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1.5.0 (2026-11-01)

This release contains changes in cell-cell interaction (CCI) mainly improvements in runtime and memory usage and requires code to be updated.

  • Memory usage is reduced through chunking (~256MB) in st.cci.run scoring and permutation testing. On a 5,000,000 bin synthetic Visium HD sample with connectomeDB2025, n_pairs=1000, runs take ~24GB in ~10 minutes.
  • SpatialData support added with a new st.io package. Supporting: atera, xenium, visium_hd and visium. Start of spatial data native plotting with st.pl.sdata_plot and added AnnData's st.pp.highly_variable_genes.
  • New datasets st.datasets.visium_hd_sge and atera_sge. All four dataset functions now return SpatialData objects; visium_sge returned AnnData and xenium_sge only downloaded the files.
  • Upgraded ligand-receptor databases to connectomeDB2025 (human and mouse).
  • Ligand-receptor pairs are an LRPairs object instead of an array of 'L_R' strings. It tracks the species and where each pair came from. Pairs can be filtered and combined (lrs.filter(...), lrs1 | lrs2).
    • st.tl.cci.run takes only LRPairs; an array of 'L_R' names raises TypeError. Wrap strings with st.tl.cci.LRPairs.from_names(names, species="human").
    • st.tl.cci.lr_pairs(database, species) loads a database shipped with stLearn: "connectomeDB2025" (default; human or mouse) or "connectomeDB2020" (human).
    • st.tl.cci.read_lr_pairs(path, species=...) reads your own csv/tsv of ligand and receptor columns.
    • st.tl.cci.load_lrs is deprecated and all APIs now use lr_pairs, or lr_pairs(...).names() for the old array. Note that lr_pairs() defaults to connectomeDB2025, where load_lrs() defaults to connectomeDB2020.
    • Previous databases names "connectomeDB2020_lit" and "connectomeDB2020_put" are removed. "connectomeDB2020" is the human only, literature pairs and the putative pairs are no longer shipped.
    • st.add.lr is removed; use read_lr_pairs or LRPairs.from_names.
    • The LR table from run stores each pair's source in var["source"] and the species in uns["lr_species"], which run_lr_go uses as its default species.
  • Spatial trajectory and other APIs use cluster label strings (cluster="6").
  • AnnData backwards compatibility available through st.io.attach_spatial for Visium/Visium HD and st.io.attach_spatial_sdata for Xenium/Atera.
  • CCI results are now a LR table (AnnData) returned by st.tl.cci.run. A spots x LR pairs with summaries in var and scores and p-values are in sparse layers. Downstream functions and plots now take lr_results.
  • Support for microns through microns_per_pixel, st.utils.microns_to_pixels, pixels_to_microns and create_grid(bin_um=).
  • SME computes weights only between nearby spots and data is kept sparse where possible.
  • st.pp.extract_feature supports ConvNeXt and takes weights=.
  • Upgrade libraries: pillow>=13.0.0,<14.0, numba>=0.64.0, scikit-image>=0.25.0, torch>=2.5.0, matplotlib>=3.11,<4.0.

API and Bug Fixes:

  • Fixed double application of scaling, adding instructions and warning for image alignment in st.io.create_stlearn Bug #303.
  • A pair listed twice is now tested once - fixed Bug #305.
  • Incorrect labelling in CCI Plot - rows labelled "Senders", columns labelled "Receivers" - should be the other way around Bugs #309, #363.
  • SME failed with "Input X contains NaN" when a spot had no array_row/array_col - these are now left out of the spot spacing fit - fixed Bug #310.
  • st.tl.cci.run(use_label=...) never applied cell heterogeneity weighting: it read the cell-type data from the wrong key, found nothing, and weighted every spot 1, so results were identical with and without use_label. It now weights by heterogeneity from cell-type proportions in uns[use_label] or per-cell labels in obs[use_label], and takes cell_prop_cutoff. CCI scores and significance change for any run that used use_label.
  • st.tl.cci.run_cci: p-values from permutation are now (k+1)/(N+1) and can no longer be 0, cutoff is now p < p_cutoff, and each permutation thread is seeded through random_state.
  • spot_mixtures matches cell types by exact name.
  • When min_expr > 0 n_spots in the LR summary counted spots with a score above min_expr, while the min_spots filter counted spots with a score above 0, so the reported count was not the one the filter used. Both now use score > 0.
  • st.tl.cci.grid dropped cells lying exactly on the inner edge of the last row or column of the grid, and on the outer edges of the corner square.
  • st.pl.lr_plot statistic names were changed from p_val, p_adj and -log10(p_adj) to p_vals, p_adjs and neg_log10_p_adjs. It always drew lr_sig_scores whatever use_label asked for, so a plot labelled p_adjs or neg_log10_p_adjs showed the masked score instead. st.pl.lr_plot and st.pl.ccinet_plot also draw on a fig or ax you passed in rather than opening a new figure, so they can go in subplots.
  • Images in uns["spatial"] are now uint8 (0-255), not floats in [0, 1].
  • st.pp.extract_feature tiles were fed as floats between 0-255 instead of 0-1. Tiles are now read as uint8, so X_tile_feature and X_morphology change.
  • st.concatenate_spatial_adata: the x and y offsets added to obsm["spatial"] for each tile were swapped, so spots on any tile off the diagonal landed on the wrong tile. Tiles also keep their aspect ratio instead of being stretched to fixed_size, each sample is scaled by its own scale factor rather than the first sample's, and fixed_size is (height, width) like a NumPy shape.
  • st.io.read_merfish works again with new notebook. Defaults spot_diameter_fullres to 10 (it was 50).
  • st.io.read_10x reads 16-bit / signed PNGs and reports missing images clearly.
  • st.add.labels / st.add.add_loupe_clusters match by barcode.
  • st.add.positions reads Space Ranger 2.0+ files.
  • st.add.parsing assigned spots the wrong coordinates (they were interleaved from the spots in obs), so every plot and anything using obsm["spatial"] was wrong. Each spot now gets its own position.
  • SME fits each axis separately when finding neighbours.
  • Spatial trajectory: st.spatial.trajectory.pseudotime orients by median pseudotime (paths may differ), shortest_path_spatial_paga returns labels, plus fixes to local_level, weight_optimizing_local, de_transition_plot and the trajectory plots.
  • st.pl.qc_plot no longer makes a dense copy.
  • Readers were moved to st.io: st.read_10x, st.read_xenium, st.read_old_st, st.read_slide_seq, st.read_merfish, st.read_seq_fish and st.create_stlearn.
  • st.tl.cci.grid vectorised and no longer takes n_cpus.
  • obsm["spatial"] is in full-resolution pixels (was image pixels), matching the rest of stLearn (used by st.tl.cci.grid).
  • SME outputs are sparse when X is sparse; no weight matrices in uns.
  • Removed: extract_feature(cnn_base="xception"), deconvolution_plot(data_alpha=), local_plot(cluster=), Spatial(basis=).
  • New defaults: pl.trajectory.pseudotime_plot(node_size=60) (area); pl.trajectory.check_trajectory(size_spatial=7, img_key=None).
  • st.spatial.trajectory.pseudotime no longer takes n_neighbors, use_rep or run_knn - these must be run prior and will throw an exception if there's no neighbourhood graph.

1.4.1 (2026-06-16)

  • Improved speed of random spot generation used in permutation testing.

API and Bug Fixes: * Fixed up docstring and types for various CCI methods. * Fixes for issues #350 and #300 with incorrect types. * Upgrade libraries: pillow>=12.0.0,<13.0

1.4.0 (2026-05-05)

  • Removed tensorflow and keras and replaced with torch and torchvision.
  • The CCI implementation removed string comparisons for spot-pair edges and replaced it with one set of two packed 32 bit integers. Removed deduplication pass (m^2) with a hash lookup (O(n)).
  • Upgrade libraries: numpy >=2.4.0, scanpy >= 1.12.0, scipy >= 1.17.0, added spatialdata, shapely, geopandas.

API and Bug Fixes: * Added row and polygon annotations. * Fixed exception handling to catch previous exceptions when rethrown.

1.3.0 (2026-02-24)

  • Removed interactive stLearn/embedded web application.
  • Removed louvain clustering - replaced with leiden.

API and Bug Fixes: * Fix import on MutableVertexPartition to use leidenalg.VertexPartition. * Switch default flavour in leiden to use igraph (and its required parameters). * Renamed methods and classes to follow lowercase standard.

1.2.2 (2025-10-20)

  • Added support for Python 3.11 and 3.12.
  • Upgraded scanpy to 1.11 - clustering will be different.
  • Added more CCI tests.

API and Bug Fixes: * Fixed copy-paste error in leiden.py file.

1.1.5 (2025-09-17)

  • Add Leiden clustering wrapper.
  • Fix documentation, refactor code in spatial.SME.

1.1.1 (2025-07-07)

  • Support Python 3.10.x
  • Added quality checks black, ruff and mypy and fixed appropriate source code.
  • Copy parameters now work with the same semantics as scanpy.
  • Library upgrades for leidenalg, louvain, numba, numpy, scanpy, and tensorflow.
  • datasets.xenium_sge - loads Xenium data (and caches it) similar to scanpy.visium_sge.

API and Bug Fixes:

  • Xenium TIFF and cell positions are now aligned.
  • Consistent with type annotations - mainly missing None annotations.
  • pl.cluster_plot - Does not keep colours from previous runs when clustering.
  • pl.trajectory.pseudotime_plot - Fix typing of cluster values in .uns["split_node"].
  • Removed datasets.example_bcba - Replaced with wrapper for scanpy.visium_sge.
  • Moved spatials directory to spatial, cleaned up pl and tl packages.

0.4.11 (2022-11-25)

0.4.10 (2022-11-22)

0.4.8 (2022-06-15)

0.4.7 (2022-03-28)

0.4.6 (2022-03-09)

0.4.5 (2022-03-02)

0.4.0 (2022-02-03)

0.3.2 (2021-03-29)

0.3.1 (2020-12-24)

0.2.7 (2020-09-12)

0.2.6 (2020-08-04)