Either a square matrix or a list of matrices for LD blocks. -#' Provide either \code{ldMatrix} or \code{genotypeMatrix}, not both. -#' @param genotypeMatrix A centered and scaled genotype matrix (n x p) as an -#' alternative to \code{ldMatrix}. Column order must match the variant order -#' in \code{refPanel}. When provided, the imputation uses an SVD-based -#' approach that avoids forming the p x p LD matrix. +#' @param ldMatrix A square LD matrix over \code{refPanel}'s variants, in its +#' order, or an \code{LdMatrix} covering them. Provide either +#' \code{ldMatrix} or \code{genotypes}, not both. +#' @param genotypes A genotype panel (see \code{\link{readGenotypes}}) or a +#' \code{\link{GenotypeMixture}} covering the variants, as an alternative +#' to \code{ldMatrix}. Its dosages are matched to \code{refPanel} by +#' variant and allele, mean-imputed and standardized here. #' @param lamb Regularization term added to the diagonal of the ldMatrix. -#' @param rcond Threshold for filtering eigenvalues in the pseudo-inverse -#' computation (only used with ldMatrix path). -#' @param svdTol Relative tolerance for filtering small singular values (only -#' used with genotypeMatrix path). +#' @param rcond Eigenvalue cutoff for the pseudo-inverse of the known +#' variants' LD: directions whose ridged eigenvalue is below \code{rcond} +#' times the largest are dropped. Applied on both routes, so a genotype +#' panel and the LD matrix computed from it give the same imputation. +#' @param svdTol Relative tolerance below which singular values of the +#' standardized genotypes are treated as numerically zero (genotype route +#' only). #' @param r2Threshold R square threshold below which SNPs are filtered from the #' output. #' @param minimumLd Minimum LD score threshold for SNP filtering. #' @param verbose Logical indicating whether to print progress information. #' #' @return A list containing filtered and unfiltered results, and filtered LD -#' matrix (ldMat is NULL when using genotypeMatrix path). +#' matrix (ldMat is NULL on the genotype route). #' @importFrom dplyr arrange bind_rows #' @examples #' data(eqtlRegionExample) @@ -2146,10 +2082,10 @@ raissSingleMatrixFromX <- function( #' ldMatrix = cor(X)) #' @export raiss <- function( - refPanel, + refPanel = NULL, knownZscores, ldMatrix = NULL, - genotypeMatrix = NULL, + genotypes = NULL, lamb = 0.01, rcond = 0.01, svdTol = 1e-8, @@ -2165,23 +2101,31 @@ raiss <- function( minimumLd = minimumLd, verbose = verbose ) - if (!is.null(genotypeMatrix)) { - if (!is.null(ldMatrix)) { - abort("Provide either ldMatrix or genotypeMatrix, not both.") - } - return(.raissGenotypePath(refPanel, knownZscores, genotypeMatrix, p)) + if (!is.null(genotypes) && !is.null(ldMatrix)) { + abort("Provide either ldMatrix or genotypes, not both.") + } + if (is.null(genotypes) && is.null(ldMatrix)) { + abort("Provide either ldMatrix or genotypes.") + } + ref <- .raissReferenceOf(ldMatrix, genotypes) + if (!is.null(ref)) { + refPanel <- refPanel %||% .raissRefPanelOf(ref, knownZscores) + } else if (is.null(refPanel)) { + abort(str_c( + "`refPanel` is required unless the LD comes as an LdMatrix or a ", + "genotype panel." + )) } - if (is.null(ldMatrix)) { - abort("Provide either ldMatrix or genotypeMatrix.") + if (!is.null(genotypes)) { + X <- .raissScaledDosage(genotypes, refPanel) + return(.raissGenotypePath(refPanel, knownZscores, X, p)) } - isSingleMatrixCase <- is.matrix(ldMatrix) || - (is.list(ldMatrix) && - !is.null(ldMatrix$ldMatrices) && - length(ldMatrix$ldMatrices) == 1) - if (isSingleMatrixCase) { - return(.raissSingleLdPath(refPanel, knownZscores, ldMatrix, p)) + R <- if (is.null(ref)) { + ldMatrix + } else { + .ldFromRef(ref, refPanel$variant_id, label = "raiss") } - .raissLdBlocksPath(refPanel, knownZscores, ldMatrix, p) + .raissLdPath(refPanel, knownZscores, R, p) } #' @rdname RaissParam @@ -2192,6 +2136,7 @@ setClass( contains = "MethodParam", slots = c( lamb = "numeric", + rcond = "numeric", svdTol = "numeric", r2Threshold = "numeric", minimumLd = "numeric", @@ -2204,20 +2149,25 @@ setClass( #' @title Arguments For RAISS Imputation During Summary-Statistic QC #' @description The settings \code{\link{summaryStatsQc}} uses when -#' \code{impute = TRUE}. Four are forwarded to \code{\link{raiss}}; the +#' \code{impute = TRUE}. Five are forwarded to \code{\link{raiss}}; the #' other four are pecotmr's own, bounding which panel variants RAISS is #' asked to impute and how wide a window it reads. #' -#' \code{\link{raiss}} also takes an \code{rcond}, but only on its -#' LD-matrix path. QC always hands it a genotype matrix, which routes -#' through the SVD solver and uses \code{svdTol} instead, so \code{rcond} -#' is not offered here --- naming it is an error rather than a setting that -#' silently does nothing. +#' \code{rcond} applies whatever the collection's LD reference is: QC hands +#' RAISS a genotype panel's dosages, which route through the SVD solver, or +#' an \code{\link{LdMatrix}}'s correlations, which are inverted directly, +#' and both drop the same weak directions. \code{svdTol} only matters on +#' the genotype route. #' @param lamb Regularization term added to the LD diagonal. Default #' \code{0.01}. -#' @param svdTol Singular-value cutoff for the pseudo-inverse. Default -#' \code{1e-12}. Note this is tighter than \code{\link{raiss}}'s own -#' \code{1e-8} default. +#' @param rcond Eigenvalue cutoff for the pseudo-inverse of the known +#' variants' LD: directions whose ridged eigenvalue is below \code{rcond} +#' times the largest are dropped. Default \code{0.01}, as +#' \code{\link{raiss}}'s own. +#' @param svdTol Relative tolerance below which singular values of the +#' standardized genotypes are treated as numerically zero, used when the LD +#' reference is a genotype panel. Default \code{1e-12}. Note this is +#' tighter than \code{\link{raiss}}'s own \code{1e-8} default. #' @param r2Threshold Imputation \eqn{R^2} below which a variant is dropped #' from the output. Default \code{0.6}. #' @param minimumLd Minimum LD score for retaining an imputed variant. @@ -2236,6 +2186,7 @@ setClass( #' @export RaissParam <- function( lamb = 0.01, + rcond = 0.01, svdTol = 1e-12, r2Threshold = 0.6, minimumLd = 5, @@ -2247,6 +2198,7 @@ RaissParam <- function( new( "RaissParam", lamb = lamb, + rcond = rcond, svdTol = svdTol, r2Threshold = r2Threshold, minimumLd = minimumLd, @@ -2258,15 +2210,15 @@ RaissParam <- function( } # The RAISS bundle as a plain list with every field present. A constructor -# result already carries all eight -- they are all formals with defaults -- +# result already carries all nine -- they are all formals with defaults -- # and the empty list that means "no options" becomes the defaults outright, # so the read sites need no per-field fallback. # @noRd -.ssqcResolveImputeArgs <- function(imputeArgs) { - if (length(imputeArgs) == 0L) { +.ssqcResolveImputeArgs <- function(raissParam) { + if (length(raissParam) == 0L) { return(as.list(RaissParam())) } - as.list(imputeArgs) + as.list(raissParam) } #' @importFrom checkmate assertNumeric @@ -2296,39 +2248,73 @@ raissModel <- function( assertNumeric(zt) assertNumeric(sigT) assertNumeric(sigIT) - if (batch) { - conditionNumber <- if (reportConditionNumber) { - rep(kappa(sigT, exact = TRUE, norm = "2"), nrow(sigIT)) - } else { - NA - } - correctInversion <- rep(checkInversion(sigT, sigTInv), nrow(sigIT)) - } else { - conditionNumber <- if (reportConditionNumber) { - kappa(sigT, exact = TRUE, norm = "2") - } else { - NA - } - correctInversion <- checkInversion(sigT, sigTInv) - } - + checks <- .raissInversionChecks( + sigT, + sigTInv, + lamb, + nOut = if (batch) nrow(sigIT) else 1L, + reportConditionNumber = reportConditionNumber + ) varRaissLdScore <- computeVar(sigIT, sigTInv, lamb, batch) - var <- varRaissLdScore$var - raissLdScore <- varRaissLdScore$raissLdScore - - mu <- computeMu(sigIT, sigTInv, zt) - varNorm <- varInBoundaries(var, lamb) + .raissResult( + varRaw = varRaissLdScore$var, + muRaw = computeMu(sigIT, sigTInv, zt), + lamb = lamb, + raissLdScore = varRaissLdScore$raissLdScore, + conditionNumber = checks$conditionNumber, + correctInversion = checks$correctInversion + ) +} - R2 <- ((1 + lamb) - varNorm) - mu <- mu / sqrt(R2) +# The inversion's diagnostics, repeated for each of the `nOut` variants a +# batch imputes: sigT's condition number when asked for (NA otherwise), and +# whether sigTInv inverts what was inverted. That is the ridged matrix +# (diagonal 1 + lamb), so it is what the inverse is checked against; against +# the raw sigT the check could only fail, off by O(lamb). The original RAiSS +# implementation checks the ridged matrix too: its fill_diagonal() writes the +# ridge into sig_t in place. +# @noRd +.raissInversionChecks <- function( + sigT, + sigTInv, + lamb, + nOut, + reportConditionNumber +) { + conditionNumber <- if (reportConditionNumber) { + rep(kappa(sigT, exact = TRUE, norm = "2"), nOut) + } else { + NA + } + sigTRidged <- `diag<-`(sigT, 1 + lamb) + list( + conditionNumber = conditionNumber, + correctInversion = rep(checkInversion(sigTRidged, sigTInv), nOut) + ) +} - return(list( +# An imputation's result from its raw variances and means, as both RAiSS +# routes (raissModel(), .raissSvdImpute()) report it: the variances bounded +# by varInBoundaries(), and each mean scaled by sqrt(R2), with R2 = (1 + lamb) +# minus that variance. +# @noRd +.raissResult <- function( + varRaw, + muRaw, + lamb, + raissLdScore, + conditionNumber, + correctInversion +) { + varNorm <- varInBoundaries(varRaw, lamb) + R2 <- (1 + lamb) - varNorm + list( var = varNorm, - mu = mu, + mu = muRaw / sqrt(R2), raissLdScore = raissLdScore, conditionNumber = conditionNumber, correctInversion = correctInversion - )) + ) } #' @param imp is the output of raissModel() @@ -2502,8 +2488,10 @@ computeVar <- function(sigIT, sigTInv, lamb, batch = TRUE) { return(list(var = var, raissLdScore = raissLdScore)) } +# TRUE when sigTInv is a pseudo-inverse of sigT (sigT sigTInv sigT == sigT), +# else FALSE -- never all.equal()'s text describing the difference. checkInversion <- function(sigT, sigTInv) { - return(all.equal(sigT, sigT %*% (sigTInv %*% sigT), tolerance = 1e-5)) + isTRUE(all.equal(sigT, sigT %*% (sigTInv %*% sigT), tolerance = 1e-5)) } varInBoundaries <- function(var, lamb) { @@ -2546,31 +2534,6 @@ invertMatRecursive <- function(mat, lamb, rcond) { ) } -invertMatEigen <- function(mat, tol = 1e-3) { - eigenMat <- eigen(mat) - L <- which(cumsum(eigenMat$values) / sum(eigenMat$values) > 1 - tol)[1] - if (is.na(L)) { - # all eigen values are extremely small - msg <- glue( - "Cannot invert the input matrix because all its eigen ", - "values are negative or close to zero" - ) - abort(msg) - } - # Both guards matter when L == 1: the subscript would drop to a vector, - # and diag() of a length-one value returns an identity matrix of THAT - # ORDER -- diag(0.1) is 0x0 -- not a 1x1 matrix holding the value, so - # the product failed with "non-conformable arguments". - kept <- seq_len(L) - vectors <- eigenMat$vectors[, kept, drop = FALSE] - matInv <- vectors %*% - diag(1 / eigenMat$values[kept], nrow = L) %*% - t(vectors) - - return(matInv) -} - - # ============================================================================= # Top-level summaryStatsQc() pipeline + helpers # ============================================================================= @@ -2729,8 +2692,10 @@ DentistParam <- function( #' #' @param zScore Numeric vector of z-scores. #' @param R Square LD correlation matrix. Provide either \code{R} or \code{X}. -#' @param X Genotype matrix (samples x SNPs). If provided, LD is computed via -#' \code{computeLd} and \code{nSample} defaults to \code{nrow(X)}. +#' @param X Genotype matrix (samples x SNPs). With \code{ldMethod = "sample"} +#' both methods work on the genotypes without forming the LD matrix; any +#' other \code{ldMethod} computes LD via \code{computeLd}. \code{nSample} +#' defaults to \code{nrow(X)}. #' @param nSample Number of samples in the LD reference panel. Required when #' \code{R} is provided and \code{method = "dentist"}; inferred from \code{X} #' when \code{X} is provided. @@ -2771,7 +2736,7 @@ ldMismatchQc <- function( names(.ldMismatchCtors()), "method" ) - methodArgs <- chosen$args + engineParam <- chosen$args if (chosen$engine == "dentist") { callArgs <- list_modify( list( @@ -2781,13 +2746,13 @@ ldMismatchQc <- function( nSample = nSample, ldMethod = ldMethod ), - !!!methodArgs + !!!engineParam ) return(exec(dentistSingleWindow, !!!callArgs)) } else { callArgs <- list_modify( list(zScore, R = R, X = X, ldMethod = ldMethod), - !!!methodArgs + !!!engineParam ) qcResults <- exec(slalom, !!!callArgs) # Standardize output: slalom uses "outliers", rename to "outlier" for @@ -2838,7 +2803,7 @@ effectiveN <- function(nCase, nControl) { # ============================================================================= # summaryStatsQc -- SumStats-input QC pipeline (replaces the previous -# data.frame/LdData/QcResult-based summaryStatsQc and rssBasicQc). +# data.frame- and QcResult-based summaryStatsQc and rssBasicQc). # ============================================================================= # Convert one entry's GRanges into a flat tibble with the column shape @@ -2857,18 +2822,18 @@ effectiveN <- function(nCase, nControl) { # Build a refVariants data.frame (chrom, pos, A1, A2, variant_id) from the # panel's own variant ranges so harmonizeAlleles can join by (chrom, pos). -# The id comes from `.ldSketchMatchIds()`, not the raw SNP label: a panel entry +# The id comes from `.ldRefMatchIds()`, not the raw SNP label: a panel entry # that spells a tag where an allele belongs still harmonizes off its A1/A2 # columns, and the repaired id is what the LD lookups downstream can find. -.refVariantsFromSketch <- function(ldSketch) { - gr <- .ldSketchRanges(ldSketch) +.refVariantsFromLdRef <- function(ldReference) { + gr <- .ldRefRanges(ldReference) mc <- S4Vectors::mcols(gr) data.frame( - chrom = .ldSketchChrom(ldSketch), + chrom = .ldRefChrom(ldReference), pos = as.integer(GenomicRanges::start(gr)), A1 = as.character(mc$A1), A2 = as.character(mc$A2), - variant_id = .ldSketchMatchIds(ldSketch), + variant_id = .ldRefMatchIds(ldReference), stringsAsFactors = FALSE ) } @@ -2930,46 +2895,38 @@ effectiveN <- function(nCase, nControl) { # Shared entry-to-sumstat data.frame converter # ----------------------------------------------------------------------------- -# Internal: convert one sumstat-entry GRanges into a flat data.frame with -# (variant_id, chrom, pos, A1, A2) base columns plus a configurable set -# of stats columns (z, beta, se, N, maf). Shared by the four pipelines -# that walk sumstats GRanges (fineMappingPipeline, twasWeights, -# ctwasPipeline, colocboostPipeline). -# -# `require` character vector of mcol names that MUST be present; errors -# when any is missing. Use this for the strict callers -# (e.g. `.fmExtractZn` needs SNP + Z + N to proceed). -# `derive` when "zFromBetaSe" and `z` is absent but BETA + SE are -# present, set z := BETA/SE. Default "none". -# `label` error-message prefix for missing-`require` errors. -# `keepChrPrefix` when TRUE keep the seqname as-is ("chr1"); when FALSE, -# strip any leading "chr" so callers that expect numeric -# chrom (ctwas, colocboost) see "1". # Base (variant_id/chrom/pos/A1/A2) frame for one sumstat-entry GRanges. +# @noRd .entryDfBase <- function(gr, mc, chr) { - colOr <- function(name) { - if (is_in(name, colnames(mc))) { - as.character(mc[[name]]) - } else { - rep(NA_character_, length(gr)) - } - } + n <- length(gr) tibble( - variant_id = colOr("SNP"), + variant_id = .entryDfCharColumn("SNP", mc, n), chrom = chr, pos = unname(as.integer(GenomicRanges::start(gr))), - A1 = colOr("A1"), - A2 = colOr("A2") + A1 = .entryDfCharColumn("A1", mc, n), + A2 = .entryDfCharColumn("A2", mc, n) ) } -# Append the optional numeric stat columns present on the entry's mcols. +# One mcols column as character, or NA for each of the `n` variants when the +# entry does not carry it. +# @noRd +.entryDfCharColumn <- function(name, mc, n) { + if (is_in(name, colnames(mc))) { + as.character(mc[[name]]) + } else { + rep(NA_character_, n) + } +} + # One mcols column as numeric. # @noRd .entryStatColumn <- function(src, mc) { as.numeric(mc[[src]]) } +# Append the optional numeric stat columns present on the entry's mcols. +# @noRd .entryDfAddStats <- function(df, mc) { statMap <- c( z = "Z", @@ -3009,6 +2966,22 @@ effectiveN <- function(nCase, nControl) { mutate(df, maf = pmin(.data$af, 1 - .data$af)) } +# Convert one sumstat-entry GRanges into a flat data.frame with +# (variant_id, chrom, pos, A1, A2) base columns plus a configurable set +# of stats columns (z, beta, se, N, maf). Shared by the four pipelines +# that walk sumstats GRanges (fineMappingPipeline, twasWeights, +# ctwasPipeline, colocboostPipeline). +# +# `require` character vector of mcol names that MUST be present; errors +# when any is missing. Use this for the strict callers +# (e.g. `.fmExtractZn` needs SNP + Z + N to proceed). +# `derive` when "zFromBetaSe" and `z` is absent but BETA + SE are +# present, set z := BETA/SE. Default "none". +# `label` error-message prefix for missing-`require` errors. +# `keepChrPrefix` when TRUE keep the seqname as-is ("chr1"); when FALSE, +# strip any leading "chr" so callers that expect numeric +# chrom (ctwas, colocboost) see "1". +# @noRd .entryToSumstatDf <- function( gr, require = character(0), @@ -3085,33 +3058,70 @@ effectiveN <- function(nCase, nControl) { # bookkeeping that DENTIST's addDupsBackDentist uses to splice the # dropped variants back into the output. .findDuplicateVariants <- function(z, ld, rThreshold) { + walk <- .dupWalk(z, rThreshold, list(R = ld)) + kept <- walk$dupBearer == -1 + c( + list( + filteredZ = z[kept], + filteredLD = ld[kept, kept, drop = FALSE] + ), + walk + ) +} + +# .findDuplicateVariants() from a factor of the LD: the same walk, its LD rows +# computed a stripe at a time from the factor, so the p x p matrix is never +# formed; the duplicates are dropped from the factor's columns. +# @noRd +.findDuplicateVariantsFactor <- function(z, factor, rThreshold) { + walk <- .dupWalk(z, rThreshold, list(F = factor)) + kept <- walk$dupBearer == -1 + c( + list( + filteredZ = z[kept], + filteredFactor = factor[, kept, drop = FALSE] + ), + walk + ) +} + +# The duplicate walk: left to right, each variant not yet marked a duplicate +# anchors every later unmarked variant whose |r| with it exceeds rThreshold. +# Its LD rows are read from the LD source `ld` (.ldSourceRows()) a stripe of +# anchors at a time, so a factor never has to supply more than a stripe. +# @noRd +.dupWalk <- function(z, rThreshold, ld) { p <- length(z) dupBearer <- rep(-1, p) corABS <- rep(0, p) sign <- rep(1, p) count <- 1L minValue <- 1 + blockSize <- .ldStripeSize() + block <- NULL + blockStart <- 0L for (i in seq_len(p - 1L)) { if (dupBearer[i] != -1) { next } + if (is.null(block) || i >= blockStart + nrow(block)) { + blockStart <- i + block <- .ldSourceRows(ld, i:min(i + blockSize - 1L, p - 1L)) + } + row <- block[i - blockStart + 1L, ] idx <- (i + 1L):p - corVec <- abs(ld[i, idx]) + corVec <- abs(row[idx]) dupIdx <- which(dupBearer[idx] == -1 & corVec > rThreshold) if (length(dupIdx) > 0) { j <- idx[dupIdx] - sign[j] <- if_else(ld[i, j] < 0, -1, sign[j]) + sign[j] <- if_else(row[j] < 0, -1, sign[j]) corABS[j] <- corVec[dupIdx] dupBearer[j] <- count } minValue <- min(minValue, min(corVec)) count <- count + 1L } - filteredZ <- z[dupBearer == -1] - filteredLD <- ld[dupBearer == -1, dupBearer == -1, drop = FALSE] list( - filteredZ = filteredZ, - filteredLD = filteredLD, dupBearer = dupBearer, corABS = corABS, sign = sign, @@ -3215,18 +3225,18 @@ effectiveN <- function(nCase, nControl) { } # Apply the panel-vs-sumstats allele harmonization using the slim -# harmonizeAlleles against the ldSketch's variant info. Threads the +# harmonizeAlleles against the ldReference's variant info. Threads the # variant-level filters (indels, strand-ambiguous, duplicates) through # so the LD-panel-anchored pass handles them in a single sweep. -.matchAgainstSketch <- function( +.matchAgainstLdRef <- function( df, - ldSketch, + ldReference, matchMinProp, removeIndels = FALSE, removeStrandAmbiguous = TRUE, removeDups = TRUE ) { - refVariants <- .refVariantsFromSketch(ldSketch) + refVariants <- .refVariantsFromLdRef(ldReference) flipCandidates <- c("Z", "BETA") colToFlip <- intersect(flipCandidates, colnames(df)) if (length(colToFlip) == 0L) { @@ -3634,38 +3644,64 @@ effectiveN <- function(nCase, nControl) { # leaves the drop to onMissing here, so they are dropped here (keeping df # aligned with the returned LD) rather than aborting the run. # -# Returns list(R, df, dropped). `R` is NULL when no variant is panel-supported, -# and `df` is empty then -- each caller shapes its own empty result. +# Returns list(R, X, df, dropped): a genotype reference gives X, the factor of +# its LD (.ldFactorFromRef()), which the mismatch QC reads directly; an +# LdMatrix gives the matrix R. Both are NULL when no variant is +# panel-supported, and `df` is empty then -- each caller shapes its own empty +# result. .qcPanelLdMatrix() turns either into the matrix for a step that +# needs one. # @noRd -.qcPanelSupportedLd <- function(df, ldSketch, label) { +.qcPanelSupportedLd <- function(df, ldReference, label) { nIn <- nrow(df) - raw <- .ldFromSketch(ldSketch, df$SNP, label = label, onMissing = "drop") + isFactor <- .ldIsGenotypeRef(ldReference) + ldFrom <- if (isFactor) .ldFactorFromRef else .ldFromRef + raw <- ldFrom(ldReference, df$SNP, label = label, onMissing = "drop") if (is.null(raw)) { - return(list(R = NULL, df = df[0L, , drop = FALSE], dropped = nIn)) + return(list(df = df[0L, , drop = FALSE], dropped = nIn)) } keptIds <- attr(raw, "keptVariantIds") - R <- `attr<-`(raw, "keptVariantIds", NULL) - if (is.null(keptIds) || length(keptIds) >= nIn) { - return(list(R = R, df = df, dropped = 0L)) + ld <- `attr<-`(raw, "keptVariantIds", NULL) + supported <- if (is.null(keptIds) || length(keptIds) >= nIn) { + list(df = df, dropped = 0L) + } else { + list( + df = filter(df, is_in(.data$SNP, keptIds)), + dropped = nIn - length(keptIds) + ) } - list( - R = R, - df = filter(df, is_in(.data$SNP, keptIds)), - dropped = nIn - length(keptIds) - ) + c(if (isFactor) list(X = ld) else list(R = ld), supported) } +# The LD matrix of a .qcPanelSupportedLd() result, formed from its factor +# when that is what the reference gave. # @noRd -.applyLdMismatchQcToEntry <- function(df, ldSketch, method) { +.qcPanelLdMatrix <- function(panel) { + panel$R %||% (crossprod(panel$X) / (nrow(panel$X) - 1)) +} + +# DENTIST truncates its SVD at a rank bounded by the panel's real size, so it +# needs that size; SLALOM does not, so a reference that cannot say (an +# LdMatrix built without `nSamples`) passes NULL there. +# @noRd +.qcMismatchNSample <- function(ldReference, method) { + if (identical(method, "dentist")) { + return(.ldRefRequireNSamples(ldReference, "DENTIST")) + } + n <- .ldRefRealNSamples(ldReference) + if (is.na(n)) NULL else n +} + +.applyLdMismatchQcToEntry <- function(df, ldReference, method, panel = NULL) { if (is.null(df$SNP) || any(is.na(df$SNP))) { abort("summaryStatsQc: ldMismatchQc requires SNP column on the entry.") } - panel <- .qcPanelSupportedLd( - df, - ldSketch, - "summaryStatsQc: ldMismatchQcMethod" - ) - if (is.null(panel$R)) { + panel <- panel %||% + .qcPanelSupportedLd( + df, + ldReference, + "summaryStatsQc: ldMismatchQcMethod" + ) + if (is.null(panel$R) && is.null(panel$X)) { return(list( df = panel$df, outliers = 0L, @@ -3673,13 +3709,15 @@ effectiveN <- function(nCase, nControl) { panelUnsupportedDropped = panel$dropped )) } - R <- panel$R df <- panel$df variantIds <- df$SNP + # A genotype reference's factor goes in as X: SLALOM reads only the lead + # column of it. qc <- ldMismatchQc( zScore = df$Z, - R = R, - nSample = .ldSketchNSamples(ldSketch), + R = panel$R, + X = panel$X, + nSample = .qcMismatchNSample(ldReference, method), method = method ) # slalom / dentist can leave NA in the outlier column when their @@ -3688,25 +3726,29 @@ effectiveN <- function(nCase, nControl) { # "no evidence of being an outlier" (conservative: keep the variant) # so the downstream df / sum() / IRanges construction stay finite. outlierFlags <- replace(qc$outlier, is.na(qc$outlier), FALSE) - # Attach the variant_id column so the diagnostics data.frame stays - # self-describing once it's separated from the input df. - diagnostics <- if (is.data.frame(qc)) { - cbind( - variant_id = as.character(variantIds), - qc, - stringsAsFactors = FALSE - ) - } else { - NULL - } list( df = filter(df, !outlierFlags), outliers = sum(outlierFlags), - diagnostics = diagnostics, + diagnostics = .ldMismatchDiagnostics(qc, variantIds), panelUnsupportedDropped = panel$dropped ) } +# The QC's per-variant statistics with each variant's id attached, so the +# table stays self-describing once it is separated from the entry; NULL when +# the method returned no table. +# @noRd +.ldMismatchDiagnostics <- function(qc, variantIds) { + if (!is.data.frame(qc)) { + return(NULL) + } + cbind( + variant_id = as.character(variantIds), + qc, + stringsAsFactors = FALSE + ) +} + # ----------------------------------------------------------------------------- # Signal screen: skip an entry/block with no strong signal by a chosen metric. # ----------------------------------------------------------------------------- @@ -3897,7 +3939,7 @@ effectiveN <- function(nCase, nControl) { # --- .runEntrySummaryStatsQc: RAISS imputation step helpers ---------------- # Panel/dosage window indices for the entry, scoped per chromosome. -.qcRaissWindowIdx <- function(df, ldSketch, flank) { +.qcRaissWindowIdx <- function(df, ldReference, flank) { # canonChrom on BOTH sides: the panel answers in its own seqnames # convention, so the two are only comparable once canonicalized. bounds <- tibble( @@ -3913,9 +3955,9 @@ effectiveN <- function(nCase, nControl) { # inner_join keeps only sketch SNPs whose chromosome appears in df (the # old is_in(skChrom, names(loByChr)) guard); filter keeps those inside the # [lo, hi] window. idx carries the original sketch row positions. - gr <- .ldSketchRanges(ldSketch) + gr <- .ldRefRanges(ldReference) tibble( - chrom = .ldSketchChrom(ldSketch), + chrom = .ldRefChrom(ldReference), bp = as.integer(GenomicRanges::start(gr)), idx = seq_along(gr) ) |> @@ -3960,37 +4002,81 @@ effectiveN <- function(nCase, nControl) { knownZ |> mutate(!!!extras) |> arrange(.data$pos) } -.qcRaissBuildInputs <- function(df, ldSketch, windowIdx, opts) { - windowPanel <- .refVariantsFromSketch(ldSketch)[ +.qcRaissBuildInputs <- function(df, ldReference, windowIdx, opts) { + # `refIdx` keeps each window variant's position in the reference through + # the sort, so dosage, frequencies and LD are all read in panel order. + windowPanel <- .refVariantsFromLdRef(ldReference)[ windowIdx, , drop = FALSE ] |> - mutate(variant_id = normalizeVariantId(.data$variant_id)) |> + mutate( + variant_id = normalizeVariantId(.data$variant_id), + refIdx = windowIdx + ) |> arrange(.data$pos) knownZ <- .qcRaissKnownZ(df) - # meanImpute = FALSE so per-variant missingness is still visible; the - # surviving columns are mean-imputed below, which is what meanImpute = - # TRUE did. - windowDosage <- `colnames<-`( - .ldSketchDosage(ldSketch, windowIdx, meanImpute = FALSE), - normalizeVariantId(.ldSketchMatchIds(ldSketch)[windowIdx]) - ) - windowed <- windowDosage[, windowPanel$variant_id, drop = FALSE] - keep <- .qcRaissTargetMask(windowPanel, knownZ, windowed, opts) - refPanel <- windowPanel[keep, , drop = FALSE] + rp <- opts$raissParam + windowed <- if (.ldIsPanel(ldReference)) { + # meanImpute = FALSE so per-variant missingness is still visible; the + # surviving columns are mean-imputed below. + .ldRefDosage(ldReference, windowPanel$refIdx, meanImpute = FALSE) + } + keep <- .qcRaissTargetMask( + windowPanel, + knownZ, + .qcRaissTargetStats(ldReference, windowPanel$refIdx, windowed, rp), + opts, + nSamples = .ldRefMacSamples(ldReference, rp$macCutoff) + ) + c( + list( + refPanel = select(windowPanel[keep, , drop = FALSE], -"refIdx"), + knownZ = knownZ, + nDroppedTargets = sum(!keep) + ), + .qcRaissLdInput(ldReference, windowPanel$refIdx[keep], windowed, keep) + ) +} + +# What RAISS imputes from: a panel's standardized dosages or a mixture's +# stacked factor, for its singular-value route, or an LdMatrix's block over +# the kept variants. +# @noRd +.qcRaissLdInput <- function(ldReference, refIdx, windowed, keep) { + if (.ldIsMatrixRef(ldReference)) { + return(list(ldMatrix = .ldRefLdAt(ldReference, refIdx))) + } + if (.ldIsMixtureRef(ldReference)) { + return(list(genotypeMatrix = .ldRefFactor(ldReference, refIdx))) + } scaled <- scale(.qtlMeanImpute(windowed[, keep, drop = FALSE])) - scaledDosage <- replace(scaled, is.na(scaled), 0) - list( - refPanel = refPanel, - knownZ = knownZ, - scaledDosage = scaledDosage, - nDroppedTargets = sum(!keep) + list(genotypeMatrix = replace(scaled, is.na(scaled), 0)) +} + +# The per-variant statistics the target cutoffs are judged on, or NULL when +# no cutoff is set. A panel of real genotypes measures them on the window's +# dosages, already in hand; a sketch or an LdMatrix answers from what it +# records (see .ldRefVariantStats()). +# @noRd +.qcRaissTargetStats <- function(ldReference, refIdx, windowed, rp) { + if (rp$mafCutoff <= 0 && rp$macCutoff <= 0 && rp$imissCutoff >= 1) { + return(NULL) + } + if (!is.null(windowed) && !.ldRefIsProjection(ldReference)) { + return(.qcRaissVariantStats(windowed)) + } + .ldRefVariantStats( + ldReference, + refIdx, + needMissing = rp$imissCutoff < 1, + step = "RAISS target filtering" ) } # Which reference-panel variants to keep, given the MAF / MAC / missingness -# cutoffs in `imputeArgs`. +# cutoffs in `raissParam` and the per-variant `stats` they are judged on +# (NULL when no cutoff is set). # # The cutoffs govern what RAISS is willing to IMPUTE, not what it is willing to # keep, so an observed variant survives whatever its frequency in the LD panel. @@ -4003,14 +4089,12 @@ effectiveN <- function(nCase, nControl) { # an imputation target, which is both slow and statistically pointless when the # study is much smaller than the panel. # @noRd -.qcRaissTargetMask <- function(refPanel, knownZ, dosage, opts) { - mafCutoff <- opts$imputeArgs$mafCutoff - macCutoff <- opts$imputeArgs$macCutoff - imissCutoff <- opts$imputeArgs$imissCutoff +.qcRaissTargetMask <- function(refPanel, knownZ, stats, opts, nSamples) { keep <- rep(TRUE, nrow(refPanel)) - if (mafCutoff <= 0 && macCutoff <= 0 && imissCutoff >= 1) { + if (is.null(stats)) { return(keep) } + rp <- opts$raissParam # Identified exactly as raissSingleMatrixFromX() does, so this mask cannot # disagree with the knowns/unknowns split it derives moments later. knownIds <- intersect(knownZ$variant_id, refPanel$variant_id) @@ -4020,11 +4104,11 @@ effectiveN <- function(nCase, nControl) { unsafe <- isTarget & (.raissUnsafeToImpute(refPanel$variant_id, knownZ$variant_id) | .raissObservedPosition(refPanel, knownZ)) - stats <- .qcRaissVariantStats(dosage) - effectiveMaf <- .panelEffectiveMaf(mafCutoff, macCutoff, nrow(dosage)) - fails <- (!is.na(stats$maf) & stats$maf < effectiveMaf) | - stats$missRate > imissCutoff | - is.na(stats$maf) + effectiveMaf <- .panelEffectiveMaf(rp$mafCutoff, rp$macCutoff, nSamples) + fails <- (!is.na(stats$maf) & stats$maf < effectiveMaf) | is.na(stats$maf) + if (!is.null(stats$missRate)) { + fails <- fails | stats$missRate > rp$imissCutoff + } keep & !(isTarget & fails) & !unsafe } @@ -4038,30 +4122,35 @@ effectiveN <- function(nCase, nControl) { } -# RaissParam() covers two audiences: four fields are raiss()'s own formals and +# RaissParam() covers two audiences: five fields are raiss()'s own formals and # four bound what RAISS is asked to impute. Splitting the bundle here lets the # call site splice rather than name each field, and keeps the two groups from # being confused for one another. # @noRd .raissForwardedNames <- function() { - c("lamb", "svdTol", "r2Threshold", "minimumLd") + c("lamb", "rcond", "svdTol", "r2Threshold", "minimumLd") } # @noRd -.raissForwardedArgs <- function(imputeArgs) { - as.list(imputeArgs)[.raissForwardedNames()] +.raissForwardedArgs <- function(raissParam) { + as.list(raissParam)[.raissForwardedNames()] } -# Run RAISS with per-call option defaults. +# Run RAISS on the inputs .qcRaissBuildInputs() assembled: the window's +# standardized dosages on the genotype route, or its LD block on the matrix +# route. These were built here from the collection's own LD reference, so +# they go straight to the route rather than back through raiss()'s checks. .qcRaissRun <- function(inp, opts) { - exec( - raiss, - refPanel = inp$refPanel, - knownZscores = inp$knownZ, - genotypeMatrix = inp$scaledDosage, - verbose = FALSE, - !!!.raissForwardedArgs(opts$imputeArgs) - ) + p <- c(.raissForwardedArgs(opts$raissParam), list(verbose = FALSE)) + if (!is.null(inp$genotypeMatrix)) { + return(.raissGenotypePath( + inp$refPanel, + inp$knownZ, + inp$genotypeMatrix, + p + )) + } + .raissLdPath(inp$refPanel, inp$knownZ, inp$ldMatrix, p) } # Convert the imputer output back into a QC tibble. @@ -4150,12 +4239,12 @@ effectiveN <- function(nCase, nControl) { ) } -# Optional RAISS imputation against the ldSketch. Returns updated df + audit +# Optional RAISS imputation against the ldReference. Returns updated df + audit # fields + before/after counts (imputation adds variants, so not monotonic). -.qcRaissImpute <- function(df, ldSketch, opts, lbl) { +.qcRaissImpute <- function(df, ldReference, opts, lbl) { imputeBefore <- nrow(df) - flank <- as.integer(opts$imputeArgs$flank) - windowIdx <- .qcRaissWindowIdx(df, ldSketch, flank) + flank <- as.integer(opts$raissParam$flank) + windowIdx <- .qcRaissWindowIdx(df, ldReference, flank) if (length(windowIdx) == 0L) { .qcEmit( lbl, @@ -4170,7 +4259,7 @@ effectiveN <- function(nCase, nControl) { imputeAfter = nrow(df) )) } - inp <- .qcRaissBuildInputs(df, ldSketch, windowIdx, opts) + inp <- .qcRaissBuildInputs(df, ldReference, windowIdx, opts) if (inp$nDroppedTargets > 0L) { .qcEmit( lbl, @@ -4268,18 +4357,18 @@ effectiveN <- function(nCase, nControl) { # --- .runEntrySummaryStatsQc: harmonization / kriging / mismatch steps ------ # Panel-vs-sumstats allele harmonization + counter bookkeeping. -.qcHarmonizeEntry <- function(df, ldSketch, opts, lbl) { +.qcHarmonizeEntry <- function(df, ldReference, opts, lbl) { nHarmIn <- nrow(df) - matched <- .matchAgainstSketch( + matched <- .matchAgainstLdRef( df, - ldSketch, + ldReference, matchMinProp = opts$matchMinProp, - removeIndels = opts$sumstatsFilterArgs$removeIndels, - removeStrandAmbiguous = opts$sumstatsFilterArgs$removeStrandAmbiguous, + removeIndels = opts$sumstatsFilterParam$removeIndels, + removeStrandAmbiguous = opts$sumstatsFilterParam$removeStrandAmbiguous, removeDups = TRUE ) harmCounts <- attr(matched, "qcCounts") - # Re-key SNP to the harmonized id: .matchAgainstSketch rewrites variant_id + # Re-key SNP to the harmonized id: .matchAgainstLdRef rewrites variant_id # to the panel orientation + sign-flips swapped variants but leaves SNP; a # stale SNP makes flipped variants miss the panel in later lookups. reKeyed <- mutate( @@ -4306,7 +4395,7 @@ effectiveN <- function(nCase, nControl) { ) list( df = reKeyed, - audit = list(matchedAgainstSketch = nrow(reKeyed)), + audit = list(matchedAgainstLdReference = nrow(reKeyed)), counts = counts ) } @@ -4359,17 +4448,18 @@ effectiveN <- function(nCase, nControl) { } # @noRd -.qcKrigingFlip <- function(df, ldSketch, opts, lbl) { - if (!isTRUE(opts$alleleFlipKriging) || nrow(df) < 2L) { +.qcKrigingFlip <- function(df, ldReference, opts, lbl, panel = NULL) { + if (!.qcKrigingRuns(df, opts)) { return(list(df = df, audit = list(), count = 0L)) } nKrIn <- nrow(df) - panel <- .qcPanelSupportedLd( - df, - ldSketch, - "summaryStatsQc: kriging prefilter" - ) - if (is.null(panel$R)) { + panel <- panel %||% + .qcPanelSupportedLd( + df, + ldReference, + "summaryStatsQc: kriging prefilter" + ) + if (is.null(panel$R) && is.null(panel$X)) { return(list( df = panel$df, count = 0L, @@ -4379,9 +4469,10 @@ effectiveN <- function(nCase, nControl) { supported <- panel$df nPanelDrop <- panel$dropped .qcEmitPanelDrop(lbl, nPanelDrop, nKrIn) + # kriging_rss takes nothing but the matrix (dev/ld-factor-audit.md). kr <- krigingOutlierQc( supported$Z, - panel$R, + .qcPanelLdMatrix(panel), n = .qcKrigingN(supported, opts), variantIds = supported$SNP ) @@ -4406,16 +4497,32 @@ effectiveN <- function(nCase, nControl) { ) } +# TRUE when the kriging allele-flip check runs on this entry. +# @noRd +.qcKrigingRuns <- function(df, opts) { + isTRUE(opts$alleleFlipKriging) && nrow(df) >= 2L +} + +# TRUE when an LD-mismatch check runs on this entry. +# @noRd +.qcMismatchRuns <- function(df, opts) { + !identical(.engineOf(opts$ldMismatchQcMethod), "none") && nrow(df) >= 2L +} + # LD-mismatch QC (SLALOM / DENTIST). Retains full per-variant diagnostics. -.qcMismatchQc <- function(df, ldSketch, opts, lbl) { - if ( - identical(.engineOf(opts$ldMismatchQcMethod), "none") || - nrow(df) < 2L - ) { +# `panel` is the entry's panel-supported LD when the caller has already read +# it (see .qcPhaseLdChecks()). +.qcMismatchQc <- function(df, ldReference, opts, lbl, panel = NULL) { + if (!.qcMismatchRuns(df, opts)) { return(list(df = df, audit = list(), count = 0L)) } nMmIn <- nrow(df) - ldQc <- .applyLdMismatchQcToEntry(df, ldSketch, opts$ldMismatchQcMethod) + ldQc <- .applyLdMismatchQcToEntry( + df, + ldReference, + opts$ldMismatchQcMethod, + panel = panel + ) df <- ldQc$df baseAudit <- list( ldMismatchOutliersDropped = ldQc$outliers, @@ -4485,7 +4592,7 @@ effectiveN <- function(nCase, nControl) { nSanIn <- nrow(df) # The boundary: the bundle travels this far, and .applySanityChecks and # the per-check helpers below it keep plain scalars. - cleaning <- opts$sumstatsCleaningArgs + cleaning <- opts$sumstatsCleaningParam sanity <- exec(.applySanityChecks, df, !!!cleaning) df <- sanity$df audit <- .qcAudit( @@ -4535,8 +4642,8 @@ effectiveN <- function(nCase, nControl) { cf <- .applyContentFilters( df, mafCutoff = opts$panelFilterParam$mafCutoff, - infoCutoff = opts$sumstatsFilterArgs$infoCutoff, - nCutoff = opts$sumstatsFilterArgs$nCutoff + infoCutoff = opts$sumstatsFilterParam$infoCutoff, + nCutoff = opts$sumstatsFilterParam$nCutoff ) df <- cf$df audit <- .qcAudit( @@ -4585,7 +4692,7 @@ effectiveN <- function(nCase, nControl) { # Re-clamp the P values just derived from Z, and report how many moved. # @noRd .qcClampDerivedP <- function(df, opts) { - cleaning <- opts$sumstatsCleaningArgs + cleaning <- opts$sumstatsCleaningParam if (!isTRUE(cleaning$clampSmallP) || nrow(df) == 0L) { return(list(df = df, nClamped = 0L)) } @@ -4659,11 +4766,11 @@ effectiveN <- function(nCase, nControl) { } # Optional RAISS imputation step wrapper (guarded; threads impute counters). -.qcRaissImputeStep <- function(df, ldSketch, opts, lbl, qcCount) { +.qcRaissImputeStep <- function(df, ldReference, opts, lbl, qcCount) { if (!isTRUE(opts$impute) || nrow(df) < 1L) { return(list(df = df, audit = list(), qcCount = qcCount)) } - imp <- .qcRaissImpute(df, ldSketch, opts, lbl) + imp <- .qcRaissImpute(df, ldReference, opts, lbl) list( df = imp$df, audit = imp$audit, @@ -4690,7 +4797,7 @@ effectiveN <- function(nCase, nControl) { # Each QC phase below is (state) -> state, so the sequence reads as a pipeline # rather than a run of reassignments. `state` carries df + entryAudit + opts + -# qcCount + lbl (+ ldSketch for the panel-aware phases); a phase touches only +# qcCount + lbl (+ ldReference for the panel-aware phases); a phase touches only # the parts it owns and passes the rest through. # Per-row sanity checks. @@ -4751,7 +4858,7 @@ effectiveN <- function(nCase, nControl) { .qcPhaseHarmonize <- function(state) { harm <- .qcHarmonizeEntry( state$df, - state$ldSketch, + state$ldReference, state$opts, state$lbl ) @@ -4778,8 +4885,32 @@ effectiveN <- function(nCase, nControl) { # reads the kriging-corrected frame and the two counts are reported together. # @noRd .qcPhaseLdChecks <- function(state) { - kr <- .qcKrigingFlip(state$df, state$ldSketch, state$opts, state$lbl) - mm <- .qcMismatchQc(kr$df, state$ldSketch, state$opts, state$lbl) + # Both checks read the same panel-supported variants -- kriging only flips + # signs -- so when both run the reference is read once, for the two. + shared <- if ( + .qcKrigingRuns(state$df, state$opts) && + .qcMismatchRuns(state$df, state$opts) + ) { + .qcPanelSupportedLd(state$df, state$ldReference, "summaryStatsQc") + } + kr <- .qcKrigingFlip( + state$df, + state$ldReference, + state$opts, + state$lbl, + panel = shared + ) + mm <- .qcMismatchQc( + kr$df, + state$ldReference, + state$opts, + state$lbl, + # Kriging has already recorded the panel's drops; its output carries + # only supported variants. + panel = if (!is.null(shared)) { + list_assign(shared, df = kr$df, dropped = 0L) + } + ) list_assign( state, df = mm$df, @@ -4800,7 +4931,7 @@ effectiveN <- function(nCase, nControl) { .qcPhaseImpute <- function(state) { imp <- .qcRaissImputeStep( state$df, - state$ldSketch, + state$ldReference, state$opts, state$lbl, state$qcCount @@ -4840,7 +4971,7 @@ effectiveN <- function(nCase, nControl) { .runEntrySummaryStatsQc <- function( gr, - ldSketch, + ldReference, opts, entryLabel = NULL ) { @@ -4861,7 +4992,7 @@ effectiveN <- function(nCase, nControl) { qcCount = pre$qcCount, opts = pre$opts, lbl = pre$lbl, - ldSketch = ldSketch + ldReference = ldReference ) |> .qcPhaseHarmonize() |> .qcPhaseScreen() |> @@ -4881,6 +5012,12 @@ effectiveN <- function(nCase, nControl) { ) } +# Number of ranges an entry contributes, NULL-safe. +# @noRd +.entryRangeCount <- function(gr) { + if (is.null(gr)) 0L else length(gr) +} + # Shrink an LD-sketch GenotypeHandle to the panel variants inside the summary # statistics' per-chromosome position span. `entries` is a list/SimpleList of # per-study (or per-tuple) GRanges. A genome-wide sketch otherwise carries a @@ -4889,14 +5026,8 @@ effectiveN <- function(nCase, nControl) { # rest is dropped at load time. NULL-safe; a no-op when the span already covers # the panel. See [[.subsetGenotypeHandle]] for why this is read-safe. # @noRd -# Number of ranges an entry contributes, NULL-safe. -# @noRd -.entryRangeCount <- function(gr) { - if (is.null(gr)) 0L else length(gr) -} - -.subsetSketchToRange <- function(ldSketch, entries) { - if (is.null(ldSketch)) { +.subsetLdRefToRange <- function(ldReference, entries) { + if (is.null(ldReference)) { return(NULL) } chromAll <- unname(list_c(map(entries, .entryChrom))) @@ -4907,18 +5038,18 @@ effectiveN <- function(nCase, nControl) { if (length(pos) == 0L) { # No span to keep: a zero-variant object references no LD, so the # retained panel is empty rather than the full genome-wide sketch. - return(.emptySketch(ldSketch)) + return(.emptyLdRef(ldReference)) } bounds <- tibble(chrom = chrom, pos = pos) |> group_by(chrom) |> summarise(lo = min(pos), hi = max(pos), .groups = "drop") - gr <- .ldSketchRanges(ldSketch) + gr <- .ldRefRanges(ldReference) # left_join keeps every sketch SNP; one on a chromosome absent from the # entries gets lo/hi = NA -> inWindow FALSE (the old is_in guard). The # explicit is.na() guards force FALSE (never NA) for an absent chrom or an # NA bp, replacing the base keep[is.na(keep)] <- FALSE. keep <- tibble( - chrom = .ldSketchChrom(ldSketch), + chrom = .ldRefChrom(ldReference), bp = as.integer(GenomicRanges::start(gr)) ) |> left_join(bounds, by = "chrom") |> @@ -4929,7 +5060,7 @@ effectiveN <- function(nCase, nControl) { .data$bp <= .data$hi ) |> pull("inWindow") - .ldSketchSubset(ldSketch, keep) + .ldRefSubset(ldReference, keep) } # Shrink an LD-sketch GenotypeHandle to EXACTLY the variants present across the @@ -4937,8 +5068,8 @@ effectiveN <- function(nCase, nControl) { # matched by canonical variant id. Applied at the end of summaryStatsQc so the # retained sketch mirrors the object's final variant set. NULL-safe. # @noRd -.subsetSketchToIds <- function(ldSketch, entries) { - if (is.null(ldSketch)) { +.subsetLdRefToIds <- function(ldReference, entries) { + if (is.null(ldReference)) { return(NULL) } # Genuine variant count (ranges) across entries, read independently of the @@ -4948,11 +5079,11 @@ effectiveN <- function(nCase, nControl) { # sketch rather than blanking a panel it simply could not key. nRanges <- sum(map_int(entries, .entryRangeCount)) if (nRanges == 0L) { - return(.emptySketch(ldSketch)) + return(.emptyLdRef(ldReference)) } ids <- unname(list_c(map(entries, .entrySnpIds))) if (length(ids) == 0L) { - return(ldSketch) + return(ldReference) } # Match on the (chrom, pos, allele) tuple rather than the raw id string. # The entry ids have just been canonicalized to chr:pos:A2:A1 by the QC @@ -4961,15 +5092,15 @@ effectiveN <- function(nCase, nControl) { # -- so a panel keyed chr:pos:A1:A2 (which is how a PLINK .bim writes ids; # a .pvar writes REF:ALT and is therefore already canonical) matches # nothing under string equality and the panel is silently emptied. This is - # the matcher `.ldFromSketchMatch()` and every other LD lookup already use. - panelIds <- .ldSketchMatchIds(ldSketch) + # the matcher `.ldFromRefMatch()` and every other LD lookup already use. + panelIds <- .ldRefMatchIds(ldReference) matched <- matchVariants( panelIds, unique(ids), removeStrandAmbiguous = FALSE ) keep <- is_in(seq_along(panelIds), matched$idxA) - .ldSketchSubset(ldSketch, keep) + .ldRefSubset(ldReference, keep) } # --- summaryStatsQc orchestration helpers ---------------------------------- @@ -5018,39 +5149,39 @@ effectiveN <- function(nCase, nControl) { # Build the per-entry QC options list from the captured call parameters. .ssqcBuildOpts <- function( - sumstatsFilterArgs, + sumstatsFilterParam, panelFilterParam, skipRegion, ldMismatchQcMethod, alleleFlipKriging, effectiveN, impute, - imputeArgs, + raissParam, matchMinProp, - sumstatsCleaningArgs, + sumstatsCleaningParam, keepVariants, - signalScreenArgs + signalScreenParam ) { # Built explicitly rather than by subsetting a captured environment with a # character vector: a renamed argument is now an error, not a NULL entry. opts <- list( - sumstatsFilterArgs = sumstatsFilterArgs, + sumstatsFilterParam = sumstatsFilterParam, panelFilterParam = panelFilterParam, skipRegion = skipRegion, ldMismatchQcMethod = ldMismatchQcMethod, alleleFlipKriging = alleleFlipKriging, effectiveN = effectiveN, impute = impute, - imputeArgs = imputeArgs, + raissParam = raissParam, matchMinProp = matchMinProp, - sumstatsCleaningArgs = sumstatsCleaningArgs + sumstatsCleaningParam = sumstatsCleaningParam ) # nCase / nControl / nForPip are deliberately absent here: they are # per-entry, and `.ssqcEntryOpts()` adds them for the entry being run. list_assign( opts, keepVariants = as.character(keepVariants), - screen = .screenResolve(signalScreenArgs) + screen = .screenResolve(signalScreenParam) ) } @@ -5099,38 +5230,38 @@ effectiveN <- function(nCase, nControl) { # retained panel no longer covers, and kriging, SLALOM/DENTIST and the RAISS # known/unknown split then only ever see what survived. # @noRd -.ssqcPrunePanel <- function(ldSketch, cutoffs, label) { - if (is.null(ldSketch) || is.null(cutoffs)) { - return(ldSketch) +.ssqcPrunePanel <- function(ldReference, cutoffs, label) { + if (is.null(ldReference) || is.null(cutoffs)) { + return(ldReference) } - ids <- .ldSketchMatchIds(ldSketch) + ids <- .ldRefMatchIds(ldReference) if (length(ids) == 0L) { - return(ldSketch) + return(ldReference) } - keep <- .panelKeepMask(ids, ldSketch, cutoffs, label) + keep <- .panelKeepMask(ids, ldReference, cutoffs, label) if (all(keep)) { - return(ldSketch) + return(ldReference) + } + if (.ldIsMatrixRef(ldReference) || .ldIsMixtureRef(ldReference)) { + return(.ldRefSubset(ldReference, keep)) } - # Prune the underlying GenotypeHandle, not just an RSE row view: the - # handle is the dosage DelayedArray's seed, and `sketch[keep, ]` leaves - # that seed carrying the whole panel (the trap `.emptySketch()` - # documents), so every read would still see the dropped variants. This is - # a seed-level edit, which is why it reaches for the handle. - pruned <- .subsetGenotypeHandle(.ldSketchHandle(ldSketch), keep) - if (methods::is(ldSketch, "GenotypeHandle")) { - return(pruned) + if (methods::is(ldReference, "GenotypeHandle")) { + return(.subsetGenotypeHandle(ldReference, keep)) } - .genotypeExperiment(pruned) + # A panel is cut where it can shed the dropped variants from its seed as + # well as from its rows (.panelSubsetRows()). The mask is over the + # PANEL's rows, so it is never applied to a handle whose rows differ. + .panelSubsetRows(ldReference, keep) } # One entry's QC result: list(gr, audit). `opts` arrives as the shared base # options; the per-entry fields are derived here rather than carried over from # the previous entry. # @noRd -.ssqcRunEntry <- function(i, sumstats, opts, ldSketch, isQtl) { +.ssqcRunEntry <- function(i, sumstats, opts, ldReference, isQtl) { .runEntrySummaryStatsQc( gr = .collectionEntry(sumstats, i), - ldSketch = ldSketch, + ldReference = ldReference, opts = .ssqcEntryOpts(opts, sumstats, i), entryLabel = .ssqcEntryLabel(sumstats, i, isQtl) ) @@ -5142,8 +5273,8 @@ effectiveN <- function(nCase, nControl) { # Panel filter, once for the shared LD reference and BEFORE any entry is # harmonized, so a variant the panel cannot support is gone from the LD # and from the summary statistics alike. - ldSketch <- .ssqcPrunePanel( - ldSketch(sumstats), + ldReference <- .ssqcPrunePanel( + ldReference(sumstats), .panelCutoffs(opts$panelFilterParam), "summaryStatsQc" ) @@ -5153,13 +5284,13 @@ effectiveN <- function(nCase, nControl) { .ssqcRunEntry, sumstats = sumstats, opts = opts, - ldSketch = ldSketch, + ldReference = ldReference, isQtl = isQtl ) list( newEntries = map(results, "gr"), entryAudits = map(results, "audit"), - ldSketch = ldSketch + ldReference = ldReference ) } @@ -5168,21 +5299,21 @@ effectiveN <- function(nCase, nControl) { # like qcDiagnostics() index these four names, so the output shape is kept # even though the input is now one argument. # @noRd -.ssqcScreenEcho <- function(signalScreenArgs, metric) { - signalScreenArgs[[metric]] %||% 0 +.ssqcScreenEcho <- function(signalScreenParam, metric) { + signalScreenParam[[metric]] %||% 0 } # Assemble the qcInfo record (echoed options + per-entry audits). .ssqcBuildQcInfo <- function( entryAudits, - sumstatsFilterArgs, + sumstatsFilterParam, panelFilterParam, - signalScreenArgs, + signalScreenParam, ldMismatchQcMethod, alleleFlipKriging, effectiveN, impute, - sumstatsCleaningArgs + sumstatsCleaningParam ) { list( timestamp = NA_character_, @@ -5191,50 +5322,58 @@ effectiveN <- function(nCase, nControl) { # these names. The bundles are an argument convention, not an # output one. options = list( - removeIndels = sumstatsFilterArgs$removeIndels, - removeStrandAmbiguous = sumstatsFilterArgs$removeStrandAmbiguous, + removeIndels = sumstatsFilterParam$removeIndels, + removeStrandAmbiguous = sumstatsFilterParam$removeStrandAmbiguous, mafCutoff = panelFilterParam$mafCutoff, macCutoff = panelFilterParam$macCutoff, imissCutoff = panelFilterParam$imissCutoff, - infoCutoff = sumstatsFilterArgs$infoCutoff, - nCutoff = sumstatsFilterArgs$nCutoff, - pipCutoffToSkip = .ssqcScreenEcho(signalScreenArgs, "pip"), - absZCutoffToSkip = .ssqcScreenEcho(signalScreenArgs, "absZ"), - bfCutoffToSkip = .ssqcScreenEcho(signalScreenArgs, "bf"), - logBfCutoffToSkip = .ssqcScreenEcho(signalScreenArgs, "logBf"), + infoCutoff = sumstatsFilterParam$infoCutoff, + nCutoff = sumstatsFilterParam$nCutoff, + pipCutoffToSkip = .ssqcScreenEcho(signalScreenParam, "pip"), + absZCutoffToSkip = .ssqcScreenEcho(signalScreenParam, "absZ"), + bfCutoffToSkip = .ssqcScreenEcho(signalScreenParam, "bf"), + logBfCutoffToSkip = .ssqcScreenEcho(signalScreenParam, "logBf"), ldMismatchQcMethod = ldMismatchQcMethod, alleleFlipKriging = alleleFlipKriging, effectiveN = effectiveN, impute = impute, # Echoed FLAT for the same reason the screen cutoffs are: these # nine names are part of the returned object's shape. - coerceNumeric = sumstatsCleaningArgs$coerceNumeric, - normalizeChr = sumstatsCleaningArgs$normalizeChr, - dropNonstandardChr = sumstatsCleaningArgs$dropNonstandardChr, - dropMissData = sumstatsCleaningArgs$dropMissData, - dropPOutOfRange = sumstatsCleaningArgs$dropPOutOfRange, - clampSmallP = sumstatsCleaningArgs$clampSmallP, - smallPFloor = sumstatsCleaningArgs$smallPFloor, - dropZeroEffect = sumstatsCleaningArgs$dropZeroEffect, - dropNonpositiveSe = sumstatsCleaningArgs$dropNonpositiveSe + coerceNumeric = sumstatsCleaningParam$coerceNumeric, + normalizeChr = sumstatsCleaningParam$normalizeChr, + dropNonstandardChr = sumstatsCleaningParam$dropNonstandardChr, + dropMissData = sumstatsCleaningParam$dropMissData, + dropPOutOfRange = sumstatsCleaningParam$dropPOutOfRange, + clampSmallP = sumstatsCleaningParam$clampSmallP, + smallPFloor = sumstatsCleaningParam$smallPFloor, + dropZeroEffect = sumstatsCleaningParam$dropZeroEffect, + dropNonpositiveSe = sumstatsCleaningParam$dropNonpositiveSe ), entryAudit = entryAudits ) } # Rebuild the SumStats object with QC'd entries, trimmed sketch, and qcInfo. -.ssqcRebuild <- function(sumstats, newEntries, newLdSketch, qcInfo) { - has <- function(nm) is_in(nm, .tupleColumnNames(sumstats)) - nSample <- if (has("nSample")) as.numeric(sumstats$nSample) else NULL +.ssqcRebuild <- function(sumstats, newEntries, newLdReference, qcInfo) { + cols <- .tupleColumnNames(sumstats) + nSample <- if (is_in("nSample", cols)) { + as.numeric(sumstats$nSample) + } else { + NULL + } if (methods::is(sumstats, "GwasSumStats")) { GwasSumStats( studyName = as.character(sumstats$study), entry = newEntries, genome = unique(unname(GenomeInfoDb::genome(sumstats))), - ldSketch = newLdSketch, + ldReference = newLdReference, varY = as.numeric(sumstats$varY), - nCase = if (has("nCase")) as.numeric(sumstats$nCase) else NULL, - nControl = if (has("nControl")) { + nCase = if (is_in("nCase", cols)) { + as.numeric(sumstats$nCase) + } else { + NULL + }, + nControl = if (is_in("nControl", cols)) { as.numeric(sumstats$nControl) } else { NULL @@ -5244,7 +5383,7 @@ effectiveN <- function(nCase, nControl) { # Carry the existing block keys through: the entries are already # split, so without this the constructor re-derives them from # seqname and every block on one chromosome collapses to one key. - blockId = if (has("blockId")) { + blockId = if (is_in("blockId", cols)) { as.character(sumstats$blockId) } else { NULL @@ -5257,7 +5396,7 @@ effectiveN <- function(nCase, nControl) { trait = as.character(sumstats$trait), entry = newEntries, genome = unique(unname(GenomeInfoDb::genome(sumstats))), - ldSketch = newLdSketch, + ldReference = newLdReference, varY = as.numeric(sumstats$varY), nSample = nSample, qcInfo = qcInfo @@ -5273,10 +5412,11 @@ effectiveN <- function(nCase, nControl) { #' tiny P; normalize CHR; coerce signed columns to numeric), variant-content #' filters (MAF / INFO / N) via \code{.applyContentFilters}, optional #' \code{skipRegion} drop, optional PIP screen, panel-vs-sumstats allele -#' harmonization against the \code{ldSketch} via \code{harmonizeAlleles} (which -#' handles indels, strand-ambiguous variants, sign / strand flips, and duplicate -#' drops in a single sweep), optional SLALOM/DENTIST LD-mismatch QC, and -#' optional RAISS imputation. No Bioconductor genome / dbSNP packages required. +#' harmonization against the \code{ldReference} via \code{harmonizeAlleles} +#' (which handles indels, strand-ambiguous variants, sign / strand flips, and +#' duplicate drops in a single sweep), optional SLALOM/DENTIST LD-mismatch QC, +#' and optional RAISS imputation. No Bioconductor genome / dbSNP packages +#' required. #' #' The returned collection has its \code{qcInfo} slot populated with a per-entry #' audit record (variant counts, drop counts at each step, which filters fired, @@ -5310,7 +5450,7 @@ effectiveN <- function(nCase, nControl) { #' variant common in the panel but rare in the study is dropped as well. #' #' @param sumstats A \code{QtlSumStats} or \code{GwasSumStats} collection. -#' @param sumstatsFilterArgs Row filters applied to the summary statistics +#' @param sumstatsFilterParam Row filters applied to the summary statistics #' themselves, built with \code{\link{SumstatsFilterParam}}: #' \code{removeIndels} drops indels during panel harmonization, #' \code{removeStrandAmbiguous} drops A/T and C/G variants, @@ -5321,8 +5461,10 @@ effectiveN <- function(nCase, nControl) { #' @param panelFilterParam LD-reference-panel filters, built with #' \code{\link{PanelFilterParam}}. \code{macCutoff} is converted to a MAF #' equivalent using \code{macCutoff / (2 * nSamples)} and the stricter of -#' it and \code{mafCutoff} applies; \code{imissCutoff} is a per-variant -#' missingness ceiling. See the panel-filters section below. +#' it and \code{mafCutoff} applies; \code{nSamples} counts the panel's real +#' samples, which for a stochastic LD sketch are those its \code{.afreq} +#' records rather than its pseudo-samples. \code{imissCutoff} is a +#' per-variant missingness ceiling. See the panel-filters section below. #' #' \code{mafCutoff} is the one setting that reaches past the panel: it is #' measured wherever it can be, against the summary statistics' own @@ -5333,7 +5475,7 @@ effectiveN <- function(nCase, nControl) { #' retain prior to harmonization. #' @param skipRegion Optional character vector of \code{"chr:start-end"} #' strings, or a \code{GRanges}, of regions to drop. -#' @param signalScreenArgs Whether to skip an entry before fitting it, +#' @param signalScreenParam Whether to skip an entry before fitting it, #' built with #' \code{\link{SignalScreenParam}} --- the same bundle #' \code{\link{fineMappingPipeline}} and \code{\link{colocboostPipeline}} @@ -5366,14 +5508,14 @@ effectiveN <- function(nCase, nControl) { #' records the setting and each entry's \code{nSource} is one of #' \code{"effective"}, \code{"column"}, \code{"total"}, or \code{NA}. #' @param impute Logical (length 1). Run RAISS imputation against the -#' \code{ldSketch}. Default \code{FALSE}. (Note: RAISS against the sketch is -#' not yet fully wired for the new path; the option is accepted but currently -#' emits a warning and is skipped.) -#' @param imputeArgs RAISS settings, built with \code{\link{RaissParam}}. +#' \code{ldReference}'s genotype panel as the last QC step, adding z-scores +#' for panel variants the input lacks (see \code{raissParam}). The audit +#' records how many variants were imputed. Default \code{FALSE}. +#' @param raissParam RAISS settings, built with \code{\link{RaissParam}}. #' A bare list is refused, since it cannot be checked. #' #' RAISS imputation scopes its reference panel to the analysis-region window -#' (so a per-chromosome / genome-wide \code{ldSketch} does not materialize +#' (so a per-chromosome / genome-wide \code{ldReference} does not materialize #' its full dosage); \code{flank} widens that window by the given number of #' base pairs on each side to retain LD context for edge variants. #' @@ -5390,7 +5532,7 @@ effectiveN <- function(nCase, nControl) { #' the panel handed to RAISS holds nothing below those. Use them to impute #' only common variants from a panel deliberately kept wider than that --- #' e.g. \code{mafCutoff = 0.001} with -#' \code{imputeArgs = RaissParam(mafCutoff = 0.01)}. A variant present in +#' \code{raissParam = RaissParam(mafCutoff = 0.01)}. A variant present in #' the sumstats is never dropped here: RAISS derives its LD basis from those #' same panel rows. #' @@ -5398,12 +5540,12 @@ effectiveN <- function(nCase, nControl) { #' are forwarded to \code{\link{raiss}} itself. #' @param matchMinProp Minimum proportion of LD panel variants that must be #' matched by the sumstats; default 0. -#' @param sumstatsCleaningArgs What makes a row a well-formed record, built with -#' \code{\link{SumstatsCleaningParam}}: \code{coerceNumeric}, +#' @param sumstatsCleaningParam What makes a row a well-formed record, built +#' with \code{\link{SumstatsCleaningParam}}: \code{coerceNumeric}, #' \code{normalizeChr} / \code{dropNonstandardChr}, \code{dropMissData}, #' \code{dropPOutOfRange}, \code{clampSmallP} / \code{smallPFloor}, #' \code{dropZeroEffect} and \code{dropNonpositiveSe}. Applied before any -#' filter or screen. Distinct from \code{sumstatsFilter}, which applies +#' filter or screen. Distinct from \code{sumstatsFilterParam}, which applies #' quality thresholds to rows that are already well formed. #' @return A new \code{QtlSumStats} / \code{GwasSumStats} with cleaned entries #' and \code{qcInfo} populated. @@ -5413,55 +5555,44 @@ effectiveN <- function(nCase, nControl) { #' @export summaryStatsQc <- function( sumstats, - sumstatsFilterArgs = SumstatsFilterParam(), + sumstatsFilterParam = SumstatsFilterParam(), panelFilterParam = PanelFilterParam(), keepVariants = NULL, skipRegion = NULL, - signalScreenArgs = SignalScreenParam(), + signalScreenParam = SignalScreenParam(), ldMismatchQcMethod = c("none", "slalom", "dentist"), alleleFlipKriging = FALSE, effectiveN = TRUE, impute = FALSE, - imputeArgs = RaissParam(), + raissParam = RaissParam(), matchMinProp = 0, - sumstatsCleaningArgs = SumstatsCleaningParam() + sumstatsCleaningParam = SumstatsCleaningParam() ) { r <- .ssqcResolveInputs( sumstats, - sumstatsFilterArgs, + sumstatsFilterParam, panelFilterParam, - signalScreenArgs, - sumstatsCleaningArgs, - imputeArgs, + signalScreenParam, + sumstatsCleaningParam, + raissParam, ldMismatchQcMethod ) opts <- .ssqcBuildOpts( - sumstatsFilterArgs = sumstatsFilterArgs, + sumstatsFilterParam = sumstatsFilterParam, panelFilterParam = panelFilterParam, skipRegion = skipRegion, ldMismatchQcMethod = r$ldMismatchQcMethod, alleleFlipKriging = alleleFlipKriging, effectiveN = effectiveN, impute = impute, - imputeArgs = r$imputeArgs, + raissParam = r$raissParam, matchMinProp = matchMinProp, - sumstatsCleaningArgs = r$cleaning, + sumstatsCleaningParam = r$cleaning, keepVariants = keepVariants, - signalScreenArgs = signalScreenArgs + signalScreenParam = signalScreenParam ) res <- .ssqcRunEntries(sumstats, opts) - .ssqcAssemble( - sumstats, - res, - sumstatsFilterArgs = sumstatsFilterArgs, - panelFilterParam = panelFilterParam, - signalScreenArgs = signalScreenArgs, - ldMismatchQcMethod = r$ldMismatchQcMethod, - alleleFlipKriging = alleleFlipKriging, - effectiveN = effectiveN, - impute = impute, - cleaning = r$cleaning - ) + .ssqcAssemble(sumstats, res, opts, signalScreenParam) } # Validate every bundle and resolve the three that have a resolved form: @@ -5471,59 +5602,47 @@ summaryStatsQc <- function( # @noRd .ssqcResolveInputs <- function( sumstats, - sumstatsFilterArgs, + sumstatsFilterParam, panelFilterParam, - signalScreenArgs, - sumstatsCleaningArgs, - imputeArgs, + signalScreenParam, + sumstatsCleaningParam, + raissParam, ldMismatchQcMethod ) { .assertMethodParam( - sumstatsFilterArgs, - "SumstatsFilterParam", - "sumstatsFilter" + sumstatsFilterParam, + "SumstatsFilterParam" ) - .assertMethodParam(panelFilterParam, "PanelFilterParam", "panelFilter") - .assertMethodParam(signalScreenArgs, "SignalScreenParam", "signalScreen") - .assertMethodParam(imputeArgs, "RaissParam", "imputeArgs") - .ssqcCheckEntries(sumstats, sumstatsFilterArgs$infoCutoff) + .assertMethodParam(panelFilterParam, "PanelFilterParam") + .assertMethodParam(signalScreenParam, "SignalScreenParam") + .assertMethodParam(raissParam, "RaissParam") + .ssqcCheckEntries(sumstats, sumstatsFilterParam$infoCutoff) .ssqcCheckPanelCutoffs(panelFilterParam) list( ldMismatchQcMethod = .resolveLdMismatchChoice(ldMismatchQcMethod), - cleaning = .sumstatsCleaningResolve(sumstatsCleaningArgs), - imputeArgs = .ssqcResolveImputeArgs(imputeArgs) + cleaning = .sumstatsCleaningResolve(sumstatsCleaningParam), + raissParam = .ssqcResolveImputeArgs(raissParam) ) } # The QC record and the rebuilt object around the filtered entries. # @noRd -.ssqcAssemble <- function( - sumstats, - res, - sumstatsFilterArgs, - panelFilterParam, - signalScreenArgs, - ldMismatchQcMethod, - alleleFlipKriging, - effectiveN, - impute, - cleaning -) { +.ssqcAssemble <- function(sumstats, res, opts, signalScreenParam) { qcInfo <- .ssqcBuildQcInfo( res$entryAudits, - sumstatsFilterArgs = sumstatsFilterArgs, - panelFilterParam = panelFilterParam, - signalScreenArgs = signalScreenArgs, - ldMismatchQcMethod = ldMismatchQcMethod, - alleleFlipKriging = alleleFlipKriging, - effectiveN = effectiveN, - impute = impute, - sumstatsCleaningArgs = cleaning + sumstatsFilterParam = opts$sumstatsFilterParam, + panelFilterParam = opts$panelFilterParam, + signalScreenParam = signalScreenParam, + ldMismatchQcMethod = opts$ldMismatchQcMethod, + alleleFlipKriging = opts$alleleFlipKriging, + effectiveN = opts$effectiveN, + impute = opts$impute, + sumstatsCleaningParam = opts$sumstatsCleaningParam ) # From the PRUNED panel, not the input's: narrowing the original again # would hand back a sketch the panel filter never reached. - newLdSketch <- .subsetSketchToIds(res$ldSketch, res$newEntries) - .ssqcRebuild(sumstats, res$newEntries, newLdSketch, qcInfo) + newLdReference <- .subsetLdRefToIds(res$ldReference, res$newEntries) + .ssqcRebuild(sumstats, res$newEntries, newLdReference, qcInfo) } # ---- slidingWindowLoop callbacks (distance mode) ------------------------ @@ -5802,21 +5921,20 @@ SumstatsCleaningParam <- function( # .assertMethodOptions accepts `list()` as "no options", and .applySanityChecks # tests these with `if (!flag)`, so a missing field must not arrive as NULL. # @noRd -.sumstatsCleaningResolve <- function(sumstatsCleaningArgs) { +.sumstatsCleaningResolve <- function(sumstatsCleaningParam) { .assertMethodParam( - sumstatsCleaningArgs, - "SumstatsCleaningParam", - "sumstatsCleaning" + sumstatsCleaningParam, + "SumstatsCleaningParam" ) list( - coerceNumeric = sumstatsCleaningArgs$coerceNumeric %||% TRUE, - normalizeChr = sumstatsCleaningArgs$normalizeChr %||% TRUE, - dropNonstandardChr = sumstatsCleaningArgs$dropNonstandardChr %||% TRUE, - dropMissData = sumstatsCleaningArgs$dropMissData %||% TRUE, - dropPOutOfRange = sumstatsCleaningArgs$dropPOutOfRange %||% TRUE, - clampSmallP = sumstatsCleaningArgs$clampSmallP %||% TRUE, - smallPFloor = sumstatsCleaningArgs$smallPFloor %||% 5e-324, - dropZeroEffect = sumstatsCleaningArgs$dropZeroEffect %||% TRUE, - dropNonpositiveSe = sumstatsCleaningArgs$dropNonpositiveSe %||% TRUE + coerceNumeric = sumstatsCleaningParam$coerceNumeric %||% TRUE, + normalizeChr = sumstatsCleaningParam$normalizeChr %||% TRUE, + dropNonstandardChr = sumstatsCleaningParam$dropNonstandardChr %||% TRUE, + dropMissData = sumstatsCleaningParam$dropMissData %||% TRUE, + dropPOutOfRange = sumstatsCleaningParam$dropPOutOfRange %||% TRUE, + clampSmallP = sumstatsCleaningParam$clampSmallP %||% TRUE, + smallPFloor = sumstatsCleaningParam$smallPFloor %||% 5e-324, + dropZeroEffect = sumstatsCleaningParam$dropZeroEffect %||% TRUE, + dropNonpositiveSe = sumstatsCleaningParam$dropNonpositiveSe %||% TRUE ) } diff --git a/R/tupleSelectors.R b/R/tupleSelectors.R index 094ef3408..70e718088 100644 --- a/R/tupleSelectors.R +++ b/R/tupleSelectors.R @@ -1,7 +1,7 @@ # ============================================================================= # Tuple row matchers # ----------------------------------------------------------------------------- -# Internal helpers shared by the FineMappingResult / TwasWeights / SumStats +# Internal helpers shared by the fine-mapping result / TwasWeights / SumStats # collections to resolve a tuple-keyed selection to a single row index. Every # collection is a RangedTupleList, so these read identity columns off mcols # and payloads off the elements -- there is no second shape to branch on. @@ -398,14 +398,14 @@ } # Internal: append a validated `region` GRanges to a constructor's column list -# (no-op when NULL). Shared by the TwasWeights / FineMappingResult +# (no-op when NULL). Shared by the TwasWeights / FineMappingResultBase # constructors so the provenance column is added identically everywhere. # Internal: an optional per-row provenance column. One helper for all three # (`dataType`, `dataSource`, `standardized`) since they differ only in name and # coercion. # # `dataSource` is per ROW, not per collection: it used to be derived from the -# collection's single `ldSketch` slot, so after combineFineMappingResults() +# collection's single `ldReference` slot, so after combineFineMappingResults() # folded individual-level and RSS-derived fits together every row got the # combined object's answer. # @@ -426,11 +426,6 @@ c(cols, set_names(list(rep(v, length.out = n)), nm)) } -# @noRd -.appendDataTypeCol <- function(cols, dataType, n) { - .appendProvenanceCol(cols, dataType, "dataType", n) -} - # @noRd .appendFmProvenanceCols <- function( cols, @@ -458,7 +453,7 @@ } # Internal: validate the optional `region` GRanges column (one range per row), -# shared by the TwasWeights / FineMappingResult validity methods. Returns a +# shared by the TwasWeights / FineMappingResultBase validity methods. Returns a # character vector of errors (empty when valid or the column is absent). .validateRegionColumn <- function(object) { if (!is_in("region", .tupleColumnNames(object))) { @@ -554,23 +549,23 @@ } # Internal: row-bind two or more per-tuple collection objects (TwasWeights / -# FineMappingResult subclasses), carrying forward EVERY column. Columns are +# FineMappingResultBase subclasses), carrying forward EVERY column. Columns are # unioned, and a collection lacking an optional column (e.g. jointContexts or # region) is padded with `.naLikeColumn` -- so adding a column to a class flows # through combines automatically rather than being hand-listed at each site. -# Preserves the concrete class and sets the `ldSketch` slot explicitly (rbind +# Preserves the concrete class and sets the `ldReference` slot explicitly (rbind # does not reliably carry it). Returns NULL when given no inputs. Every # collection is a RangedTupleList now, so this only drops the NULL parts and # hands off; the old DFrame path went with the rebase onto GRangesList. # # `slots` carries the collection-level state a concrete subclass adds beyond -# `ldSketch` (SumStatsBase's `genome` / `qcInfo`). It is passed in rather than -# read off `parts[[1L]]` because those slots describe the WHOLE collection, so -# merging them is the caller's decision -- first-wins would silently drop the +# `ldReference` (SumStatsBase's `genome` / `qcInfo`). It is passed in rather +# than read off `parts[[1L]]` because those slots describe the WHOLE collection, +# so merging them is the caller's decision -- first-wins would silently drop the # other parts' QC audit. .rbindCollections <- function( parts, - ldSketch = NULL, + ldReference = NULL, slots = list(), genome = NULL ) { @@ -578,7 +573,7 @@ if (length(parts) == 0L) { return(NULL) } - .rbindRangedCollections(parts, ldSketch, slots, genome) + .rbindRangedCollections(parts, ldReference, slots, genome) } # Internal: aggregate a per-entry accessor across every row of a @@ -706,7 +701,7 @@ .fmrEntryPart <- function(i, x, perEntry, meta, viewArgs) { # `perEntry` is an internal per-row function taking the stored payload, not # an S4 generic dispatching on a rebuilt entry. That indirection is what - # the FineMappingRow class existed for. + # the retired FineMappingEntry class existed for. raw <- exec(perEntry, .fmrRowParts(x, i), !!!viewArgs) if (is.null(raw) || nrow(raw) == 0L) { return(NULL) @@ -737,8 +732,6 @@ } } -# Concatenate column `cn` across all parts (NA-filling parts that lack it). -# @noRd # Row-binding a collection: the elements append and the mcols rbind. # # S4Vectors::combineRows() does the union-with-NA-fill this needs, but it @@ -749,7 +742,7 @@ # @noRd .rbindRangedCollections <- function( parts, - ldSketch, + ldReference, slots = list(), genome = NULL ) { @@ -775,12 +768,14 @@ methods::new, cls, grl, - !!!c(list(ldSketch = ldSketch), slots) + !!!c(list(ldReference = ldReference), slots) ) validObject(out) out } +# Concatenate column `cn` across all parts (NA-filling parts that lack it). +# @noRd .rbindColumn <- function(cn, parts) { pieces <- map(parts, .rbindColPiece, cn = cn, parts = parts) # GRanges concat may warn on seqinfo. @@ -790,7 +785,7 @@ # ============================================================================= # Per-row primitives # ----------------------------------------------------------------------------- -# The per-row views a collection projects. These replace the FineMappingRow +# The per-row views a collection projects. These replace the FineMappingEntry # S4 class, which existed only to be the DISPATCH TARGET for exactly this work: # a collection method would rebuild an entry for each row and call the exported # generic on it. That indirection cost a whole class, a validity method and a @@ -819,11 +814,6 @@ ) } -# The same, for a set of rows collapsed into one logical entry (a multi-block -# row group). The fit payload comes from the first row, matching the previous -# .fmeEntryFromRows behaviour. -# @noRd - # @noRd .fmrPartsVariantIds <- function(parts) { variantIds(.asFmRowPayload(parts)) @@ -845,7 +835,7 @@ } # ---- per-row views ---------------------------------------------------------- -# Each of these is the body of what used to be a FineMappingRow method, +# Each of these is the body of what used to be a FineMappingEntry method, # reading the stored form through `parts` instead of through S4 slots. They are # what .fmrAggregateView() maps over. @@ -1024,7 +1014,7 @@ list(variantIds = vids[ok], weights = w[ok]) } -# Class-aware row payload: the TwasWeights and FineMappingResult shapes differ +# Class-aware row payload: the TwasWeights and fine-mapping shapes differ # (weights vs a susie fit), so callers that accept either weight source route # through here. Replaces the old .collectionEntry bridge. # @noRd @@ -1051,7 +1041,7 @@ # A fine-mapping row deliberately reports NULL: cTWAS would otherwise # renormalize alpha into an UNstandardized weight, which is inconsistent with # the standardized posterior effect the topLoci view already carries. Preserved -# verbatim from the FineMappingRow methodFits method. +# verbatim from the retired FineMappingEntry methodFits method. # @noRd .rowFits <- function(parts) { if (methods::is(parts, "TwasWeightsRow")) methodFits(parts) else NULL @@ -1070,15 +1060,12 @@ TRUE } -# ---- row-payload builders --------------------------------------------------- -# What callers use in place of the retired FineMappingRow / TwasWeightsRow -# constructors. Same arguments, but they return the STORED form directly -- a -# plain list holding one GRanges plus the fit payload -- so nothing has to be -# converted on the way into a collection. +# ---- row-payload normalizers ------------------------------------------------ +# The per-row views read a FineMappingRow / TwasWeightsRow. Callers may also +# hold a single-row collection -- what fineMappingResult() returns -- so these +# accept either and hand back the row. -# Accept either a row payload (the stored form) or a legacy entry object, so -# the constructors can be flipped independently of their ~300 call sites. -# Delete the entry branch once nothing constructs entries. +# A FineMappingRow from a row or from a fine-mapping collection's rows. # @noRd .asFmRowPayload <- function(e) { # A single-row collection is the other shape callers hand over -- it is @@ -1120,8 +1107,6 @@ abort(msg) } -# @noRd - # Shape-tolerant readers for values that may still be a legacy entry object # while the flip is in progress. Delete the entry branch (and these) once # nothing produces entries. @@ -1155,7 +1140,7 @@ # ============================================================================= # Combining summary-statistics collections # ----------------------------------------------------------------------------- -# A SumStatsBase subclass carries three COLLECTION-level slots -- ldSketch, +# A SumStatsBase subclass carries three COLLECTION-level slots -- ldReference, # genome and qcInfo -- on top of the elements and their mcols, so row-binding # per-block pieces back into one collection has to say what happens to each. # The rules live here once, shared by combineGwasSumStats() and @@ -1165,9 +1150,9 @@ # qcInfo `entryAudit` concatenates in element order (it is indexed BY # element); the rest is carried from the first part after checking # the QC options agree -# ldSketch the panels union, read through the parts' shared GenotypeHandle +# ldReference the panels union, read through the parts' shared GenotypeHandle # -# The ldSketch rule is the one that matters for correctness. A block-parallel +# The ldReference rule is the one that matters for correctness. A block-parallel # pipeline narrows each piece's panel to that block, so first-wins would hand # back a collection spanning every block whose LD reference covers only the # first -- and cTWAS computes its full-panel LD from exactly that slot. @@ -1192,14 +1177,14 @@ invisible(NULL) } -# The collection-level slots BEYOND ldSketch that a combine has to merge, for +# The collection-level slots BEYOND ldReference that a combine has to merge, for # whatever concrete class `parts` are. A slot describes the whole collection, # so first-wins would silently drop the other parts' state; each gets its own # rule and an unrecognised slot is an error rather than a quiet default, so # adding one to a class cannot slip through a combine unnoticed. # @noRd .rtlExtraSlots <- function(parts, fn) { - own <- setdiff(.rtlOwnSlots(parts[[1L]]), "ldSketch") + own <- setdiff(.rtlOwnSlots(parts[[1L]]), "ldReference") out <- compact(list( qcInfo = if (is_in("qcInfo", own)) .ssCombineQcInfo(parts, fn) )) @@ -1217,9 +1202,9 @@ # Merge `parts` into one collection, reconciling every collection-level slot. # The single path behind `c()` / `append()` and the four combine*() wrappers. -# A non-NULL `ldSketch` overrides the unioned panel. +# A non-NULL `ldReference` overrides the unioned panel. # @noRd -.combineTupleCollections <- function(parts, ldSketch, fn) { +.combineTupleCollections <- function(parts, ldReference, fn) { .rtlRequireSameClass(parts, fn) # Forced before the rebuild: GRangesList() merges seqinfo and rejects # mismatched builds itself, with a message about sequence-level genomes @@ -1227,7 +1212,7 @@ genome <- .rtlCombineGenome(parts, fn) .rbindCollections( parts, - ldSketch = ldSketch %||% .combineLdSketch(parts, fn), + ldReference = ldReference %||% .combineLdRef(parts, fn), slots = .rtlExtraSlots(parts, fn), genome = genome ) @@ -1244,10 +1229,10 @@ } # Row-bind summary-statistics parts, merging the three collection-level slots. -# `ldSketch` (when non-NULL) overrides the unioned panel. +# `ldReference` (when non-NULL) overrides the unioned panel. # @noRd -.rbindSumStats <- function(parts, ldSketch, fn) { - .combineTupleCollections(parts, ldSketch, fn) +.rbindSumStats <- function(parts, ldReference, fn) { + .combineTupleCollections(parts, ldReference, fn) } # One genome build per collection (every entry shares the LD sketch), so the @@ -1340,8 +1325,8 @@ # collection has no panel); a mix is an error, because silently keeping the # panels that exist would leave elements harmonized against nothing. # @noRd -.combineLdSketch <- function(parts, fn) { - sketches <- map(parts, ldSketch) +.combineLdRef <- function(parts, fn) { + sketches <- map(parts, ldReference) present <- !map_lgl(sketches, is.null) if (!any(present)) { return(NULL) @@ -1349,40 +1334,161 @@ if (!all(present)) { msg <- glue( "{fn}: {sum(!present)} of {length(parts)} inputs carry no ", - "ldSketch. Combine collections that all have an LD reference, or ", - "all have none." + "ldReference. Combine collections that all have an LD reference, ", + "or all have none." ) abort(msg) } - handles <- map(sketches, .ldSketchHandle) - .ssCheckSameSource(handles, fn) + if (any(map_lgl(sketches, .ldIsMatrixRef))) { + return(.combineLdMatrixRefs(sketches, fn)) + } + if (any(map_lgl(sketches, .ldIsMixtureRef))) { + return(.combineMixtureRefs(sketches, fn)) + } + .ssCheckSameSource(sketches, fn) + chromPaths <- .ssUnionChromPaths(sketches, fn) + handles <- map(sketches, .ldRefHandle) + if (any(map_lgl(handles, is.null))) { + return(.ssUnionPanels(sketches, chromPaths)) + } handle <- methods::initialize( handles[[1L]], snpInfo = .ssUnionSnpInfo(handles), - chromPaths = .ssUnionChromPaths(handles, fn) + chromPaths = chromPaths ) - .asLdSketch(handle) + .asLdRef(handle) } -# Union the per-chromosome shard paths across handles. Two sketches trimmed -# from one genoMeta panel to different chromosomes share path/format/samples -# and differ ONLY here, so unioning is what lets their snpInfo rows keep -# routing: a row reaches its file by its own CHR. One chromosome mapping to -# two different files means the parts are not the same panel after all. +# The union of panels from one source that read through no handle -- a VCF, +# GDS or PLINK1 panel, or a per-chromosome set of them: their rows, each +# once, in genomic order, over the first panel's samples. The parts' assays +# are bound, not read. Parts that are all the same panel union to it. # @noRd -.ssUnionChromPaths <- function(handles, fn) { - combined <- .ssConcatChr(map(handles, .ssHandleChromPaths)) +.ssUnionPanels <- function(panels, chromPaths) { + first <- panels[[1L]] + if (all(map_lgl(map(panels, rownames), identical, rownames(first)))) { + return(first) + } + rr <- suppressWarnings( + exec(c, !!!unname(map(panels, SummarizedExperiment::rowRanges))) + ) + stacked <- SummarizedExperiment::SummarizedExperiment( + assays = list( + dosage = exec( + BiocGenerics::rbind, + !!!unname(map(panels, .seDosage)) + ) + ), + rowRanges = rr, + colData = SummarizedExperiment::colData(first), + metadata = S4Vectors::metadata(first) + ) + kept <- which(!duplicated(names(rr))) + ord <- kept[order( + canonChrom(as.character(seqnames(rr)[kept])), + start(rr)[kept] + )] + md <- S4Vectors::metadata(stacked) + if (!is.null(md$genotypeSource)) { + md$genotypeSource$chromPaths <- chromPaths + } + S4Vectors::`metadata<-`(stacked[ord, ], value = md) +} + +# LdMatrix references, unlike panels, cannot be unioned: the LD between +# variants held by different parts is recorded in neither. They combine only +# when one of them already holds every other's variants with the same LD -- +# the same matrix, or one part that kept all of the others' variants. +# Otherwise the caller passes the matrix covering them all as `ldReference`. +# @noRd +.combineLdMatrixRefs <- function(refs, fn) { + if (!all(map_lgl(refs, .ldIsMatrixRef))) { + msg <- glue( + "{fn}: inputs mix genotype-panel and LdMatrix references; ", + "combine collections that share one LD reference." + ) + abort(msg) + } + widest <- refs[[which.max(map_int(refs, length))]] + if (all(map_lgl(refs, .ldmCoveredBy, by = widest))) { + return(widest) + } + abort(c( + glue( + "{fn}: the inputs' LdMatrix references cover different variants, ", + "and the LD between them is held by none." + ), + i = "Pass an LdMatrix covering all of their variants as `ldReference`." + )) +} + +# Mixture references combine like LdMatrix ones: when one of them already +# holds every other's variants and they average the same panels with the +# same weights. Their panels stay lazy, so the widest mixture loses nothing. +# @noRd +.combineMixtureRefs <- function(refs, fn) { + if (!all(map_lgl(refs, .ldIsMixtureRef))) { + msg <- glue( + "{fn}: inputs mix GenotypeMixture references with other kinds; ", + "combine collections that share one LD reference." + ) + abort(msg) + } + widest <- refs[[which.max(map_int(refs, .ldRefNVariants))]] + sameMix <- map_lgl(refs, .gmSameMixture, y = widest) + covered <- map_lgl(refs, .ldRefVariantsCoveredBy, by = widest) + if (all(sameMix & covered)) { + return(widest) + } + abort(c( + glue( + "{fn}: the inputs' GenotypeMixture references differ or cover ", + "different variants." + ), + i = "Pass the mixture covering all of their variants as `ldReference`." + )) +} + +# TRUE when every variant of `x` is in `by`, coded the same way. +# @noRd +.ldRefVariantsCoveredBy <- function(x, by) { + m <- matchVariants( + .ldRefMatchIds(x), + .ldRefMatchIds(by), + removeStrandAmbiguous = FALSE + ) + length(m$idxA) == .ldRefNVariants(x) && all(m$sign > 0) +} + +# TRUE when every variant of `x` is in `by`, coded the same way, with the +# same LD. +# @noRd +.ldmCoveredBy <- function(x, by) { + m <- matchVariants( + .ldRefMatchIds(x), + .ldRefMatchIds(by), + removeStrandAmbiguous = FALSE + ) + length(m$idxA) == length(x) && + all(m$sign > 0) && + .ldmAgreesOn(x, by, m$idxA, m$idxB) +} + +# Union the per-chromosome shard paths across LD references (panels, or +# bare handles). Two sketches trimmed from one genoMeta panel to different +# chromosomes share path/format/samples and differ ONLY here, so unioning is +# what lets their rows keep routing: a row reaches its file by its own CHR. +# One chromosome mapping to two different files means the parts are not the +# same panel after all. +# @noRd +.ssUnionChromPaths <- function(refs, fn) { + combined <- .ssConcatChr(map(refs, .ldRefChromPaths)) byChrom <- split(unname(combined), names(combined)) walk2(names(byChrom), byChrom, .ssCheckOneFilePerChrom, fn = fn) # Every chromosome agrees, so the first mapping stands for all of them. combined[!duplicated(names(combined))] } -# @noRd -.ssHandleChromPaths <- function(h) { - h@chromPaths -} - # @noRd .ssConcatChr <- function(pieces) { if (length(pieces) == 0L) { @@ -1400,41 +1506,48 @@ } msg <- glue( "{fn}: chromosome '{ch}' maps to two different genotype ", - "files across the inputs, so their LD sketches cannot ", + "files across the inputs, so their LD references cannot ", "be unioned." ) abort(msg) } -# Every panel must read from the same file(s): the union keeps the first -# handle and widens its snpInfo, and each row's `fileIdx` addresses a position -# in THAT file, so rows from another panel would read the wrong variants. +# Every panel must read from the same file(s) and samples: a handle union +# keeps the first handle and widens its snpInfo, each row's `fileIdx` +# addressing a position in THAT file, and a panel union binds the parts over +# the first one's samples. # @noRd -.ssCheckSameSource <- function(handles, fn) { - same <- map_lgl(handles, .ssSameGenotypeSource, first = handles[[1L]]) +.ssCheckSameSource <- function(sketches, fn) { + same <- map_lgl(sketches, .ssSameGenotypeSource, first = sketches[[1L]]) if (all(same)) { return(invisible(NULL)) } msg <- glue( "{fn}: inputs reference different genotype panels (input ", - "{which(!same)[[1L]]} differs from the first). Their LD sketches ", + "{which(!same)[[1L]]} differs from the first). Their LD references ", "cannot be unioned." ) abort(msg) } # @noRd -.ssSameGenotypeSource <- function(h, first) { +.ssSameGenotypeSource <- function(x, first) { # chromPaths is deliberately NOT compared: two sketches trimmed from the # same genoMeta panel to different chromosomes differ there and nowhere # else, and .ssUnionChromPaths() merges them (erroring on a genuine # conflict). Everything below must match for the union to mean anything -- - # snpInfo rows carry file POSITIONS, which only make sense against the - # same files and the same sample axis. - identical(h@path, first@path) && - identical(h@format, first@format) && - identical(h@nSamples, first@nSamples) && - identical(h@sampleIds, first@sampleIds) + # rows address the same files, over the same sample axis. + a <- .ldRefSource(x) + b <- .ldRefSource(first) + identical(a$path, b$path) && + identical(a$format, b$format) && + identical(.ssSampleIds(x), .ssSampleIds(first)) +} + +# An LD reference's samples: a panel's columns, a bare handle's sample ids. +# @noRd +.ssSampleIds <- function(x) { + if (methods::is(x, "GenotypeHandle")) sampleIds(x) else colnames(x) } # The parts' snpInfo rows, de-duplicated by variant id and returned in genomic diff --git a/R/twasWeights.R b/R/twasWeights.R index 0fcc17316..6d05440bb 100644 --- a/R/twasWeights.R +++ b/R/twasWeights.R @@ -26,7 +26,7 @@ # DFrame-subclass collection keyed by the identity tuple (study, context, # trait, method). Each row holds a TwasWeightsRow payload (variant ids # + per-variant weight vector/matrix). Class-level slots: -# * ldSketch GenotypeHandle (NULL for individual-level fits, the +# * ldReference GenotypeHandle (NULL for individual-level fits, the # LD-sketch handle for RSS-derived weights). # Constructor + accessors below. The twasWeights pipeline helpers # (learnTwasWeights, CV, ensemble, etc.) follow at the bottom. @@ -52,21 +52,22 @@ NULL #' column lists the semicolon-joined members of the joined axis. For non-joint #' rows the joint columns are \code{NA_character_}. Tuple uniqueness is enforced #' jointly across the identity-tuple columns and any present joint columns. -#' @slot ldSketch The LD reference genotype panel the weights were -#' derived against, or \code{NULL} when the weights were learned from -#' individual-level data. Used downstream for cross-pipeline LD-sketch -#' identity validation. +#' @slot ldReference The LD reference the weights were derived against (a +#' genotype panel, a \code{GenotypeMixture} or an \code{LdMatrix}), or +#' \code{NULL} when the weights were learned from individual-level data. +#' Used downstream to check that pipelines combining results share one LD +#' reference. #' @export setClass( "TwasWeights", contains = "RangedTupleList", - representation(ldSketch = "LdSketch_OR_NULL"), + representation(ldReference = "LdReference_OR_NULL"), validity = function(object) .validateTwasWeights(object) ) # Validity for the TwasWeights collection: required columns, entry payloads, # region/traitPos provenance, joint* column types, tuple uniqueness, and the -# optional ldSketch. Returns TRUE or a character vector of error messages. +# optional ldReference. Returns TRUE or a character vector of error messages. # @noRd #' @importFrom checkmate makeAssertCollection assertNames .validateTwasWeights <- function(object) { @@ -170,7 +171,7 @@ setClass( #' shape as \code{jointStudies}. #' @param jointTraits Optional character vector for cross-trait joints. Same #' shape as \code{jointStudies}. -#' @param ldSketch An optional genotype panel (see +#' @param ldReference An optional genotype panel (see #' \code{\link{readGenotypes}}), or \code{NULL} for individual-level fits. #' @param traitPos Optional per-row trait genomic anchor (a \code{GRanges} or #' \code{NULL}), carried forward as provenance; not part of the identity key. @@ -202,7 +203,7 @@ TwasWeights <- function( jointTraits = NULL, traitPos = NULL, region = NULL, - ldSketch = NULL + ldReference = NULL ) { .twasAssertArgs( studyName, @@ -230,13 +231,7 @@ TwasWeights <- function( region, n ) - obj <- new( - "TwasWeights", - .rtlGrlWithMetadata(entry, cols), - ldSketch = .asLdSketch(ldSketch) - ) - validObject(obj) - obj + .rtlNewValidated("TwasWeights", entry, cols, ldReference) } # The identity and joint-axis arguments, checked. Mirrors @@ -302,24 +297,11 @@ TwasWeights <- function( .appendRegionCol(.appendTraitPosCol(withJoint, traitPos, n), region, n) } -# One entry -> its element: the variants as a GRanges, with the per-variant -# weights carried in the element's own mcols alongside the alleles. -# @noRd - -# The non-variant half of each entry, as outer mcols columns. Objects of -# arbitrary shape (fits, cvResult, dataType) ride in SimpleLists. -# @noRd - # @noRd .twPayloadStandardized <- function(p) { weightStandardized(p) } -# Every `entry` must be a TwasWeightsRow. Checked before the payload is -# unpacked, so a wrong type reports itself rather than surfacing as a missing -# method on whatever was passed instead. -# @noRd - # Require study/context/trait/method/entry to share one length; returns it. # @noRd .twasCheckRowLengths <- function(studyName, context, trait, method, entry) { @@ -432,15 +414,6 @@ setMethod( mcols(x[[i]])$weight } -# Rebuild the TwasWeightsRow for a tuple from its stored element(s). -# -# The entry is a DERIVED VIEW, not stored state: the variants live in the -# element, the weights in that element's mcols, and the rest of the payload in -# the collection's mcols. A tuple split across chromosomes owns several -# elements, so they are stitched back into the single entry callers expect -- -# the same contract sumStats() keeps. -# @noRd - # The row payload a (study, context, trait, method) selector pins. # # Read directly rather than via twasWeights(): that accessor returns a @@ -609,9 +582,9 @@ setMethod( #' @export setMethod("studyName", "TwasWeights", function(x) unique(as.character(x$study))) -#' @rdname ldSketch +#' @rdname ldReference #' @export -setMethod("ldSketch", "TwasWeights", function(x) x@ldSketch) +setMethod("ldReference", "TwasWeights", function(x) x@ldReference) #' @rdname contexts #' @export @@ -645,12 +618,12 @@ setMethod("show", "TwasWeights", function(object) { .trim = FALSE )) } - ldSrc <- if (is.null(object@ldSketch)) { + ldSrc <- if (is.null(object@ldReference)) { "NULL (individual-level fit)" } else { - .ldSketchLabel(object@ldSketch) + .ldRefLabel(object@ldReference) } - cat(glue(" LD sketch: {ldSrc}\n", .trim = FALSE)) + cat(glue(" LD reference: {ldSrc}\n", .trim = FALSE)) }) @@ -715,7 +688,7 @@ setMethod("show", "TwasWeights", function(object) { mrash = list( fn = "mrash_weights", impl = "mrashWeights", - args = list(initPriorSd = TRUE, max.iter = 100) + args = list(max.iter = 100) ), enet = list(fn = "enet_weights", impl = "enetWeights", args = list()), lasso = list( @@ -857,7 +830,7 @@ setMethod("show", "TwasWeights", function(object) { # fit enters -- not by reading the sources, which hides forwarding behind # list_modify()/exec() and imported-without-:: calls. test_twasWeights.R # re-traces them and fails if one drifts. -# @noRd + # Which wrapper and engine each TWAS weight method actually reaches. Split # by data path because the two share almost nothing: a method's individual # and summary-statistic routes usually run different engines entirely, and @@ -909,7 +882,12 @@ setMethod("show", "TwasWeights", function(object) { } # Summary statistics: each chain ends in pecotmr's own RSS solver or an -# upstream *.rss entry point, never the individual-level engine. +# upstream *.rss entry point, never the individual-level engine. pecotmr's +# solvers are reached through their LD-source paths (.penalizedRssPath(), +# .lassosumRssPath(), .prsCsRun(), .sdprRun()), which take the options of +# penalizedRss(), lassosumRss(), prsCs() and sdpr() under the same names, so +# a genotype reference can be used through its factor without forming the LD +# matrix. # @noRd .twasMethodChainsSumstat <- function() { list( @@ -917,26 +895,26 @@ setMethod("show", "TwasWeights", function(object) { "scadRssWeights", ".penalizedRssWeights", ".rssShrinkGridWeights", - "penalizedRss" + ".penalizedRssPath" ), mcp = c( "mcpRssWeights", ".penalizedRssWeights", ".rssShrinkGridWeights", - "penalizedRss" + ".penalizedRssPath" ), l0learn = c( "l0learnRssWeights", ".rssShrinkGridWeights", - "penalizedRss" + ".penalizedRssPath" ), lasso = c( "lassosumRssWeights", ".rssShrinkGridWeights", - "lassosumRss" + ".lassosumRssPath" ), - prsCs = c("prsCsWeights", "prsCs"), - dprGibbs = c("sdprWeights", "sdpr"), + prsCs = c("prsCsWeights", ".prsCsRun"), + dprGibbs = c("sdprWeights", ".sdprRun"), mrash = c("mrashRssWeights", "susieR::mr.ash.rss"), mrmash = c( "mrmashRssWeights", @@ -964,7 +942,9 @@ setMethod("show", "TwasWeights", function(object) { "Y", "y", "stat", + "ldReference", "LD", + "ld", "bhat", "shat", "R", @@ -994,7 +974,7 @@ setMethod("show", "TwasWeights", function(object) { fn <- if (str_detect(nm, fixed("::"))) { .engineCallee(nm) } else { - tryCatch( + try_fetch( get(nm, envir = asNamespace("pecotmr")), error = function(cnd) NULL ) @@ -1028,11 +1008,6 @@ setMethod("show", "TwasWeights", function(object) { } -# The argument constructor for each weight-method token. Several tokens share -# an engine, so this maps token -> constructor rather than token -> package. -# Tokens absent from this list take no options at all; .twasTokenNoArgsReason -# says why, so the error can be specific. -# @noRd # The implementation that runs one method on one input path. # @noRd .twasMethodImplFor <- function(token, inputKind) { @@ -1060,7 +1035,7 @@ setMethod("show", "TwasWeights", function(object) { if (is.null(impl)) { return(NULL) } - fn <- tryCatch( + fn <- try_fetch( get(impl, envir = asNamespace("pecotmr")), error = function(cnd) NULL ) @@ -1083,7 +1058,7 @@ setMethod("show", "TwasWeights", function(object) { if (is.null(nm)) { return(NULL) } - tryCatch( + try_fetch( get(nm, envir = asNamespace("pecotmr")), error = function(cnd) NULL ) @@ -1105,7 +1080,8 @@ setMethod("show", "TwasWeights", function(object) { .twasMethodsFor <- function(inputKind) { conf <- keep( .twasConfigurableMethods(), - function(tk) !is.null(.twasMethodImplFor(tk, inputKind)) + .twasMethodRunsOn, + inputKind = inputKind ) unique(c( conf, @@ -1113,6 +1089,12 @@ setMethod("show", "TwasWeights", function(object) { )) } +# Whether configurable method `token` has an implementation on `inputKind`. +# @noRd +.twasMethodRunsOn <- function(token, inputKind) { + !is.null(.twasMethodImplFor(token, inputKind)) +} + # Every configurable method's name, independent of input path. The answer to # "is this a method that takes options", which the token normalisers ask. # @noRd @@ -1120,21 +1102,6 @@ setMethod("show", "TwasWeights", function(object) { names(.twasMethodCapabilities) } -# Path-blind view, kept for the callers that have not yet been taught which -# input class they are serving: the individual-level constructor, or the -# summary-statistics one for a method that only runs there (prsCs). -# @noRd -.twasMethodCtors <- function() { - toks <- .twasConfigurableMethods() - set_names( - map(toks, function(tk) { - .twasMethodCtorFor(tk, "QtlDataset") %||% - .twasMethodCtorFor(tk, "QtlSumStats") - }), - toks - ) -} - # Why a token takes no options, so the error can say something useful rather # than just "unknown method". # @noRd @@ -1460,15 +1427,15 @@ setMethod("show", "TwasWeights", function(object) { # the fit it needs. These wrappers extract weights from an existing fit and # never fine-map, so the run is already doomed; checking here reports the # missing fit by name instead of surfacing it from inside the per-fold map. -# `available(token)` answers "is this token's fit present?" for the caller's -# own supply channel. +# `available(token, ...)` answers "is this token's fit present?" for the +# caller's own supply channel; `...` carries what that channel needs. # @noRd -.twasRequireSusieFits <- function(weightMethods, available, fnLabel, how) { +.twasRequireSusieFits <- function(weightMethods, available, fnLabel, how, ...) { tokens <- .twasSusieTokensRequested(weightMethods) if (length(tokens) == 0L) { return(invisible(NULL)) } - missing <- tokens[!map_lgl(tokens, available)] + missing <- tokens[!map_lgl(tokens, available, ...)] if (length(missing) == 0L) { return(invisible(NULL)) } @@ -1895,7 +1862,7 @@ twasWeightsCv <- function( # fit trained on a different split has seen some of this split's held-out # samples, so its out-of-fold predictions are contaminated and the CV metrics # come out optimistic -- silently. Fits carry the producer's partition -# fingerprint (see .cvPartitionKey); anything unstamped is refused too, since +# fingerprint (see .cvPartitionKey); a fit carrying none is refused too, since # it cannot be shown to match. # @noRd .twasCheckFoldFitPartition <- function(fittedModelsCv, samplePartitions) { @@ -2028,15 +1995,14 @@ twasWeightsCv <- function( .twasCheckFoldFitPartition(fittedModelsCv, samplePartitions) .twasRequireSusieFits( weightMethods, - available = function(tk) { - !is.null(.twasFoldFitsFor(fittedModelsCv, tk)) - }, + available = .twasFoldFitAvailable, fnLabel = "twasWeightsCv", how = str_c( "Cross-validation refits on each fold, so it needs that fold's ", "own fit: pass `fittedModelsCv = list( = )` ", "from a fineMappingPipeline() run with cvFolds > 1." - ) + ), + fittedModelsCv = fittedModelsCv ) if (is.null(seed) && !exists(".Random.seed") && verbose >= 1) { inform(str_c( @@ -2047,6 +2013,12 @@ twasWeightsCv <- function( .twasAsMethodRecord(weightMethods) } +# Whether `fittedModelsCv` carries per-fold fits for `tk`. +# @noRd +.twasFoldFitAvailable <- function(tk, fittedModelsCv) { + !is.null(.twasFoldFitsFor(fittedModelsCv, tk)) +} + # Fit one TWAS weight method by name against the filtered design matrix, # embedding the fitted weights back into the full variant space. `ctx` carries # the shared fit state (X, Y, Xfiltered, validColumns, fitRetention, @@ -2321,8 +2293,9 @@ twasWeightsCv <- function( #' are already standardized. Default \code{FALSE}. #' @param weightsDataType Character or \code{NULL}. Data-type label recorded on #' the weights (e.g. \code{"individual"}). -#' @param ldSketch A genotype panel (see \code{\link{readGenotypes}}) to -#' record on the weights as their LD sketch, or \code{NULL}. +#' @param ldReference An LD reference (a genotype panel, see +#' \code{\link{readGenotypes}}, a \code{GenotypeMixture} or an +#' \code{LdMatrix}) to record on the weights, or \code{NULL}. #' @param seed Integer or \code{NULL}. When supplied, seeds the main-process #' RNG and the parallel method-fitting RNG via the \code{BiocParallel} #' \code{RNGseed}, for reproducibility under multi-threading. The @@ -2360,7 +2333,7 @@ learnTwasWeights <- function( fitRetention = c("none", "slim", "full"), weightStandardized = FALSE, weightsDataType = NULL, - ldSketch = NULL, + ldReference = NULL, verbose = 1, seed = NULL ) { @@ -2391,7 +2364,7 @@ learnTwasWeights <- function( fitRetention = fitRetention, weightStandardized = weightStandardized, weightsDataType = weightsDataType, - ldSketch = ldSketch, + ldReference = ldReference, verbose = verbose, seed = seed ) @@ -2479,17 +2452,15 @@ learnTwasWeights <- function( .twasRequireLearnFits <- function(resolvedMethods, fittedModels) { .twasRequireSusieFits( resolvedMethods, - available = partial( - .twasLearnFitAvailable, - resolvedMethods = resolvedMethods, - fittedModels = fittedModels - ), + available = .twasLearnFitAvailable, fnLabel = "learnTwasWeights", how = str_c( "Pass it as `fittedModels = list( = )`, or on the ", "method's own arguments; run fineMappingPipeline() first to ", "produce one." - ) + ), + resolvedMethods = resolvedMethods, + fittedModels = fittedModels ) } @@ -2505,7 +2476,7 @@ learnTwasWeights <- function( fitRetention, weightStandardized, weightsDataType, - ldSketch, + ldReference, verbose, seed ) { @@ -2539,7 +2510,7 @@ learnTwasWeights <- function( ) |> .twasApplyRownames(X) rows <- .buildTwasWeightEntries(weightsList, .twasVariantIds(X), ctx) - .twasLearnResult(rows, ldSketch) + .twasLearnResult(rows, ldReference) } # The learn path's column-wise row record as a TwasWeights collection. NOT @@ -2547,14 +2518,14 @@ learnTwasWeights <- function( # and map_chr()s across them -- a different input entirely, and sharing a # name with it would let collate order decide which one a call reaches. # @noRd -.twasLearnResult <- function(rows, ldSketch) { +.twasLearnResult <- function(rows, ldReference) { TwasWeights( studyName = rows$study, context = rows$context, trait = rows$trait, method = rows$method, entry = rows$entry, - ldSketch = ldSketch + ldReference = ldReference ) } @@ -2748,7 +2719,7 @@ estimateSparsity <- function(weightResults) { } # Route a caller's per-method arguments into a weight function. A wrapper's -# own formals (a pre-fit, fitRetention, initPriorSd, ...) bind by name; any +# own formals (a pre-fit, fitRetention, ...) bind by name; any # else is a tool option and goes in the wrapper's `methodArgs` list, so an # unknown option errors inside the wrapper rather than vanishing. Wrappers # with no `methodArgs` formal get everything by name, and R reports an unused diff --git a/R/twasWeightsPipeline.R b/R/twasWeightsPipeline.R index 3cc2440ed..cb865b6cd 100644 --- a/R/twasWeightsPipeline.R +++ b/R/twasWeightsPipeline.R @@ -7,12 +7,12 @@ # and pads a side lacking an optional column such as joint* / region). # @noRd #' @importFrom checkmate assertClass -.rbindTwasWeights <- function(a, b, ldSketch = NULL) { +.rbindTwasWeights <- function(a, b, ldReference = NULL) { assertClass(a, "TwasWeights") assertClass(b, "TwasWeights") # Carry forward every column (joint*, region, ...) and reconcile the # collection-level slots via the shared combine. - .combineTupleCollections(list(a, b), ldSketch, ".rbindTwasWeights") + .combineTupleCollections(list(a, b), ldReference, ".rbindTwasWeights") } # Normalize combine() varargs: accept either N objects or a single list of @@ -49,8 +49,8 @@ #' #' @param ... Two or more \code{TwasWeights} objects, or a single \code{list} of #' them. -#' @param ldSketch Optional genotype panel (see \code{\link{readGenotypes}}) to -#' attach to the +#' @param ldReference Optional genotype panel (see \code{\link{readGenotypes}}) +#' to attach to the #' combined collection. Default \code{NULL}. Applied when combining two or #' more inputs; a single input is returned unchanged. #' @return A single combined \code{TwasWeights}. @@ -66,9 +66,9 @@ #' combineTwasWeights(tw1, tw2) #' @importFrom stringr str_starts #' @export -combineTwasWeights <- function(..., ldSketch = NULL) { +combineTwasWeights <- function(..., ldReference = NULL) { parts <- .asCombineList(list(...), "TwasWeights", "combineTwasWeights") - .combineTupleCollections(parts, ldSketch, "combineTwasWeights") + .combineTupleCollections(parts, ldReference, "combineTwasWeights") } # --- Multi-region (jointRegions) helpers for the QtlDataset method ---------- @@ -169,21 +169,6 @@ combineTwasWeights <- function(..., ldSketch = NULL) { ) } -# Mapping from short / canonical TWAS weight-method name to dispatch -# capability. Used to reject incompatible (input class, method) pairs. -# -# `allowsIndiv` : may be invoked on a QtlDataset (individual-level X, Y). -# `allowsRss` : may be invoked on a QtlSumStats / GwasSumStats (RSS). -# `multivariate` : requires a multi-trait / multi-context Y (mvsusie / -# mr.mash family). -# -# Rules from `dev/refactor-design.md` (`twasWeightsPipeline` row): -# - PRS-CS is RSS-only. -# - BGLR / CRAN-stable qgg methods (bayes_a/b/c/l/n/r, b_lasso, dpr_*) -# are individual-level only. -# - mr.mash / mvsusie follow the multi-trait / multi-context rules of -# the mvSuSiE fine-mapping family. -# @noRd # User-facing TWAS method tokens are unified across input classes; # auto-dispatch picks the individual-level vs sumstat implementation based # on the QtlDataset / QtlSumStats input. Each entry records: @@ -198,9 +183,10 @@ combineTwasWeights <- function(..., ldSketch = NULL) { # b_lasso) are individual-only until the qgg CRAN release adds qBayes # sumstat support. dpr_gibbs has the SDPR sumstat counterpart; # dpr_vb / dpr_adaptive_gibbs remain individual-only. enet has no cpp11 -# sumstat solver yet (lassosumRssRcpp is pure L1, no alpha mixing) and is +# sumstat solver yet (lassosumRssCpp is pure L1, no alpha mixing) and is # documented as individual-only for now. prsCs has no individual-level # counterpart (it is a sumstat-only Bayesian shrinkage method). +# @noRd .twasMethodCapabilities <- list( # NOTE: fine-mapping methods (susie / susieInf / susieAsh / mvsusie / # fsusie) are NOT listed here. Their availability is governed by @@ -369,11 +355,6 @@ combineTwasWeights <- function(..., ldSketch = NULL) { list(tokens = unique(methods), methodList = methodList) } -# Named-list `methods`: re-key each entry to its canonical `_weights` -# name, merge the caller's kwargs over the method's defaults, and carry the -# token->impl map as an "impl" attribute (without this, downstream -# .resolveMethodFunction falls back to the bare token, which is not a function). -# @noRd # One `methods` entry re-keyed to its canonical name, with the caller's # kwargs merged over the method's defaults. A token with no learner default # is kept under its own name (the capability gate reports it downstream). @@ -393,6 +374,11 @@ combineTwasWeights <- function(..., ldSketch = NULL) { ) } +# Named-list `methods`: re-key each entry to its canonical `_weights` +# name, merge the caller's kwargs over the method's defaults, and carry the +# token->impl map as an "impl" attribute (without this, downstream +# .resolveMethodFunction falls back to the bare token, which is not a function). +# @noRd .twasNormalizeListMethods <- function(methods) { entries <- map(names(methods), .twasNormalizeOneMethod, methods = methods) # Later entries win on a repeated canonical key, as the keyed assignment @@ -610,7 +596,7 @@ combineTwasWeights <- function(..., ldSketch = NULL) { } # Canonical fine-mapping tokens actually present as methods in a -# FineMappingResult, matched tolerantly across canonical / camelCase / +# fine-mapping result, matched tolerantly across canonical / camelCase / # snake_case spellings (mirrors the candidate logic in .twasFineMappingFits). # @noRd .twasFineMappingMethodsPresent <- function(fineMappingResult) { @@ -641,7 +627,7 @@ combineTwasWeights <- function(..., ldSketch = NULL) { } # Reject fine-mapping methods (susie / susieInf / susieAsh / mvsusie / -# fsusie) when no FineMappingResult is supplied. twasWeightsPipeline is +# fsusie) when no fine-mapping result is supplied. twasWeightsPipeline is # not allowed to re-fit fine-mapping models from scratch; users must run # fineMappingPipeline() first and pass the result via `fineMappingResult`. # Input-class compatibility (e.g. fsusie has no QtlSumStats path) is @@ -653,7 +639,8 @@ combineTwasWeights <- function(..., ldSketch = NULL) { tokens, fineMappingResult, inputKind, - cvFolds = 0 + cvFolds = 0, + fitFullData = TRUE ) { fmTokens <- intersect(tokens, .twasFineMappingTokens()) if (length(fmTokens) == 0L) { @@ -666,16 +653,19 @@ combineTwasWeights <- function(..., ldSketch = NULL) { .twasRequireFmResult(fineMappingResult, fmTokens) .twasCheckFmPresent(fmTokens, fineMappingResult) .twasCheckFmCvPresent(fmTokens, fineMappingResult, cvFolds) + if (isTRUE(fitFullData)) { + .twasCheckFmFullFits(fmTokens, fineMappingResult) + } invisible(NULL) } -# Whether row `i` of a FineMappingResult carries a cross-validation result. +# Whether row `i` of a fine-mapping result carries a cross-validation result. # @noRd .fmrRowHasCv <- function(i, fineMappingResult) { !is.null(cvResult(.fmrRowParts(fineMappingResult, i))) } -# Whether a FineMappingResult carries any cross-validation result at all. +# Whether a fine-mapping result carries any cross-validation result at all. # @noRd .fmrAnyCvResult <- function(fineMappingResult) { if (!is(fineMappingResult, "FineMappingResultBase")) { @@ -715,6 +705,31 @@ combineTwasWeights <- function(..., ldSketch = NULL) { abort(msg) } +# Full-data weights for a fine-mapping-derived method are read off the +# fine-mapping fit, so a row from a cross-validation-only fine-mapping run +# (CrossValidationParam(fitFullData = FALSE)) cannot supply them. Say so up +# front rather than failing inside the extraction. +# @noRd +.twasCheckFmFullFits <- function(fmTokens, fineMappingResult) { + rows <- seq_len(nrow(fineMappingResult)) + method <- as.character(fineMappingResult$method) + noFit <- rows[ + is_in(method, fmTokens) & + !map_lgl(rows, .fmrRowHasFit, fineMappingResult = fineMappingResult) + ] + if (length(noFit) == 0L) { + return(invisible(NULL)) + } + msg <- glue( + "twasWeightsPipeline: {length(noFit)} fineMappingResult row(s) for ", + "{str_flatten(unique(method[noFit]), ', ')} carry no full-data fit -- ", + "they come from a cross-validation-only fine-mapping run. Rerun ", + "fineMappingPipeline() with CrossValidationParam(fitFullData = TRUE), ", + "or run twasWeightsPipeline() cross-validation-only as well." + ) + abort(msg) +} + # Reject fine-mapping methods that have no TWAS-weight extractor (e.g. fsusie). # @noRd .twasCheckFmAdapters <- function(fmTokens) { @@ -732,7 +747,7 @@ combineTwasWeights <- function(..., ldSketch = NULL) { } # A supplied fineMappingResult is mandatory (fine-mapping is never re-fit) and -# must be a FineMappingResult. +# must be a FineMappingResultBase. # @noRd #' @importFrom checkmate assertClass .twasRequireFmResult <- function(fineMappingResult, fmTokens) { @@ -742,7 +757,7 @@ combineTwasWeights <- function(..., ldSketch = NULL) { "twasWeightsPipeline: method(s) {fmStr} are fine-mapping ", "methods and may not be re-fit by twasWeightsPipeline. Run ", "fineMappingPipeline() first and pass the result via ", - "`fineMappingResult = `." + "`fineMappingResult = `." ) abort(msg) } @@ -766,7 +781,7 @@ combineTwasWeights <- function(..., ldSketch = NULL) { "twasWeightsPipeline: method(s) {missingStr} were requested but ", "the supplied fineMappingResult contains no such fine-mapping ", "fit. Run fineMappingPipeline() with method(s) {missingStr} first ", - "and pass the result via `fineMappingResult = `." + "and pass the result via `fineMappingResult = `." ) abort(msg) } @@ -788,11 +803,6 @@ combineTwasWeights <- function(..., ldSketch = NULL) { FALSE } -# Enforce the multi-trait / multi-context rule for mvsusie / mr.mash -# methods (same family as the fine-mapping mvSuSiE rule in the design -# doc). Multivariate methods need at least 2 traits *or* at least 2 -# contexts in the Y matrix passed to learnTwasWeights. -# @noRd # The multivariate rule across a MultiStudyQtlDataset's components. # # A multivariate method -- mvsusie, mr.mash, fsusie, and the PCA alternative @@ -835,13 +845,18 @@ combineTwasWeights <- function(..., ldSketch = NULL) { # @noRd .twasComponentIsUnivariate <- function(key, components) { d <- components[[key]] - nTraits <- length(tryCatch(traitNames(d), error = function(cnd) { + nTraits <- length(try_fetch(traitNames(d), error = function(cnd) { character(0) })) - nCtx <- length(tryCatch(contexts(d), error = function(cnd) character(0))) + nCtx <- length(try_fetch(contexts(d), error = function(cnd) character(0))) nTraits < 2L && nCtx < 2L } +# Enforce the multi-trait / multi-context rule for mvsusie / mr.mash +# methods (same family as the fine-mapping mvSuSiE rule in the design +# doc). Multivariate methods need at least 2 traits *or* at least 2 +# contexts in the Y matrix passed to learnTwasWeights. +# @noRd .twasCheckMultivariateY <- function(tokens, nTraits, nContexts) { multivariateTokens <- tokens[map_lgl(tokens, .twasIsMultivariateToken)] if (length(multivariateTokens) == 0L) { @@ -858,7 +873,10 @@ combineTwasWeights <- function(..., ldSketch = NULL) { } } -# Reject SumStats inputs that have not been QC'd via summaryStatsQc. +# Reject SumStats inputs that have not been QC'd via summaryStatsQc(), which +# is mandatory before twasWeightsPipeline for SumStats input: it also drops the +# variants absent from the ldReference, so once this passes every entry's SNP +# set is a subset of the ldReference panel. # @noRd .twasAssertQcd <- function(sumstats) { if (length(qcInfo(sumstats)) == 0L) { @@ -900,7 +918,7 @@ combineTwasWeights <- function(..., ldSketch = NULL) { .twrRowParts(twasWeights, idx[[1L]]) } -# Convert a FineMappingResult (single-method susie/susie_inf row matched +# Convert a fine-mapping result (single-method susie/susie_inf row matched # to the requested study/context/trait) into a `fittedModels` list # suitable for `learnTwasWeights`. Pulls the trimmedFit from the matching # entry. Returns a (possibly empty) list. @@ -977,17 +995,6 @@ combineTwasWeights <- function(..., ldSketch = NULL) { fits[[token]] } -# Collect the cross-validation payload that fineMappingPipeline stored on the -# FineMappingResult for one (study, context, trait) tuple. fineMapping records -# one cvResult per (study, context, trait, method) entry (samplePartition + -# per-fold predictions/metrics, keyed by the TWAS snake method name); this -# merges them across the fine-mapping methods of the tuple into a single -# twasWeightsCv()-shaped list so twasWeightsPipeline can reuse the partition and -# feed those out-of-fold predictions into the SR-TWAS ensemble without re- -# fitting the fine-mapping models. A multi-region entry stores cvResult as a -# per-region list; the first region carrying CV is used. Returns NULL when no -# fine-mapping entry for the tuple recorded CV. -# @noRd # Concatenate per-row lists, empty-safe. # @noRd .twasCvConcat <- function(pieces) { @@ -1016,6 +1023,17 @@ combineTwasWeights <- function(..., ldSketch = NULL) { hit[[1L]] } +# Collect the cross-validation payload that fineMappingPipeline stored on the +# fine-mapping result for one (study, context, trait) tuple. It records one +# cvResult per (study, context, trait, method) entry (samplePartition + +# per-fold predictions/metrics, keyed by the TWAS snake method name); this +# merges them across the fine-mapping methods of the tuple into a single +# twasWeightsCv()-shaped list so twasWeightsPipeline can reuse the partition and +# feed those out-of-fold predictions into the SR-TWAS ensemble without re- +# fitting the fine-mapping models. A multi-region entry stores cvResult as a +# per-region list; the first region carrying CV is used. Returns NULL when no +# fine-mapping entry for the tuple recorded CV. +# @noRd .twasCvResultFor <- function(fineMappingResult, studyName, context, trait) { if (is.null(fineMappingResult)) { return(NULL) @@ -1056,8 +1074,9 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' \itemize{ #' \item a \code{\link{QtlDataset}} for individual-level cohort fits; #' \item a \code{\link{QtlSumStats}} for per-trait RSS fits; -#' \item a \code{\link{GwasSumStats}} for per-LD-block PRS-CS-style fits -#' from GWAS summary statistics. +#' \item a \code{\link{MultiStudyQtlDataset}}, whose individual-level +#' studies and optional summary-statistic studies are each fitted, +#' then combined. #' } #' #' Method-restriction rules (enforced): @@ -1065,33 +1084,35 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' \item \code{mr.mash}, \code{mvsusie} follow the multi-trait / #' multi-context rules of the fine-mapping \code{mvsusie} family #' (require at least two traits OR at least two contexts). -#' \item RSS-only methods (PRS-CS, \code{lassosumRss}, SDPR, all -#' \code{*Rss} variants) are rejected on \code{QtlDataset} -#' input. -#' \item Individual-level-only methods (BGLR and CRAN-stable qgg: -#' \code{bayes_a/b/c/l/n/r}, \code{b_lasso}, \code{dpr_*}) are -#' rejected on \code{QtlSumStats} / \code{GwasSumStats} input. +#' \item \code{prsCs} runs only on summary statistics, so it is rejected +#' on \code{QtlDataset} input. +#' \item \code{enet}, \code{dprVb}, \code{dprAdaptiveGibbs} and the +#' Bayesian alphabet (\code{bayesA}, \code{bayesB}, \code{bayesC}, +#' \code{bayesL}, \code{bayesN}, \code{bayesR}, \code{bLasso}) run +#' only on individual-level data, so they are rejected on +#' \code{QtlSumStats} input. #' } #' -#' Both \code{QtlSumStats} and \code{GwasSumStats} inputs must have been QC'd -#' via \code{\link{summaryStatsQc}} first; otherwise an error is raised pointing +#' A \code{QtlSumStats} input must have been QC'd via +#' \code{\link{summaryStatsQc}} first; otherwise an error is raised pointing #' at that function. #' -#' The returned \code{\link{TwasWeights}} collection's \code{ldSketch} slot is -#' set automatically: \code{NULL} for individual-level fits, the input's -#' \code{ldSketch} for RSS-derived fits. +#' The returned \code{\link{TwasWeights}} collection's \code{ldReference} slot +#' is set automatically: \code{NULL} for individual-level fits, the input's +#' \code{ldReference} for RSS-derived fits. #' -#' Optionally a \code{FineMappingResult} may be supplied as a source of pre-fit -#' SuSiE / SuSiE-inf / SuSiE-ash objects; their \code{trimmedFit} payloads are -#' passed through to \code{learnTwasWeights} / the RSS sub-pipelines via the +#' Optionally a \code{QtlFineMappingResult} may be supplied as a source of +#' pre-fit SuSiE / SuSiE-inf / SuSiE-ash objects; their stored fits are passed +#' through to \code{learnTwasWeights} / the RSS sub-pipelines via the #' \code{fittedModels} slot, avoiding a re-fit. #' -#' When the supplied \code{FineMappingResult} was produced with cross-validation -#' (\code{fineMappingPipeline(..., cvFolds > 1)}), each matching \code{(study, -#' context, trait)} entry's \code{cvResult} is reused: its fold partition -#' becomes the CV partition (unless \code{samplePartition} is given explicitly) -#' and its per-fold out-of-fold predictions/metrics are fed directly into the -#' SR-TWAS ensemble in place of re-fitting those fine-mapping methods here. +#' When the supplied result was produced with cross-validation (a +#' \code{\link{CrossValidationParam}} with \code{folds >= 2}), each matching +#' \code{(study, context, trait)} entry's \code{cvResult} is reused: its fold +#' partition becomes the CV partition (unless \code{samplePartition} is given +#' explicitly) and its per-fold out-of-fold predictions/metrics are fed +#' directly into the SR-TWAS ensemble in place of re-fitting those +#' fine-mapping methods here. #' Non-fine-mapping methods (lasso, enet, ...) are still cross-validated on the #' same shared partition. #' @@ -1101,14 +1122,25 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' embedded \code{QtlSumStats}, then rbinds the results. #' @param methods A character vector of short method names, a preset string #' (\code{"default"} or \code{"fastDefault"}), or a named list of -#' \code{_weights = args} entries. For QtlSumStats / GwasSumStats -#' inputs the default switches to the RSS preset (\code{c("susieRss", -#' "susieInfRss", "lassosumRss", "prsCs", "sdpr")}). -#' @param contexts Optional character vector of contexts to restrict processing -#' to (QtlDataset / QtlSumStats inputs). Default \code{NULL} (use all -#' contexts). -#' @param traitId Optional character vector of trait identifiers to restrict -#' processing to (QtlDataset / QtlSumStats inputs). Default \code{NULL}. +#' \code{_weights = args} entries. For QtlSumStats input the +#' default is \code{c("lasso", "prsCs", "dprGibbs")}, which there run +#' lassosum, PRS-CS and SDPR; the fine-mapping methods are not in it, and +#' are requested by name together with a \code{fineMappingResult}. +#' @param contexts Optional context selection; \code{NULL} (the default) uses +#' every context. A character vector applies to every study: a context no +#' study has is an error, and one only some studies lack is skipped for +#' those, with a warning. A list keyed by study name picks each study's +#' contexts separately (\code{list(A = "liver")}); studies it does not name +#' keep all of theirs. +#' @param traitId Optional trait selection; \code{NULL} (the default) uses +#' every trait, or those overlapping \code{region} when one is given. A +#' character vector applies to every study. A list keyed by study name +#' picks each study's traits separately (\code{list(A = "ENSG1")}), and a +#' study's entry may itself be a list keyed by context +#' (\code{list(A = list(brain = "ENSG1"))}). Studies and contexts the list +#' does not name keep all of their traits. A joint fit spanning a study's +#' contexts uses every trait selected in any of them, minus the (context, +#' trait) pairs a per-context entry leaves out. #' @param region Optional variant window for QtlDataset trait selection: a #' \code{GRanges}, a \code{"chr:start-end"} string, or a one-row data.frame #' with \code{chrom}/\code{start}/\code{end}. Mutually exclusive with @@ -1117,17 +1149,17 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' position when extracting variants. Required when \code{traitId} is #' supplied. Mutually exclusive with \code{region}. #' @section Panel filters on the RSS path: On \code{QtlSumStats} input there -#' is no genotype matrix to filter, so \code{panelFilter}'s +#' is no genotype matrix to filter, so \code{panelFilterParam}'s #' \code{mafCutoff} / \code{macCutoff} / \code{imissCutoff} are measured #' against the \strong{LD reference panel} instead: a variant whose panel #' genotypes fall below the cutoffs is dropped before the z-scores and LD #' matrix are built. The thresholds mean the same thing as -#' \code{genotypeFilter}'s on the \code{QtlDataset} path (MAC is converted -#' to a MAF equivalent and the stricter of the two applies), so one number -#' carries across input types. The defaults filter nothing. +#' \code{genotypeFilterParam}'s on the \code{QtlDataset} path (MAC is +#' converted to a MAF equivalent and the stricter of the two applies), so +#' one number carries across input types. The defaults filter nothing. #' #' This discards \emph{observed} variants, unlike -#' \code{summaryStatsQc(imputeArgs = ...)}, which only bounds which variants +#' \code{summaryStatsQc(raissParam = ...)}, which only bounds which variants #' RAISS will impute. #' #' @param genotypeFilterParam For QtlDataset: per-call genotype-filter @@ -1149,14 +1181,20 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' flat \code{cvResult} data frame (one row per region). \code{TRUE} #' concatenates the ranges' genotypes into one joint fit. Ignored for a #' single-range / cis request. -#' @param jointSpecification Optional joint-fit specification (NULL by default). -#' When NULL, the pipeline runs the implicit multi-trait / multi-context -#' mr.mash branches as before. When non-NULL, the argument is parsed and -#' validated via the joint-spec grammar documented under -#' \code{parseJointSpecification}; the per-spec axis dispatcher implementation -#' is in progress and a non-NULL value currently errors with an informative -#' message. -#' @param fineMappingResult Optional \code{FineMappingResult}. When supplied, +#' @param jointSpecification Optional joint-fit specification saying which +#' axes \code{mrmash} fits across. When NULL (the default) it runs its +#' implicit multi-trait / multi-context fit. Otherwise give a character +#' vector of axes (one spec) or a list of specs. A spec is a character +#' vector drawn from \code{"study"}, \code{"context"} and \code{"trait"}, +#' or \code{list(axes = , scope = )}, where \code{scope} +#' (keys \code{study}, \code{context}, \code{trait}) limits the fit to the +#' listed values, each of which must exist in \code{data}. A list-form +#' spec always goes inside the outer list, even on its own. Other methods +#' run per (study, context, trait) as usual. The \code{"study"} axis needs +#' summary statistics: it is rejected on a \code{QtlDataset}, and on a +#' \code{MultiStudyQtlDataset} only the summary-statistics studies take +#' part. +#' @param fineMappingResult Optional \code{QtlFineMappingResult}. When supplied, #' its SuSiE / SuSiE-inf / SuSiE-ash trimmed fits for the matching (study, #' context, trait) tuples are injected into \code{learnTwasWeights} via #' \code{fittedModels} so SuSiE-family weight methods reuse the prior fit @@ -1172,9 +1210,9 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' produce one fit jointly across multiple \code{(context, trait)} columns. #' @param mashPrior Optional \code{\link{MashPrior}} (or coercible) supplying #' data-driven prior matrices for mr.mash weight methods; \code{NULL} to skip. -#' @param fitFullData Logical. If \code{TRUE}, fit final weights on the full -#' data after cross-validation. Default \code{TRUE}. -#' @param usePCA Logical (length 1). \code{QtlDataset} only. When +#' @param usePCA Logical (length 1). Individual-level data only: a +#' \code{QtlDataset}, or the \code{QtlDataset} components of a +#' \code{MultiStudyQtlDataset}. When #' \code{TRUE}, each multi-trait context is additionally PCA-reduced and #' weights are fitted for each top principal component \emph{as a trait} #' (\code{topPC1}, \code{topPC2}, ...), alongside the per-trait rows. The @@ -1191,18 +1229,21 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' takes, which has no \code{"none"} --- its \code{susieFit} slot is part #' of the returned object's contract. #' -#' \code{"full"} is honoured only by the mr.mash engines, the only ones -#' that distinguish the two retaining levels; every other method keeps its -#' fit whole or not at all. Requesting \code{"full"} for a run where no -#' method can honour it is an error, and a mixed run warns rather than -#' dropping it in silence. +#' \code{"full"} is honoured by the mr.mash engines and, on summary +#' statistics, by \code{lasso}, \code{scad}, \code{mcp} and +#' \code{l0learn}, whose full fit also keeps every candidate's +#' coefficients; every other method keeps its fit whole or not at all. +#' Requesting \code{"full"} for a run where no method can honour it is an +#' error, and a mixed run warns rather than dropping it in silence. #' @param naAction Character. How to handle missing values in the extracted #' data. -#' @param crossValidationArgs Cross-validation settings, built with +#' @param crossValidationParam Cross-validation settings, built with #' \code{\link{CrossValidationParam}}: \code{folds} (default \code{0}, no #' CV), \code{threads}, \code{samplePartition}, \code{maxVariants} (cap -#' on the CV design matrix) and \code{weightMethods} (which methods to -#' cross-validate; unset means every method with non-zero weights). +#' on the CV design matrix), \code{weightMethods} (which methods to +#' cross-validate; unset means every method with non-zero weights) and +#' \code{fitFullData} (\code{FALSE} keeps only the cross-validation +#' results; default \code{TRUE}). #' #' \code{folds} defaulted to \code{5} before, while #' \code{\link{fineMappingPipeline}}'s defaulted to \code{0}. The two now @@ -1211,16 +1252,16 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' Cross-validation holds out \strong{samples}, so it is not possible on #' \code{QtlSumStats} input; that method \strong{errors} if it is set #' rather than returning results that were never cross-validated. -#' @param ensembleArgs SR-TWAS ensemble settings, built with +#' @param ensembleParam SR-TWAS ensemble settings, built with #' \code{\link{EnsembleParam}}: \code{enabled} (default \code{FALSE}), #' \code{r2Threshold}, \code{solver} and \code{alpha}. Stacking reads #' out-of-fold predictions, so \code{enabled = TRUE} requires -#' \code{crossValidation} with \code{folds >= 2} and is an error +#' \code{crossValidationParam} with \code{folds >= 2} and is an error #' otherwise --- it previously returned a result quietly missing its #' ensemble row. #' @param estimatePi If TRUE, estimate spike-and-slab sparsity from mr.ash #' before BGLR / qgg spike-and-slab methods that consume it. -#' @param residualizationArgs Covariate residualization settings, built with +#' @param residualizationParam Covariate residualization settings, built with #' \code{\link{ResidualizationParam}} and forwarded to #' \code{\link{residualizedPhenotypes}} / #' \code{\link{residualizedGenotypes}}: \code{phenotypeCovariates} and @@ -1232,7 +1273,8 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' \code{QtlDataset} / \code{MultiStudyQtlDataset} input only. A #' \code{QtlSumStats} run has no covariates to regress out, so the bundle #' is \strong{ignored} there rather than refused --- unlike -#' \code{crossValidation}, which errors. Residualization is on by default, +#' \code{crossValidationParam}, which errors. Residualization is on by +#' default, #' so refusing a non-default value would reject the default bundle; CV is #' off by default, so a non-default value there is an explicit request for #' something the input cannot do. @@ -1270,8 +1312,8 @@ combineTwasWeights <- function(..., ldSketch = NULL) { #' @param ... Reserved for method-specific arguments. #' #' @return A \code{\link{TwasWeights}} collection keyed by \code{(study, -#' context, trait, method)}. The \code{ldSketch} slot is \code{NULL} for -#' individual-level fits and equals the input's \code{ldSketch} for +#' context, trait, method)}. The \code{ldReference} slot is \code{NULL} for +#' individual-level fits and equals the input's \code{ldReference} for #' RSS-derived fits. #' @examples #' data(qtlDatasetExample) @@ -1299,23 +1341,23 @@ setGeneric("twasWeightsPipeline", function(data, ...) { )) } -# Run the multivariate joint TWAS-weight fit over the (context, trait) grid for -# `traits`: dispatch each cis-region through the joint engine and merge -# per-region results. `marker` = the TwasJointPipeline config; `ctx` bundles the -# shared state -# (xRegions, data, norm, useCtx, fineMappingResult, dataDrivenPriorMatricesCv, -# cisWindow, verbose). +# Run joint specs through the engine over every region block of `ctx` and +# merge the per-region results by (study, context, trait, method). Shared by +# the per-tuple multivariate grid (a synthesized context x trait spec) and the +# explicit jointSpecification path, so both fit under the same pipeline +# `marker` and the same engine `args`. `ctx` is .twasJointSetup()'s record. # @noRd -.twasRunMultivariateGrid <- function(traits, marker, ctx) { - synthSpec <- list(list(axes = c("context", "trait"), scope = NULL)) +.twasRunJointSpecGrid <- function(spec, tokens, contexts, traits, marker, ctx) { allLabs <- map_chr(ctx$xRegions, .twasRegionLabel) allRegions <- map( seq_along(ctx$xRegions), - .twasMvGridRegion, - synthSpec = synthSpec, + .twasJointSpecRegion, + spec = spec, + tokens = tokens, + contexts = contexts, + traits = traits, marker = marker, - ctx = ctx, - traits = traits + ctx = ctx ) keep <- !map_lgl(allRegions, is.null) perRegion <- allRegions[keep] @@ -1343,21 +1385,25 @@ setGeneric("twasWeightsPipeline", function(data, ...) { fineMappingResult, dataDrivenPriorMatricesCv, cisWindow, - residualizationArgs, + residualizationParam, verbose ) { - .twasRunMultivariateGrid( - grid$allTraits, + # A per-context selection goes in whole: the joint scope then takes each + # study's union of it and drops the (context, trait) pairs it leaves out. + .twasRunJointSpecGrid( + list(list(axes = c("context", "trait"), scope = NULL)), + norm$tokens, + grid$useCtx, + grid$traitSelection %||% grid$allTraits, marker, list( xRegions = xRegions, data = data, norm = norm, - useCtx = grid$useCtx, fineMappingResult = fineMappingResult, dataDrivenPriorMatricesCv = dataDrivenPriorMatricesCv, cisWindow = cisWindow, - residualizationArgs = residualizationArgs, + residualizationParam = residualizationParam, verbose = verbose ) ) @@ -1378,7 +1424,7 @@ setGeneric("twasWeightsPipeline", function(data, ...) { dataDrivenPriorMatricesCv, cisWindow, naAction, - residualizationArgs, + residualizationParam, usePCA, nPCs, verbose @@ -1393,7 +1439,7 @@ setGeneric("twasWeightsPipeline", function(data, ...) { fineMappingResult = fineMappingResult, dataDrivenPriorMatricesCv = dataDrivenPriorMatricesCv, cisWindow = cisWindow, - residualizationArgs = residualizationArgs, + residualizationParam = residualizationParam, verbose = verbose )) } @@ -1401,6 +1447,7 @@ setGeneric("twasWeightsPipeline", function(data, ...) { studyName = grid$study, useCtx = grid$useCtx, allTraits = grid$allTraits, + traitSelection = grid$traitSelection, marker = marker, data = data, xRegions = xRegions, @@ -1410,7 +1457,7 @@ setGeneric("twasWeightsPipeline", function(data, ...) { dataDrivenPriorMatricesCv = dataDrivenPriorMatricesCv, cisWindow = cisWindow, naAction = naAction, - residualizationArgs = residualizationArgs, + residualizationParam = residualizationParam, usePCA = usePCA, nPCs = nPCs, verbose = verbose @@ -1423,20 +1470,27 @@ setGeneric("twasWeightsPipeline", function(data, ...) { .twasQdsAssertParams <- function( methods, fitRetention, - crossValidationArgs, - ensembleArgs + crossValidationParam, + ensembleParam ) { - .twasWarnUnretainableDetail(fitRetention, methods) + .twasWarnUnretainableDetail(fitRetention, methods, "QtlDataset") .assertMethodParam( - crossValidationArgs, - "CrossValidationParam", - "crossValidation" + crossValidationParam, + "CrossValidationParam" ) - .assertMethodParam(ensembleArgs, "EnsembleParam", "ensemble") - .ensembleAssertCv(ensembleArgs, crossValidationArgs) + .assertMethodParam(ensembleParam, "EnsembleParam") + .ensembleAssertCv(ensembleParam, crossValidationParam) invisible(NULL) } +# The QtlDataset method of twasWeightsPipeline(). Each stage returns only the +# values it derives; nothing is grafted onto a captured environment. `cfg` +# (.twasJointCfg()) is the one record of the run's settings, and the pipeline +# marker is built from it BEFORE the joint phase, so explicit joint fits run +# under the same marker and engine args as the per-tuple loop. The joint phase +# consumes the mrmash token; `norm` comes back holding only the methods that +# still have to go through the per-tuple loop. +# @noRd .twasPipelineQtlDataset <- function( data, methods = "default", @@ -1450,14 +1504,13 @@ setGeneric("twasWeightsPipeline", function(data, ...) { fineMappingResult = NULL, twasWeights = NULL, mashPrior = NULL, - crossValidationArgs = CrossValidationParam(), - fitFullData = TRUE, - ensembleArgs = EnsembleParam(), + crossValidationParam = CrossValidationParam(), + ensembleParam = EnsembleParam(), estimatePi = TRUE, usePCA = FALSE, nPCs = 10L, fitRetention = c("slim", "none", "full"), - residualizationArgs = ResidualizationParam(), + residualizationParam = ResidualizationParam(), weightsDataType = NULL, naAction = c("drop", "impute"), verbose = 1, @@ -1465,15 +1518,14 @@ setGeneric("twasWeightsPipeline", function(data, ...) { ) { naAction <- arg_match(naAction) fitRetention <- arg_match(fitRetention) + contexts <- parseContexts(contexts, data) + traitId <- parseTraitIds(traitId, data) .twasQdsAssertParams( methods, fitRetention, - crossValidationArgs, - ensembleArgs + crossValidationParam, + ensembleParam ) - cvCfg <- .cvResolve(crossValidationArgs) - # Each stage returns only the values it derives; nothing is grafted onto a - # captured environment. resolved <- .twasQdsResolveInputs( data = data, region = region, @@ -1482,67 +1534,59 @@ setGeneric("twasWeightsPipeline", function(data, ...) { genotypeFilterParam = genotypeFilterParam ) data <- resolved$data - xRegions <- resolved$xRegions - # One record of the settings the joint phase and the shared dispatcher - # both need, so each names them once. - jointCfg <- list( - data = data, + cfg <- .twasJointCfg( contexts = contexts, traitId = traitId, cisWindow = cisWindow, - dataType = weightsDataType, - verbose = verbose, - xRegions = xRegions, + xRegions = resolved$xRegions, + fineMappingResult = fineMappingResult, + mashPrior = mashPrior, + crossValidationParam = crossValidationParam, + ensembleParam = ensembleParam, + estimatePi = estimatePi, fitRetention = fitRetention, + residualizationParam = residualizationParam, + weightsDataType = weightsDataType, + verbose = verbose, seed = seed ) - joint <- .twasQdsResolveTokens( + resolvedTokens <- .twasQdsResolveTokens( jointSpecification, methods, fineMappingResult, - cvCfg$folds, - fitFullData, - mashPrior, - cvCfg$samplePartition, - jointCfg + .cvResolve(crossValidationParam), + data + ) + setup <- .twasJointSetup(data, resolvedTokens$norm, cfg) + joint <- .twasQdsJointPhase( + resolvedTokens$parsedJointSpec, + setup$marker, + setup$ctx, + contexts, + traitId ) if (joint$done) { return(joint$result) } - # The joint phase consumes the mrmash token; `norm` comes back holding - # only the methods that still have to go through the per-tuple loop. - norm <- joint$norm - samplePartition <- joint$samplePartition - dataDrivenPriorMatricesCv <- joint$dataDrivenPriorMatricesCv - grid <- .twasQdsResolveGrid(data, contexts, traitId, region, norm$tokens) - # The bundles are the user-facing form; below this line the marker and - # the joint engine that reads it keep plain scalars. - marker <- .twasQdsMarker( - crossValidationArgs = list_assign( - cvCfg, - samplePartition = samplePartition - ), - ensembleArgs = .ensembleResolve(ensembleArgs), - fitFullData = fitFullData, - weightsDataType = weightsDataType, - standardized = .qtlScaleResiduals(data, residualizationArgs), - fitRetention = fitRetention, - estimatePi = estimatePi, - verbose = verbose, - seed = seed + grid <- .twasQdsResolveGrid( + data, + contexts, + traitId, + region, + joint$norm$tokens ) tw <- .twasQdsRunGrid( grid, - marker, + setup$marker, data = data, - xRegions = xRegions, - norm = norm, + xRegions = resolved$xRegions, + norm = joint$norm, fineMappingResult = fineMappingResult, twasWeights = twasWeights, - dataDrivenPriorMatricesCv = dataDrivenPriorMatricesCv, + dataDrivenPriorMatricesCv = setup$ctx$dataDrivenPriorMatricesCv, cisWindow = cisWindow, naAction = naAction, - residualizationArgs = residualizationArgs, + residualizationParam = residualizationParam, usePCA = usePCA, nPCs = nPCs, verbose = verbose @@ -1558,40 +1602,6 @@ setMethod( .twasPipelineQtlDataset ) -# `cisWindow` expands a trait's own coordinates; `region` is literal. Supplying -# both signals a misunderstanding -> reject. -# @noRd -.twasQdsCheckRegionCisWindow <- function( - region, - cisWindow -) { - if (!is.null(region) && !is.null(cisWindow)) { - msg <- glue( - "twasWeightsPipeline(QtlDataset): specify either `region` or ", - "`cisWindow`, not both. `cisWindow` expands each trait's own ", - "coordinates, whereas `region` is the literal variant window." - ) - abort(msg) - } - invisible(NULL) -} - -# fitFullData = FALSE (CV-only) is meaningful only with cross-validation. -# @noRd -.twasQdsCheckFitFull <- function( - fitFullData, - cvFolds -) { - if (!isTRUE(fitFullData) && cvFolds <= 1L) { - msg <- glue( - "twasWeightsPipeline: fitFullData = FALSE requires ", - "cross-validation (cvFolds > 1)." - ) - abort(msg) - } - invisible(NULL) -} - # Unpack the MashPrior bundle: route the full-data prior into the mr.mash method # args, the per-fold priors + fold partition into the CV machinery. Returns the # updated parameter bundle. @@ -1650,44 +1660,41 @@ setMethod( jointRegions, genotypeFilterParam ) { - .twasQdsCheckRegionCisWindow(region, cisWindow) + .rejectRegionWithCisWindow( + region, + cisWindow, + "twasWeightsPipeline(QtlDataset)" + ) list( data = .qtlApplyFilterOverrides(data, genotypeFilterParam), xRegions = .makeXRegions(region, jointRegions) ) } -# Run the joint engine for a QtlDataset with the given spec + token set. -# @noRd -.twasQdsJointDispatch <- function(jointSpec, tokens, cfg) { - .twasDispatchJointSpecsQtlDataset( - jointSpec, - cfg$data, - tokens, - cfg$contexts, - cfg$traitId, - cfg$cisWindow, - cfg$dataType, - cfg$verbose, - xRegions = cfg$xRegions, - fitRetention = cfg$fitRetention, - seed = cfg$seed - ) -} # Explicit jointSpecification path: run the per-spec axis dispatcher for # mr.mash. Returns list(done, result, norm): `done` requests an early return # with `result`; otherwise `norm` is the mrmash-stripped normalization for the # per-tuple loop below. # @noRd -.twasQdsJointPhase <- function(parsedJointSpec, norm, cfg) { +.twasQdsJointPhase <- function( + parsedJointSpec, + marker, + ctx, + contexts, + traitId +) { + norm <- ctx$norm if (length(parsedJointSpec) == 0L) { return(list(done = FALSE, result = NULL, norm = norm)) } - jointResult <- .twasQdsJointDispatch( + jointResult <- .twasDispatchJointSpecsQtlDataset( parsedJointSpec, - intersect(norm$tokens, "mrmash"), - cfg + norm$tokens, + marker, + ctx, + contexts, + traitId ) keep <- setdiff(norm$tokens, intersect(norm$tokens, "mrmash")) if (length(keep) == 0L) { @@ -1715,40 +1722,106 @@ setMethod( ) } -# Normalize methods, run the capability / fine-mapping / fit-full gates and the -# mash-prior unpacking, then hand off to the joint phase. Returns the phase -# record extended with the mash-derived sample partition and CV prior matrices -# the per-tuple loop still needs. Mirrors .fmQdsResolveTokens. +# Parse the joint spec and normalize the methods, then run the capability, +# per-input argument and fine-mapping gates. The mash-prior unpacking and the +# joint phase follow once the pipeline marker exists (.twasJointSetup()), so +# that explicit joint fits run under the same marker as everything else. # @noRd .twasQdsResolveTokens <- function( jointSpecification, methods, fineMappingResult, - cvFolds, - fitFullData, - mashPrior, - samplePartition, - cfg + cvCfg, + data ) { - parsedJointSpec <- parseJointSpecification(jointSpecification, cfg$data) - rawNorm <- .twasNormalizeMethods(methods) - .twasCheckMethodCapabilities(rawNorm$tokens, "QtlDataset") + parsedJointSpec <- parseJointSpecification(jointSpecification, data) + norm <- .twasNormalizeMethods(methods) + .twasCheckMethodCapabilities(norm$tokens, "QtlDataset") .twasCheckMethodArgsForInput( - .twasMethodListArgs(rawNorm$methodList), + .twasMethodListArgs(norm$methodList), "QtlDataset" ) .twasCheckFineMappingMethods( - rawNorm$tokens, + norm$tokens, fineMappingResult, "QtlDataset", - cvFolds = cvFolds + cvFolds = cvCfg$folds, + fitFullData = cvCfg$fitFullData ) - .twasQdsCheckFitFull(fitFullData, cvFolds) - mash <- .twasQdsUnpackMash(mashPrior, samplePartition, rawNorm) - list_assign( - .twasQdsJointPhase(parsedJointSpec, mash$norm, cfg), - samplePartition = mash$samplePartition, - dataDrivenPriorMatricesCv = mash$dataDrivenPriorMatricesCv + list(parsedJointSpec = parsedJointSpec, norm = norm) +} + +# The individual-level settings a TWAS run fits under, as one record: built +# once by the QtlDataset method and once by the MultiStudyQtlDataset method, +# with the same builder, so a setting added for one reaches the other. +# @noRd +.twasJointCfg <- function( + contexts, + traitId, + cisWindow, + xRegions, + fineMappingResult, + mashPrior, + crossValidationParam, + ensembleParam, + estimatePi, + fitRetention, + residualizationParam, + weightsDataType, + verbose, + seed +) { + list( + contexts = contexts, + traitId = traitId, + cisWindow = cisWindow, + xRegions = xRegions, + fineMappingResult = fineMappingResult, + mashPrior = mashPrior, + crossValidationParam = crossValidationParam, + ensembleParam = ensembleParam, + estimatePi = estimatePi, + fitRetention = fitRetention, + residualizationParam = residualizationParam, + weightsDataType = weightsDataType, + verbose = verbose, + seed = seed + ) +} + +# Everything an individual-level TWAS fit on `data` needs from the record +# `cfg` (.twasJointCfg()): the mash prior unpacked into the methods, the +# pipeline marker, and the engine-args context. Used for the QtlDataset +# method's own run and for each MultiStudyQtlDataset component's joint fits. +# @noRd +.twasJointSetup <- function(data, norm, cfg) { + cvCfg <- .cvResolve(cfg$crossValidationParam) + mash <- .twasQdsUnpackMash(cfg$mashPrior, cvCfg$samplePartition, norm) + marker <- .twasQdsMarker( + crossValidationParam = list_assign( + cvCfg, + samplePartition = mash$samplePartition + ), + ensembleParam = .ensembleResolve(cfg$ensembleParam), + weightsDataType = cfg$weightsDataType, + standardized = .qtlScaleResiduals(data, cfg$residualizationParam), + fitRetention = cfg$fitRetention, + estimatePi = cfg$estimatePi, + verbose = cfg$verbose, + seed = cfg$seed + ) + list( + marker = marker, + ctx = list( + data = data, + xRegions = cfg$xRegions, + norm = mash$norm, + fineMappingResult = cfg$fineMappingResult, + dataDrivenPriorMatricesCv = mash$dataDrivenPriorMatricesCv, + cisWindow = cfg$cisWindow, + residualizationParam = cfg$residualizationParam, + verbose = cfg$verbose + ) ) } @@ -1770,26 +1843,18 @@ setMethod( study = studyName, useCtx = useCtx, allTraits = allTraits, + # Set only when traits are chosen context by context: the grid spans + # every (context, trait) pair of allTraits, and this drops the pairs + # the selection leaves out. + traitSelection = if (.hasNestedTraitSelection(traitId)) traitId, multivariate = any(map_lgl(tokens, .twasIsMultivariateToken)) ) } -# Selected contexts (all when NULL; else validated against the dataset). +# Selected contexts: those parseContexts() kept for this study, or all. # @noRd .twasQdsResolveContexts <- function(data, contexts) { - allCtx <- contexts(data) - if (is.null(contexts)) { - return(allCtx) - } - bad <- setdiff(contexts, allCtx) - if (length(bad) > 0L) { - badStr <- str_flatten(bad, ", ") - msg <- glue( - "twasWeightsPipeline(QtlDataset): unknown context(s): {badStr}" - ) - abort(msg) - } - contexts + .selectedContexts(contexts, studyName(data)) %||% contexts(data) } # Traits to iterate: traitId when supplied, else per-context region overlap, @@ -1813,9 +1878,8 @@ setMethod( # Joint-pipeline marker carrying the CV / ensemble config for the engine. # @noRd .twasQdsMarker <- function( - crossValidationArgs, - ensembleArgs, - fitFullData, + crossValidationParam, + ensembleParam, weightsDataType, standardized, fitRetention, @@ -1829,9 +1893,8 @@ setMethod( # The two groups travel as records, not as nine loose fields: # this function's only job is to store them, so unrolling them # into its signature would name each one twice for nothing. - crossValidationArgs = crossValidationArgs, - ensembleArgs = ensembleArgs, - fitFullData = fitFullData, + crossValidationParam = crossValidationParam, + ensembleParam = ensembleParam, dataType = weightsDataType, fitRetention = fitRetention, # DERIVED from what the accessors actually do: genotypes reach @@ -1843,7 +1906,7 @@ setMethod( estimatePi = estimatePi, verbose = verbose, seed = seed, - ldSketch = NULL + ldReference = NULL ) ) } @@ -1852,7 +1915,13 @@ setMethod( # adds top-PC rows ALONGSIDE the per-trait ones, matching # fineMappingPipeline's c(univRows, pcaRows). # @noRd -.twasQdsUnivPlan <- function(usePCA, studyName, useCtx, allTraits) { +.twasQdsUnivPlan <- function( + usePCA, + studyName, + useCtx, + allTraits, + traitSelection +) { univCell <- .lookupJointCell("univariate", "individual") list( cells = if (isTRUE(usePCA)) { @@ -1863,7 +1932,8 @@ setMethod( scope = list( studies = studyName, contexts = set_names(list(useCtx), studyName), - traits = set_names(list(allTraits), studyName) + traits = set_names(list(allTraits), studyName), + traitSelection = traitSelection ) ) } @@ -1876,6 +1946,7 @@ setMethod( studyName, useCtx, allTraits, + traitSelection, marker, data, xRegions, @@ -1885,12 +1956,18 @@ setMethod( dataDrivenPriorMatricesCv, cisWindow, naAction, - residualizationArgs, + residualizationParam, usePCA, nPCs, verbose ) { - plan <- .twasQdsUnivPlan(usePCA, studyName, useCtx, allTraits) + plan <- .twasQdsUnivPlan( + usePCA, + studyName, + useCtx, + allTraits, + traitSelection + ) cells <- plan$cells scope <- plan$scope labs <- map_chr(xRegions, .twasRegionLabel) @@ -1908,7 +1985,7 @@ setMethod( dataDrivenPriorMatricesCv = dataDrivenPriorMatricesCv, cisWindow = cisWindow, naAction = naAction, - residualizationArgs = residualizationArgs, + residualizationParam = residualizationParam, nPCs = nPCs, verbose = verbose ) @@ -1927,7 +2004,7 @@ setMethod( dataDrivenPriorMatricesCv, cisWindow, naAction, - residualizationArgs, + residualizationParam, nPCs, verbose ) { @@ -1941,7 +2018,7 @@ setMethod( regionIndex = bi, nRegions = length(xRegions), naAction = naAction, - residualizationArgs = residualizationArgs, + residualizationParam = residualizationParam, nPCs = nPCs, verbose = verbose ) @@ -1975,7 +2052,7 @@ setMethod( if (is.null(jointResult)) { return(tw) } - .rbindTwasWeights(tw, jointResult, ldSketch = NULL) + .rbindTwasWeights(tw, jointResult, ldReference = NULL) } #' @rdname twasWeightsPipeline @@ -1995,12 +2072,12 @@ setMethod( weightsDataType = NULL, verbose = 1L, panelFilterParam = PanelFilterParam(), - crossValidationArgs = CrossValidationParam() + crossValidationParam = CrossValidationParam() ) { fitRetention <- arg_match(fitRetention) - .twasWarnUnretainableDetail(fitRetention, methods) + .twasWarnUnretainableDetail(fitRetention, methods, "QtlSumStats") .cvRefuseOnSumstats( - crossValidationArgs, + crossValidationParam, "twasWeightsPipeline", "QtlSumStats" ) @@ -2032,12 +2109,10 @@ setMethod( studyCol = part$studyCol, contextCol = part$contextCol, traitCol = part$traitCol, - ldSketch = part$ldSketch, + ldReference = part$ldReference, methodArgs = methodArgs ) c( - # The univariate builder keeps whatever the per-method fit returns, - # so it takes no fitRetention. exec( .twasQssUnivariateRows, !!!rowArgs, @@ -2045,6 +2120,7 @@ setMethod( data = cfg$data, twasWeights = cfg$twasWeights, weightsDataType = cfg$dataType, + fitRetention = cfg$fitRetention, fineMappingResult = cfg$fineMappingResult, panelFilterParam = cfg$panelFilterParam ), @@ -2079,17 +2155,16 @@ setMethod( verbose, panelFilterParam ) { - # summaryStatsQc() is mandatory before twasWeightsPipeline for SumStats - # input; it also drops variants not present in the ldSketch, so every - # entry's SNP set is a subset of the ldSketch panel by the time we get here. .twasAssertQcd(data) + contexts <- parseContexts(contexts, data) + traitId <- parseTraitIds(traitId, data) # One record of the settings the joint phase and the shared dispatcher # both need, so each names them once. jointCfg <- list( data = data, contexts = contexts, traitId = traitId, - dataType = weightsDataType, + weightsDataType = weightsDataType, verbose = verbose, fitRetention = fitRetention, panelFilterParam = panelFilterParam @@ -2149,12 +2224,12 @@ setMethod( panelFilterParam = panelFilterParam ) ) - .twasQssAssemble(rows, joint$result, part$ldSketch) + .twasQssAssemble(rows, joint$result, part$ldReference) } # Normalize the methods argument into (tokens, methodArgs). The default set # excludes fine-mapping methods; those must be requested explicitly together -# with a FineMappingResult passed via `fineMappingResult`. +# with a fine-mapping result passed via `fineMappingResult`. # @noRd .twasSumStatsMethodTokens <- function(methods) { if (is.null(methods)) { @@ -2199,21 +2274,6 @@ setMethod( set_names(rep(list(list()), length(tokens)), tokens) } -# Run the joint engine for a QtlSumStats with the given spec + token set. -# @noRd -.twasQssJointDispatch <- function(jointSpec, tokens, cfg) { - .twasDispatchJointSpecsQtlSumStats( - jointSpec, - cfg$data, - tokens, - cfg$contexts, - cfg$traitId, - cfg$dataType, - cfg$verbose, - fitRetention = cfg$fitRetention, - panelFilterParam = cfg$panelFilterParam %||% PanelFilterParam() - ) -} # Joint-specification dispatch for mrmash. Returns list(done, result, tokens, # methodArgs): `done` requests an early return with `result`; otherwise the @@ -2228,9 +2288,10 @@ setMethod( methodArgs = methodArgs )) } - jointResult <- .twasQssJointDispatch( + jointResult <- .twasDispatchJointSpecsQtlSumStats( parsedJointSpec, - intersect(tokens, "mrmash"), + tokens, + methodArgs, cfg ) keep <- setdiff(tokens, "mrmash") @@ -2273,13 +2334,7 @@ setMethod( studyCol <- as.character(data$study) contextCol <- as.character(data$context) traitCol <- as.character(data$trait) - selRows <- .twasQssSelectRows( - data, - contextCol, - traitCol, - contexts, - traitId - ) + selRows <- .twasQssSelectRows(data, contexts, traitId) isMv <- map_lgl(tokens, .twasIsMultivariateToken) multivariateTokens <- tokens[isMv] univariateTokens <- tokens[!isMv] @@ -2297,29 +2352,14 @@ setMethod( selRows = selRows, multivariateTokens = multivariateTokens, univariateTokens = univariateTokens, - ldSketch = ldSketch(data) + ldReference = ldReference(data) ) } # Row indices matching the contexts / traitId filters (error if none). # @noRd -.twasQssSelectRows <- function( - data, - contextCol, - traitCol, - contexts, - traitId -) { - byContext <- if (is.null(contexts)) { - seq_len(nrow(data)) - } else { - which(is_in(contextCol, contexts)) - } - selRows <- if (is.null(traitId)) { - byContext - } else { - byContext[is_in(traitCol[byContext], traitId)] - } +.twasQssSelectRows <- function(data, contexts, traitId) { + selRows <- which(.sumStatsRowsSelected(data, contexts, traitId)) if (length(selRows) == 0L) { msg <- glue( "twasWeightsPipeline(QtlSumStats): no entries matched the ", @@ -2376,7 +2416,7 @@ setMethod( # Assemble a TwasWeights collection from a flat list of row records. # @noRd -.twasRowsToWeights <- function(rows, ldSketch) { +.twasRowsToWeights <- function(rows, ldReference) { if (length(rows) == 0L) { return(NULL) } @@ -2386,7 +2426,7 @@ setMethod( trait = map_chr(rows, "trait"), method = map_chr(rows, "method"), entry = map(rows, "entry"), - ldSketch = ldSketch + ldReference = ldReference ) } @@ -2425,16 +2465,29 @@ setMethod( ) } +# The pipeline's fitRetention for a summary-statistics weight function that +# takes one, unless the caller set it for that method; a function without the +# argument keeps no fit. "none" is passed on too, unlike on the +# individual-level path: the fine-mapping adapters here keep a slim fit +# unless told otherwise, so leaving it unset would not drop it. +# @noRd +.twasQssRetainArgs <- function(args, fnName, fitRetention) { + if (!is_in("fitRetention", names(formals(fnName)))) { + return(args) + } + list_assign(args, fitRetention = args$fitRetention %||% fitRetention) +} + # Run a weight function, warning (with `errPrefix`) and returning NULL on error. # @noRd #' @importFrom rlang try_fetch -.twasTryWeights <- function(fn, stat, ldMat, userArgs, errPrefix) { +.twasTryWeights <- function(fn, stat, ldReference, userArgs, errPrefix) { try_fetch( { wfn <- get(fn, mode = "function") wArgs <- .twasWeightCallArgs( fn, - list(stat = stat, LD = ldMat), + list(stat = stat, ldReference = ldReference), userArgs ) exec(wfn, !!!wArgs) @@ -2455,9 +2508,10 @@ setMethod( contextCol, traitCol, data, - ldSketch, + ldReference, twasWeights, weightsDataType, + fitRetention, fineMappingResult, methodArgs, panelFilterParam @@ -2473,9 +2527,10 @@ setMethod( contextCol = contextCol, traitCol = traitCol, data = data, - ldSketch = ldSketch, + ldReference = ldReference, twasWeights = twasWeights, weightsDataType = weightsDataType, + fitRetention = fitRetention, fineMappingResult = fineMappingResult, methodArgs = methodArgs, panelFilterParam = panelFilterParam @@ -2515,9 +2570,10 @@ setMethod( contextCol, traitCol, data, - ldSketch, + ldReference, twasWeights, weightsDataType, + fitRetention, fineMappingResult, methodArgs, panelFilterParam @@ -2536,7 +2592,7 @@ setMethod( st, ctx, tr, - ldSketch, + ldReference, cutoffs = .panelCutoffs(panelFilterParam) ) fitted <- compact(map( @@ -2547,6 +2603,7 @@ setMethod( tr = tr, fitCtx = fitCtx, methodArgs = methodArgs, + fitRetention = fitRetention, fineMappingResult = fineMappingResult, weightsDataType = weightsDataType )) @@ -2613,7 +2670,7 @@ setMethod( st, ctx, tr, - ldSketch, + ldReference, cutoffs = NULL ) { allDf <- as.data.frame( @@ -2632,7 +2689,7 @@ setMethod( "context='{ctx}', trait='{tr}'" ) df <- allDf[ - .panelKeepMask(allDf$variant_id, ldSketch, cutoffs, label), + .panelKeepMask(allDf$variant_id, ldReference, cutoffs, label), , drop = FALSE ] @@ -2644,19 +2701,19 @@ setMethod( varY = varY, variantNames = variantIds ) + # The entry's block of the reference, read once and shared by every + # method fitted to it. list( variantIds = variantIds, stat = stat, - ldMat = .ldFromSketch( - ldSketch, + ldReference = .ldRefInMemory( + ldReference, variantIds, label = "twasWeightsPipeline" ) ) } -# Fit one univariate method for one entry -> a row record, or NULL on skip. -# @noRd # The weight function's arguments for one tuple: the user's args and, when the # token is a fine-mapping method, the precomputed fit threaded into its # dedicated *Fit argument. NULL when such a token has no fit to thread. @@ -2688,6 +2745,8 @@ setMethod( list_assign(baseArgs, !!!set_names(list(fit), spec$adapter$rssFitArg)) } +# Fit one univariate method for one entry -> a row record, or NULL on skip. +# @noRd .twasQssUnivariateFitOne <- function( tk, st, @@ -2695,6 +2754,7 @@ setMethod( tr, fitCtx, methodArgs, + fitRetention, fineMappingResult, weightsDataType ) { @@ -2716,8 +2776,8 @@ setMethod( weights <- .twasTryWeights( spec$fn, fitCtx$stat, - fitCtx$ldMat, - userArgs, + fitCtx$ldReference, + .twasQssRetainArgs(userArgs, spec$fn, fitRetention), .twasFitErrUniv(tk, st, ctx, tr) ) if (is.null(weights)) { @@ -2783,7 +2843,7 @@ setMethod( contextCol, traitCol, data, - ldSketch, + ldReference, methodArgs, fitRetention, fineMappingResult, @@ -2809,7 +2869,7 @@ setMethod( contextCol = contextCol, traitCol = traitCol, data = data, - ldSketch = ldSketch, + ldReference = ldReference, methodArgs = methodArgs, fitRetention = fitRetention, fineMappingResult = fineMappingResult, @@ -2827,7 +2887,7 @@ setMethod( contextCol, traitCol, data, - ldSketch, + ldReference, methodArgs, fitRetention, fineMappingResult, @@ -2846,11 +2906,12 @@ setMethod( st, tr, ctxNames, - ldSketch = ldSketch, + ldReference = ldReference, cutoffs = .panelCutoffs(panelFilterParam) ) - ldMat <- .ldFromSketch( - ldSketch, + # The group's block of the reference, read once and shared by every method. + blockReference <- .ldRefInMemory( + ldReference, mvStat$variantIds, label = "twasWeightsPipeline" ) @@ -2862,7 +2923,7 @@ setMethod( tr = tr, ctxNames = ctxNames, mvStat = mvStat, - ldMat = ldMat, + blockReference = blockReference, methodArgs = methodArgs, fitRetention = fitRetention, fineMappingResult = fineMappingResult, @@ -2879,7 +2940,7 @@ setMethod( st, tr, ctxNames, - ldSketch = NULL, + ldReference = NULL, cutoffs = NULL ) { allDf <- as.data.frame( @@ -2897,7 +2958,7 @@ setMethod( "twasWeightsPipeline(QtlSumStats, multivariate): study='{st}', ", "trait='{tr}'" ) - keep <- .panelKeepMask(allDf$variant_id, ldSketch, cutoffs, label) + keep <- .panelKeepMask(allDf$variant_id, ldReference, cutoffs, label) firstDf <- allDf[keep, , drop = FALSE] variantIds <- firstDf$variant_id filled <- .twasQssFillContexts(data, st, tr, ctxNames, variantIds) @@ -2911,11 +2972,6 @@ setMethod( ) } -# Read each context's z / N into the shared matrix. Every context must present -# the same SNPs in the same order -- the multivariate fitters index z by -# position, not by name, so a differing order would silently pair one context's -# variant with another's. -# @noRd # One context's z column and median N, checked against the shared SNP order. # @noRd .twasQssContextStats <- function(ctx, data, st, tr, variantIds) { @@ -2932,9 +2988,15 @@ setMethod( list(z = kept$z, n = stats::median(kept$N, na.rm = TRUE)) } +# Read each context's z / N into the shared matrix. Every context must present +# the same SNPs in the same order -- the multivariate fitters index z by +# position, not by name, so a differing order would silently pair one context's +# variant with another's. +# # Every context shares one SNP order (asserted per context), which is what # lets the columns simply be laid side by side rather than filled into a # preallocated matrix. +# @noRd .twasQssFillContexts <- function(data, st, tr, ctxNames, variantIds) { stats <- map( ctxNames, @@ -2964,7 +3026,7 @@ setMethod( "twasWeightsPipeline(QtlSumStats, multivariate): every ", "entry for (study='{st}', trait='{tr}') must share an ", "identical SNP order after summaryStatsQc(). Use the same ", - "ldSketch on every entry." + "ldReference on every entry." )) } @@ -2982,10 +3044,14 @@ setMethod( fitRetention, fineMappingResult ) { - baseArgs <- .twasMrmashRetainDefaults( - .twasUserArgs(methodArgs, tk), - spec$adapter, - tk, + baseArgs <- .twasQssRetainArgs( + .twasMrmashRetainDefaults( + .twasUserArgs(methodArgs, tk), + spec$adapter, + tk, + fitRetention + ), + spec$fn, fitRetention ) if (is.null(spec$adapter)) { @@ -3003,7 +3069,8 @@ setMethod( } # Fit one multivariate method for a group -> one row record per context (empty -# list on skip). +# list on skip). `userArgs` is NULL when the pre-fit a fine-mapping token needs +# is missing. # @noRd .twasQssMultivariateFitOne <- function( tk, @@ -3011,7 +3078,7 @@ setMethod( tr, ctxNames, mvStat, - ldMat, + blockReference, methodArgs, fitRetention, fineMappingResult, @@ -3033,22 +3100,43 @@ setMethod( fitRetention = fitRetention, fineMappingResult = fineMappingResult ) - # NULL when the pre-fit a fine-mapping token needs is missing. if (is.null(userArgs)) { return(list()) } weights <- .twasTryWeights( spec$fn, mvStat$stat, - ldMat, + blockReference, userArgs, .twasFitErrMv(tk, st, tr) ) + .twasMvRowsFromWeights( + weights, + ctxNames, + mvStat, + st, + tr, + tk, + weightsDataType + ) +} + +# Per-context row records from one multivariate fit's weights (empty list when +# the fit failed and `weights` is NULL). The fit is hung off the weights as an +# attribute; the rows store the two separately. +# @noRd +.twasMvRowsFromWeights <- function( + weights, + ctxNames, + mvStat, + st, + tr, + tk, + weightsDataType +) { if (is.null(weights)) { return(list()) } - # The fit is hung off the weights as an attribute; the rows store the - # two separately. wMatrix <- if (is.matrix(weights)) weights else as.matrix(weights) fitAttr <- attr(wMatrix, "fit") bare <- `attr<-`(wMatrix, "fit", NULL) @@ -3143,9 +3231,9 @@ setMethod( .twasQssAssemble <- function( rows, jointResult, - ldSketch + ldReference ) { - perTupleResult <- .twasRowsToWeights(rows, ldSketch) + perTupleResult <- .twasRowsToWeights(rows, ldReference) if (is.null(jointResult)) { if (is.null(perTupleResult)) { msg <- glue( @@ -3159,7 +3247,7 @@ setMethod( if (is.null(perTupleResult)) { return(jointResult) } - .rbindTwasWeights(perTupleResult, jointResult, ldSketch = ldSketch) + .rbindTwasWeights(perTupleResult, jointResult, ldReference = ldReference) } # ============================================================================= @@ -3174,24 +3262,28 @@ setMethod( # Per-embedded-study TWAS-weights worker for .multiStudyPipelineDriver: recurse # twasWeightsPipeline on one QtlDataset. `cfg` bundles the parent's forwarded # args. +# +# Every setting is named -- not the `...`/dotArgs channel this once spliced, +# which is what made multi-study TWAS inherit the per-study CV default and +# silently drop residualization / fitRetention. A name this entry point does +# not accept is an "unused argument" error; `cfg`'s panelFilterParam belongs +# to the summary-statistics path and is deliberately not forwarded. # @noRd .twasPerStudy <- function(qd, cfg) { - # Checked forwarding, not the `...`/dotArgs channel this used to splice: - # that is what made multi-study TWAS inherit the per-study CV default and - # silently drop residualization / fitRetention. - # Every setting is named -- NOT the `...`/dotArgs channel this once - # spliced, which is what made multi-study TWAS inherit the per-study CV - # default and silently drop residualization / fitRetention. A name this - # entry point does not accept is an "unused argument" error; `cfg`'s - # panelFilterArgs belongs to the summary-statistics path and is - # deliberately not forwarded. + # `cfg` carries the parsed selection for every study; this study gets its + # own share, and a study with none of its contexts selected sits out. + s <- studyName(qd) + contexts <- .selectedContexts(cfg$contexts, s) + if (!is.null(contexts) && length(contexts) == 0L) { + return(NULL) + } twasWeightsPipeline( data = qd, jointSpecification = NULL, - fitRetention = .twasRetentionEnum(cfg), + fitRetention = cfg$fitRetention, methods = cfg$methods, - contexts = cfg$contexts, - traitId = cfg$traitId, + contexts = contexts, + traitId = .studyTraitIdArg(cfg$traitId, s), region = cfg$region, cisWindow = cfg$cisWindow, jointRegions = cfg$jointRegions, @@ -3199,15 +3291,16 @@ setMethod( twasWeights = cfg$twasWeights, naAction = cfg$naAction, verbose = cfg$verbose, - crossValidationArgs = cfg$crossValidationArgs, - ensembleArgs = cfg$ensembleArgs, + crossValidationParam = cfg$crossValidationParam, + ensembleParam = cfg$ensembleParam, seed = cfg$seed, genotypeFilterParam = cfg$genotypeFilterParam, mashPrior = cfg$mashPrior, - fitFullData = cfg$fitFullData, estimatePi = cfg$estimatePi, - weightsDataType = cfg$dataType, - residualizationArgs = cfg$residualizationArgs + usePCA = cfg$usePCA, + nPCs = cfg$nPCs, + weightsDataType = cfg$weightsDataType, + residualizationParam = cfg$residualizationParam ) } @@ -3218,37 +3311,36 @@ setMethod( # from, residualize, cross-validate, stack or fit on full data. `cfg` # carries those for the individual-level sibling and they are not # forwarded here. + # + # The parsed selection is applied here, to the rows, so the recursion runs + # on what it keeps with nothing left to filter -- and rows it leaves out + # are not an error when another component holds the selected tuples. + keepRows <- .sumStatsRowsSelected(ss, cfg$contexts, cfg$traitId) + if (!any(keepRows)) { + return(NULL) + } twasWeightsPipeline( - data = ss, + data = if (all(keepRows)) ss else ss[which(keepRows)], jointSpecification = NULL, - fitRetention = .twasRetentionEnum(cfg), + fitRetention = cfg$fitRetention, methods = cfg$methods, - contexts = cfg$contexts, - traitId = cfg$traitId, + contexts = NULL, + traitId = NULL, fineMappingResult = cfg$fineMappingResult, twasWeights = cfg$twasWeights, verbose = cfg$verbose, panelFilterParam = cfg$panelFilterParam, - weightsDataType = cfg$dataType + weightsDataType = cfg$weightsDataType ) } -# The config carries the engine-facing pair; the per-study recursion re-enters -# the public method, which takes the single enum. One place converts back. +# Few methods distinguish the retention levels (.twasFullDetailTokens()); +# every other weight method keeps its fit whole or not at all, so "full" is +# the same as "slim" for them. Erroring when NO requested method can honour +# it, and warning when only some can, is the alternative to dropping it in +# silence. Which methods can depends on the input the run reads. # @noRd -.twasRetentionEnum <- function(cfg) { - if (!isTRUE(cfg$fitRetention)) { - return("none") - } - cfg$fitRetention %||% "slim" -} - -# Only the mr.mash engines distinguish the retention levels; every other -# weight method keeps its fit whole or not at all, so "full" is the same as -# "slim" for them. Erroring when NO requested method can honour it, and -# warning when only some can, is the alternative to dropping it in silence. -# @noRd -.twasWarnUnretainableDetail <- function(fitRetention, methods) { +.twasWarnUnretainableDetail <- function(fitRetention, methods, inputKind) { if (!identical(fitRetention, "full")) { return(invisible(NULL)) } @@ -3256,12 +3348,14 @@ setMethod( if (length(tokens) == 0L) { return(invisible(NULL)) } - honours <- str_detect(tokens, regex("mrmash", ignore_case = TRUE)) + honouring <- .twasFullDetailTokens(inputKind) + honours <- is_in(tokens, honouring) if (!any(honours)) { abort(glue( - "twasWeightsPipeline: fitRetention = \"full\" is only honoured ", - "by the mr.mash methods; {str_flatten(tokens, ', ')} cannot vary ", - "the retained detail. Use fitRetention = \"slim\"." + "twasWeightsPipeline: fitRetention = \"full\" is honoured on ", + "{inputKind} input only by {str_flatten(honouring, ', ')}; ", + "{str_flatten(tokens, ', ')} cannot vary the retained detail. ", + "Use fitRetention = \"slim\"." )) } if (!all(honours)) { @@ -3274,6 +3368,18 @@ setMethod( invisible(NULL) } +# The methods whose "full" fit keeps more than their "slim" one, on one input +# path: mr.mash's whole fit, and on summary statistics the shrinkage-grid +# methods' candidate coefficients (lassosumRssWeights() and the penalized +# RSS weights). +# @noRd +.twasFullDetailTokens <- function(inputKind) { + if (identical(inputKind, "QtlSumStats")) { + return(c("mrmash", "lasso", "scad", "mcp", "l0learn")) + } + "mrmash" +} + # ---- MultiStudyQtlDataset pipeline worker + phase helpers ------------------ # Method tokens from a `methods` arg: a character vector as-is; a named list -> @@ -3293,8 +3399,6 @@ setMethod( } } -# Drop mrmash (handled by the joint dispatcher) from a `methods` arg. -# @noRd # A methods argument as an ordinary named list, whatever form it arrived in. # A MethodOptions record is a SimpleList, so `is.list()` is FALSE for it and the # helpers below would otherwise fall through to their "leave it alone" branch. @@ -3312,6 +3416,8 @@ setMethod( is(methods, "MethodsSelectionParam") } +# Drop mrmash (handled by the joint dispatcher) from a `methods` arg. +# @noRd .twasMsStripMrmash <- function(methods) { if (.twasMethodsIsParam(methods)) { return(.methodsParamDrop(methods, "mrmash")) @@ -3339,25 +3445,11 @@ setMethod( (is.character(methods) || is.list(methods)) && length(methods) == 0L } -# Resolve the MultiStudyQtlDataset inputs: the region/cisWindow guard, the -# naAction choice and the joint-region spec. Mirrors .fmMsResolveInputs so -# both MultiStudy workers read the same way, rather than one open-coding -# what the other names. +# The MultiStudyQtlDataset method of twasWeightsPipeline(). `jointCfg` is the +# QtlDataset method's own record (.twasJointCfg()) plus the collection and the +# two filter settings the components apply, so each component's joint fits +# run under the settings its own QtlDataset run would use. # @noRd -.twasMsResolveInputs <- function(naAction, region, cisWindow, jointRegions) { - if (!is.null(region) && !is.null(cisWindow)) { - msg <- glue( - "twasWeightsPipeline(MultiStudyQtlDataset): specify either ", - "`region` or `cisWindow`, not both." - ) - abort(msg) - } - list( - naAction = arg_match(naAction, c("drop", "impute")), - xRegions = .makeXRegions(region, jointRegions) - ) -} - .twasPipelineMultiStudy <- function( data, methods = "default", @@ -3372,33 +3464,55 @@ setMethod( fineMappingResult = NULL, twasWeights = NULL, mashPrior = NULL, - fitFullData = TRUE, estimatePi = TRUE, + usePCA = FALSE, + nPCs = 10L, fitRetention = c("slim", "none", "full"), weightsDataType = NULL, naAction = c("drop", "impute"), verbose = 1, - residualizationArgs = ResidualizationParam(), - crossValidationArgs = CrossValidationParam(), - ensembleArgs = EnsembleParam(), + residualizationParam = ResidualizationParam(), + crossValidationParam = CrossValidationParam(), + ensembleParam = EnsembleParam(), seed = NULL ) { fitRetention <- arg_match(fitRetention) - .twasWarnUnretainableDetail(fitRetention, methods) - inputs <- .twasMsResolveInputs(naAction, region, cisWindow, jointRegions) + # Every collection holds individual-level studies, so their rule applies + # here; a summary-statistic study is checked again by its own route. + .twasWarnUnretainableDetail(fitRetention, methods, "QtlDataset") + inputs <- .msResolveInputs( + naAction, + region, + cisWindow, + jointRegions, + "twasWeightsPipeline(MultiStudyQtlDataset)" + ) naAction <- inputs$naAction - # One record of the settings the joint phase and the shared dispatcher - # both need, so each names them once. - jointCfg <- list( - data = data, - contexts = contexts, - traitId = traitId, + cfg <- .twasMsCfg( + methods = methods, + contexts = parseContexts(contexts, data), + traitId = parseTraitIds(traitId, data), + region = region, cisWindow = cisWindow, + jointRegions = jointRegions, + fineMappingResult = fineMappingResult, + twasWeights = twasWeights, + naAction = naAction, verbose = verbose, - xRegions = inputs$xRegions, + crossValidationParam = crossValidationParam, + ensembleParam = ensembleParam, + seed = seed, + genotypeFilterParam = genotypeFilterParam, + panelFilterParam = panelFilterParam, + mashPrior = mashPrior, + estimatePi = estimatePi, + usePCA = usePCA, + nPCs = nPCs, fitRetention = fitRetention, - seed = seed + weightsDataType = weightsDataType, + residualizationParam = residualizationParam ) + jointCfg <- .twasMsJointCfg(cfg, data, inputs$xRegions) joint <- .twasMsResolveTokens( jointSpecification, methods, @@ -3408,30 +3522,37 @@ setMethod( if (joint$done) { return(joint$result) } - .twasMsDriver( + .twasMsDriver(data, joint$result, list_assign(cfg, methods = joint$methods)) +} + +# The MultiStudyQtlDataset joint record, derived from the record the +# per-component recursion receives (`cfg`, .twasMsCfg()): the settings each +# component's own QtlDataset run fits under (.twasJointCfg()), plus the +# collection and the two filters the components apply. Deriving it, rather +# than re-listing the settings, keeps the per-study joint fits on exactly the +# settings of the per-study fits. +# @noRd +.twasMsJointCfg <- function(cfg, data, xRegions) { + list_assign( + .twasJointCfg( + contexts = cfg$contexts, + traitId = cfg$traitId, + cisWindow = cfg$cisWindow, + xRegions = xRegions, + fineMappingResult = cfg$fineMappingResult, + mashPrior = cfg$mashPrior, + crossValidationParam = cfg$crossValidationParam, + ensembleParam = cfg$ensembleParam, + estimatePi = cfg$estimatePi, + fitRetention = cfg$fitRetention, + residualizationParam = cfg$residualizationParam, + weightsDataType = cfg$weightsDataType, + verbose = cfg$verbose, + seed = cfg$seed + ), data = data, - contexts = contexts, - traitId = traitId, - cisWindow = cisWindow, - region = region, - jointRegions = jointRegions, - fineMappingResult = fineMappingResult, - twasWeights = twasWeights, - naAction = naAction, - verbose = verbose, - crossValidationArgs = crossValidationArgs, - ensembleArgs = ensembleArgs, - seed = seed, - genotypeFilterParam = genotypeFilterParam, - panelFilterParam = panelFilterParam, - mashPrior = mashPrior, - fitFullData = fitFullData, - estimatePi = estimatePi, - fitRetention = fitRetention, - weightsDataType = weightsDataType, - residualizationArgs = residualizationArgs, - joint$result, - joint$methods + genotypeFilterParam = cfg$genotypeFilterParam, + panelFilterParam = cfg$panelFilterParam ) } @@ -3443,25 +3564,6 @@ setMethod( .twasPipelineMultiStudy ) -# Run the joint engine for a MultiStudyQtlDataset with the given spec + token -# set. The NULL in the dataType slot is deliberate: the per-component -# recursion resolves each study's own data type. -# @noRd -.twasMsJointDispatch <- function(jointSpec, tokens, cfg) { - .twasDispatchJointSpecsMultiStudy( - jointSpec, - cfg$data, - tokens, - cfg$contexts, - cfg$traitId, - cfg$cisWindow, - NULL, - cfg$verbose, - xRegions = cfg$xRegions, - fitRetention = cfg$fitRetention, - seed = cfg$seed - ) -} # Joint-specification dispatch for mrmash. Returns list(done, result, methods) # where `done` requests an early return with `result` and `methods` is the @@ -3477,9 +3579,10 @@ setMethod( ) return(list(done = FALSE, result = NULL, methods = methods)) } - jointResult <- .twasMsJointDispatch( + jointResult <- .twasDispatchJointSpecsMultiStudy( parsedJointSpec, - intersect(.twasMethodTokensFromArg(methods), "mrmash"), + .twasMethodTokensFromArg(methods), + methods, cfg ) stripped <- .twasMsStripMrmash(methods) @@ -3525,39 +3628,59 @@ setMethod( .twasCheckFineMappingMethods( .twasMethodTokensFromArg(methods), fineMappingResult, - "MultiStudyQtlDataset" + "MultiStudyQtlDataset", + fitFullData = .cvResolve(cfg$crossValidationParam)$fitFullData ) .twasMsJointPhase(parsedJointSpec, methods, cfg) } # Run the per-study / per-component recursion via the shared multi-study driver. +# `cfg` is the `.twasMsCfg()` record of the settings each component re-runs +# with. # @noRd -.twasMsDriver <- function( - data, +.twasMsDriver <- function(data, jointResult, cfg) { + .multiStudyPipelineDriver( + data, + jointResult, + .twasPerStudy, + .twasSumStats, + cfg, + .rbindTwasWeights, + TwasWeights, + "twasWeightsPipeline", + noun = "weights" + ) +} + +# The settings the per-study / per-component recursion re-runs each component +# with, as the record .multiStudyPipelineDriver() hands to .twasPerStudy / +# .twasSumStats. +# @noRd +.twasMsCfg <- function( + methods, contexts, traitId, - cisWindow, region, + cisWindow, jointRegions, fineMappingResult, twasWeights, naAction, verbose, - crossValidationArgs, - ensembleArgs, + crossValidationParam, + ensembleParam, seed, genotypeFilterParam, panelFilterParam, mashPrior, - fitFullData, estimatePi, + usePCA, + nPCs, fitRetention, weightsDataType, - residualizationArgs, - jointResult, - methods + residualizationParam ) { - cfg <- list( + list( methods = methods, contexts = contexts, traitId = traitId, @@ -3568,28 +3691,18 @@ setMethod( twasWeights = twasWeights, naAction = naAction, verbose = verbose, - crossValidationArgs = crossValidationArgs, - ensembleArgs = ensembleArgs, + crossValidationParam = crossValidationParam, + ensembleParam = ensembleParam, seed = seed, genotypeFilterParam = genotypeFilterParam, panelFilterParam = panelFilterParam, mashPrior = mashPrior, - fitFullData = fitFullData, estimatePi = estimatePi, + usePCA = usePCA, + nPCs = nPCs, fitRetention = fitRetention, - dataType = weightsDataType, - residualizationArgs = residualizationArgs - ) - .multiStudyPipelineDriver( - data, - jointResult, - .twasPerStudy, - .twasSumStats, - cfg, - .rbindTwasWeights, - TwasWeights, - "twasWeightsPipeline", - noun = "weights" + weightsDataType = weightsDataType, + residualizationParam = residualizationParam ) } @@ -3686,26 +3799,21 @@ setMethod( zetaValid / zetaSum } -# Ensemble stacking objective (sum of squared residuals). `...` absorbs the -# gradient's extra optim args (PtP, Pty). +# Ensemble stacking objective: the sum of squared residuals of the stacked +# prediction `Pvalid %*% z`. `problem` is the record .solveEnsembleLbfgsb() +# builds once -- the predictions, the outcome and their cross-products -- and +# hands optim() as its single extra argument, so the objective and its +# gradient read one shared definition, each taking the part it needs. # @noRd -.ensembleObj <- function(Pvalid, yObs) { - # Captured by name, and forced here so the closure holds values rather - # than promises into a frame that has already returned. - force(Pvalid) - force(yObs) - function(z) sum((yObs - Pvalid %*% z)^2) +.ensembleObj <- function(z, problem) { + sum((problem$yObs - problem$Pvalid %*% z)^2) } -# Gradient of the ensemble stacking objective, same construction. Building -# both as closures of exactly what each needs is what lets optim() be called -# with no `...`: previously it forwarded the union of both callbacks' -# arguments to both, and each absorbed the other's half in a `...` tail. +# Gradient of the stacking objective, 2 (P'P z - P'y), from the +# cross-products in `problem`. # @noRd -.ensembleGrad <- function(PtP, Pty) { - force(PtP) - force(Pty) - function(z) as.vector(2 * (PtP %*% z - Pty)) +.ensembleGrad <- function(z, problem) { + as.vector(2 * (problem$PtP %*% z - problem$Pty)) } # Solve ensemble stacking via L-BFGS-B (box-constrained optimization, then @@ -3717,14 +3825,18 @@ setMethod( # @return Normalized coefficient vector of length Kvalid. # @noRd .solveEnsembleLbfgsb <- function(Pvalid, yObs, Kvalid) { - PtP <- crossprod(Pvalid) - Pty <- as.vector(crossprod(Pvalid, yObs)) - + problem <- list( + Pvalid = Pvalid, + yObs = yObs, + PtP = crossprod(Pvalid), + Pty = as.vector(crossprod(Pvalid, yObs)) + ) fit <- try_fetch( optim( par = rep(1 / Kvalid, Kvalid), - fn = .ensembleObj(Pvalid, yObs), - gr = .ensembleGrad(PtP, Pty), + fn = .ensembleObj, + gr = .ensembleGrad, + problem = problem, method = "L-BFGS-B", lower = rep(0, Kvalid) ), @@ -4153,8 +4265,6 @@ ensembleWeights <- function( invisible(NULL) } -# Assemble one dataset's (samples x methods) prediction matrix. -# @noRd # One method's aligned prediction column for dataset `d`. # @noRd .ensemblePredColumn <- function(k, predsD, nm, aln, contextIndex, d) { @@ -4178,6 +4288,8 @@ ensembleWeights <- function( pCol } +# Assemble one dataset's (samples x methods) prediction matrix. +# @noRd .ensembleBuildPd <- function(predsD, nm, aln, contextIndex, d) { cols <- map( seq_along(nm$predNames), @@ -4324,8 +4436,6 @@ ensembleWeights <- function( if (is.matrix(w)) w else matrix(w, ncol = 1) } -# Zeta-weighted sum of the matched weight matrices; univariate -> named vector. -# @noRd # One method's zeta-scaled contribution, or NULL when its weight matrix does # not line up with the first one's shape. # @noRd @@ -4343,6 +4453,8 @@ ensembleWeights <- function( zeta[i] * wMat } +# Zeta-weighted sum of the matched weight matrices; univariate -> named vector. +# @noRd .ensembleAccumulateWeights <- function(wtList, wtKeys, matched, zeta) { firstWt <- .ensembleAsMatrix(wtList[[wtKeys[which(matched)[1]]]]) shape <- dim(firstWt) @@ -4400,33 +4512,6 @@ ensembleWeights <- function( ) } -# The entry in one region's collection matching a (study, context, trait, -# method) key, or NULL when that region lacks it. -# @noRd -.twasEntryMatchingKey <- function(tw, key) { - hit <- which( - as.character(tw$study) == key[[1L]] & - as.character(tw$context) == key[[2L]] & - as.character(tw$trait) == key[[3L]] & - as.character(tw$method) == key[[4L]] - ) - if (length(hit)) .twrRowParts(tw, hit[[1L]]) else NULL -} - -# The merged entry for base row `r`: gather that key's entry from every region -# and concatenate them. -# @noRd -.twasMergedEntryForRow <- function(r, base, twList, regionLabels) { - key <- c( - as.character(base$study[[r]]), - as.character(base$context[[r]]), - as.character(base$trait[[r]]), - as.character(base$method[[r]]) - ) - perRegion <- map(twList, .twasEntryMatchingKey, key = key) - .twasMergeRegionEntries(perRegion, regionLabels) -} - # Canonical method name for a snake_case token (identity when not in the table). # @noRd .twasCanonicalMethod <- function(s, snakeToCanonical) { @@ -4439,39 +4524,57 @@ ensembleWeights <- function( .twasCapabilityViolation(caps[[tk]], tk, inputKind) } -# One region's multivariate-grid joint fit (region `bi` of ctx$xRegions). +# One region block of .twasRunJointSpecGrid(). # @noRd -.twasMvGridRegion <- function(bi, synthSpec, marker, ctx, traits) { +.twasJointSpecRegion <- function( + bi, + spec, + tokens, + contexts, + traits, + marker, + ctx +) { .runJointSpecs( - synthSpec, + spec, ctx$data, dataForm = "individual", pipeline = marker, - jointMethods = ctx$norm$tokens, - contexts = ctx$useCtx, + jointMethods = tokens, + contexts = contexts, traitIds = traits, - args = list( - methodList = ctx$norm$methodList, - fineMappingResult = ctx$fineMappingResult, - dataDrivenPriorMatricesCv = ctx$dataDrivenPriorMatricesCv, - cisWindow = ctx$cisWindow, - region = ctx$xRegions[[bi]], - regionIndex = bi, - nRegions = length(ctx$xRegions), - residualizationArgs = ctx$residualizationArgs, - verbose = ctx$verbose - ) + args = .twasJointArgs(ctx, bi) + ) +} + +# The engine `args` of an individual-level TWAS joint run over region block +# `bi`. Residualization rides here: the group enumerators read it while +# extracting X and Y. +# @noRd +.twasJointArgs <- function(ctx, bi) { + list( + methodList = ctx$norm$methodList, + fineMappingResult = ctx$fineMappingResult, + dataDrivenPriorMatricesCv = ctx$dataDrivenPriorMatricesCv, + cisWindow = ctx$cisWindow, + region = ctx$xRegions[[bi]], + regionIndex = bi, + nRegions = length(ctx$xRegions), + residualizationParam = ctx$residualizationParam, + verbose = ctx$verbose ) } -# The traits of one context: traitId when supplied, else region overlap, else -# every trait in the context. +# The traits of one context: those traitId selects there, else region +# overlap, else every trait in the context. A traitId that leaves this +# context out keeps all of its traits -- region picks traits only when no +# traitId is given at all. # @noRd .twasQdsCtxTraits <- function(ctx, data, traitId, region) { se <- molecularTraits(data, contexts = ctx) ids <- rownames(se) if (!is.null(traitId)) { - intersect(ids, traitId) + intersect(ids, .selectedTraits(traitId, studyName(data), ctx) %||% ids) } else if (!is.null(region)) { rr <- SummarizedExperiment::rowRanges(se) ids[IRanges::overlapsAny(rr, region)] @@ -4495,7 +4598,7 @@ ensembleWeights <- function( dataDrivenPriorMatricesCv, cisWindow, naAction, - residualizationArgs, + residualizationParam, nPCs, verbose ) { @@ -4510,7 +4613,7 @@ ensembleWeights <- function( dataDrivenPriorMatricesCv = dataDrivenPriorMatricesCv, cisWindow = cisWindow, naAction = naAction, - residualizationArgs = residualizationArgs, + residualizationParam = residualizationParam, nPCs = nPCs, verbose = verbose ) @@ -4745,26 +4848,26 @@ EnsembleParam <- function( # predictions, so without folds there is nothing to stack -- and the old # behaviour was to return a result silently missing its ensemble row. # @noRd -.ensembleAssertCv <- function(ensembleArgs, crossValidationArgs) { - if (!isTRUE(ensembleArgs$enabled) || .cvEnabled(crossValidationArgs)) { +.ensembleAssertCv <- function(ensembleParam, crossValidationParam) { + if (!isTRUE(ensembleParam$enabled) || .cvEnabled(crossValidationParam)) { return(invisible(NULL)) } abort(glue( "twasWeightsPipeline: EnsembleParam(enabled = TRUE) needs ", "out-of-fold predictions to stack, so it requires ", "CrossValidationParam(folds >= 2); got folds = ", - "{crossValidationArgs$folds %||% 0}." + "{crossValidationParam$folds %||% 0}." )) } # The ensemble settings as a plain list, so a cfg record can carry the group # as one named field instead of four loose ones. # @noRd -.ensembleResolve <- function(ensembleArgs) { +.ensembleResolve <- function(ensembleParam) { list( - enabled = isTRUE(ensembleArgs$enabled), - r2Threshold = ensembleArgs$r2Threshold %||% 0.01, - solver = ensembleArgs$solver %||% "quadprog", - alpha = ensembleArgs$alpha %||% 1 + enabled = isTRUE(ensembleParam$enabled), + r2Threshold = ensembleParam$r2Threshold %||% 0.01, + solver = ensembleParam$solver %||% "quadprog", + alpha = ensembleParam$alpha %||% 1 ) } diff --git a/R/variantId.R b/R/variantId.R index b162df998..42970a797 100644 --- a/R/variantId.R +++ b/R/variantId.R @@ -20,8 +20,10 @@ stripChrPrefix <- function(x) str_remove(x, "^chr") #' Canonicalize chromosome identifiers to a normalized string. #' -#' Strips a leading "chr"/"ch" prefix (case-insensitive), uppercases, and maps -#' common synonyms to a single form (23 -> X, 24 -> Y, M -> MT). Returns a +#' Strips a leading "chr"/"ch" prefix (case-insensitive), uppercases, drops +#' the leading zeros of a numeric label (01 -> 1, as UK Biobank's BGEN files +#' write chromosomes) and maps common synonyms to a single form (23 -> X, +#' 24 -> Y, M -> MT). Returns a #' character vector and never coerces to integer, so non-autosomal chromosomes #' (X, Y, MT) survive instead of collapsing to NA. This is the single chromosome #' normalizer used for variant matching and identity throughout the package; it @@ -34,7 +36,8 @@ canonChrom <- function(x) { bare <- as.character(x) |> str_remove(regex("^chr", ignore_case = TRUE)) |> str_remove(regex("^ch", ignore_case = TRUE)) |> - str_to_upper() + str_to_upper() |> + str_remove("^0+(?=[0-9]+$)") canonChromLabel(bare) } @@ -111,7 +114,7 @@ isSnpAlleles <- function(a1, a2) { #' belongs -- \code{chr21:13988152:INS:T} whose real alleles are #' \code{A}/\code{AT} -- so the id string stops encoding its alleles even #' though the panel's own A1/A2 columns are correct. The id-string matchers -#' (\code{.ldFromSketchMatch}, \code{.subsetSketchToIds}) key on the id and +#' (\code{.ldFromRefMatch}, \code{.subsetLdRefToIds}) key on the id and #' cannot interpret such a record. The allele COLUMNS are authoritative, so the #' id is rebuilt from them; the variant is never dropped for how its id happens #' to spell an allele. @@ -471,16 +474,15 @@ normalizeVariantId <- function(ids, chrPrefix = TRUE, convention = NULL) { #' is `FALSE`. #' @param removeUnmatched Whether to remove unmatched variants. Default is #' `TRUE`. -#' @return An \code{AlleleQcResult} S4 object. Use \code{$harmonizedData} to -#' recover the post-QC variant data.frame and \code{$qcSummary} to inspect the -#' per-variant merge/flip/strand diagnostics. +#' @return A list with \code{harmonizedData}, the post-QC variant +#' data.frame, and \code{qcSummary}, the per-variant merge/flip/strand +#' diagnostics. #' @importFrom dplyr mutate inner_join filter pull select everything row_number #' @importFrom dplyr if_else any_of all_of rename across #' @importFrom purrr partial #' @importFrom vctrs vec_duplicate_detect #' @importFrom tidyr separate #' @keywords internal -#' @noRd #' @details Pure panel-vs-sumstats allele harmonization: match by (chrom, pos), #' detect A1/A2 swap, sign-flip \code{colToFlip} columns and complement #' \code{colToComplement} columns on swap. Variant-allele filters (indels, @@ -488,6 +490,7 @@ normalizeVariantId <- function(ids, chrPrefix = TRUE, convention = NULL) { #' corresponding \code{removeIndels} / \code{removeStrandAmbiguous} / #' \code{removeDups} flags are set; MAF / INFO / N column-numeric filters run #' in \code{.applyContentFilters()} before this function. +#' @noRd harmonizeAlleles <- function( targetData, refVariants, diff --git a/README.md b/README.md index f8b3482e6..d1558c873 100644 --- a/README.md +++ b/README.md @@ -50,8 +50,9 @@ Now you should be able to build `pecotmr` from source. ## Developer's notes + When any changes are made to `roxygen2` markup or the C++ code in -the src directory, run `devtools::document()` to update the -[RcppExports.cpp](src/RcppExports.cpp), the package namespaces (see +the src directory, run `devtools::document()` to update the cpp11 +registration code ([src/cpp11.cpp](src/cpp11.cpp) and +[R/cpp11.R](R/cpp11.R)), the package namespaces (see [NAMESPACE](NAMESPACE)), and the package documentation files (in the "man" subdirectory), diff --git a/_pkgdown.yml b/_pkgdown.yml index 2957dc5d8..f8a5f892f 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -124,8 +124,9 @@ reference: - GwasFineMappingResult-class - GwasSumStats-class - H2Estimate-class - - LdData-class + - GenotypeMixture-class - LdEigen-class + - LdMatrix-class - LdScore-class - LdStatistic-class - MashPrior-class @@ -155,8 +156,9 @@ reference: - fineMappingRow - GwasFineMappingResult - GwasSumStats - - LdData + - GenotypeMixture-constructor - LdEigen + - LdMatrix-constructor - LdScore - MashPrior - MultiStudyQtlDataset @@ -301,7 +303,7 @@ reference: - credibleSetSummary - credibleSets - lbf - - ldSketch + - ldReference - marginalEffects - methodNames - genomicRegion @@ -346,14 +348,14 @@ reference: - colocViews - colocBoostOutcomes - computingTime - - ldSketch + - ldReference - regionVcp - subtitle: "SumStatsBase" contents: - af - coef - - ldSketch + - ldReference - maf - nSamples - variantColumnReplace @@ -393,7 +395,7 @@ reference: - cvResult - weightsDataType - methodFits - - ldSketch + - ldReference - methodNames - genomicRegion - weightStandardized @@ -447,18 +449,13 @@ reference: - annotationJackknifeCoefs - traitNames - - subtitle: "LdData" + - subtitle: "LdMatrix / GenotypeMixture" contents: - - blockMetadata - ldMatrix - - genotypes - mixtureWeights - ldPanelNSamples - - refPanel - - snpIdx - variantIds - - variantInfo - - hasGenotypes + - genotypes - subtitle: "LdEigen" contents: @@ -561,10 +558,9 @@ reference: - title: "LD infrastructure" desc: > - Computing, loading, and manipulating LD matrices, plus - design-matrix conditioning utilities. + Computing and checking LD matrices, plus design-matrix conditioning + utilities. contents: - - loadLdMatrix - computeLd - checkLd - enforceDesignFullRank @@ -647,7 +643,6 @@ reference: - glmnetWeights - lassoWeights - enetWeights - - lassosumRss - lassosumRssWeights - mcpWeights - mcpRssWeights @@ -655,7 +650,6 @@ 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