examples-R/inst/doc/ExpressionAndProteinCorrelation.html at master · isb-cgc/examples-R · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
<!DOCTYPE html>
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>

<title>Expression and Protein Correlation</title>

<script type="text/javascript">
window.onload = function() {
  var imgs = document.getElementsByTagName('img'), i, img;
  for (i = 0; i < imgs.length; i++) {
    img = imgs[i];
    // center an image if it is the only element of its parent
    if (img.parentElement.childElementCount === 1)
      img.parentElement.style.textAlign = 'center';
  }
};
</script>

<!-- Styles for R syntax highlighter -->
<style type="text/css">
   pre .operator,
   pre .paren {
     color: rgb(104, 118, 135)
   }

   pre .literal {
     color: #990073
   }

   pre .number {
     color: #099;
   }

   pre .comment {
     color: #998;
     font-style: italic
   }

   pre .keyword {
     color: #900;
     font-weight: bold
   }

   pre .identifier {
     color: rgb(0, 0, 0);
   }

   pre .string {
     color: #d14;
   }
</style>

<!-- R syntax highlighter -->
<script type="text/javascript">
var hljs=new function(){function m(p){return p.replace(/&/gm,"&amp;").replace(/</gm,"&lt;")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};
hljs.initHighlightingOnLoad();
</script>


<style type="text/css">
body, td {
   font-family: sans-serif;
   background-color: white;
   font-size: 13px;
}

body {
  max-width: 800px;
  margin: auto;
  padding: 1em;
  line-height: 20px;
}

tt, code, pre {
   font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}

h1 {
   font-size:2.2em;
}

h2 {
   font-size:1.8em;
}

h3 {
   font-size:1.4em;
}

h4 {
   font-size:1.0em;
}

h5 {
   font-size:0.9em;
}

h6 {
   font-size:0.8em;
}

a:visited {
   color: rgb(50%, 0%, 50%);
}

pre, img {
  max-width: 100%;
}
pre {
  overflow-x: auto;
}
pre code {
   display: block; padding: 0.5em;
}

code {
  font-size: 92%;
  border: 1px solid #ccc;
}

code[class] {
  background-color: #F8F8F8;
}

table, td, th {
  border: none;
}

blockquote {
   color:#666666;
   margin:0;
   padding-left: 1em;
   border-left: 0.5em #EEE solid;
}

hr {
   height: 0px;
   border-bottom: none;
   border-top-width: thin;
   border-top-style: dotted;
   border-top-color: #999999;
}

@media print {
   * {
      background: transparent !important;
      color: black !important;
      filter:none !important;
      -ms-filter: none !important;
   }

   body {
      font-size:12pt;
      max-width:100%;
   }

   a, a:visited {
      text-decoration: underline;
   }

   hr {
      visibility: hidden;
      page-break-before: always;
   }

   pre, blockquote {
      padding-right: 1em;
      page-break-inside: avoid;
   }

   tr, img {
      page-break-inside: avoid;
   }

   img {
      max-width: 100% !important;
   }

   @page :left {
      margin: 15mm 20mm 15mm 10mm;
   }

   @page :right {
      margin: 15mm 10mm 15mm 20mm;
   }

   p, h2, h3 {
      orphans: 3; widows: 3;
   }

   h2, h3 {
      page-break-after: avoid;
   }
}
</style>


</head>

<body>
<h1>Expression and Protein Correlation</h1>

<p>In this example, we will look at the correlation between mRNAseq-based gene expression data and RPPA-based protein expression data.  We will do this using two molecular data tables from the isb-cgc:tcga_201510_alpha dataset and a cohort table from the isb-cgc:tcga_cohorts dataset.</p>

<pre><code class="r">library(ISBCGCExamples)

# The directory in which the files containing SQL reside.
#sqlDir = file.path(&quot;/PATH/TO/GIT/CLONE/OF/examples-R/inst/&quot;,
sqlDir = file.path(system.file(package = &quot;ISBCGCExamples&quot;),&quot;sql&quot;)
</code></pre>

<pre><code class="r">######################[ TIP ]########################################
## Set the Google Cloud Platform project id under which these queries will run.
##
## If you are using the workshop docker image, this is already
## set for you in your .Rprofile and you can skip this step.

# project = &quot;YOUR-PROJECT-ID&quot;
#####################################################################
</code></pre>

<h2>Spearman Correlation in BigQuery</h2>

<p>We will start by performing the correlation directly in BigQuery.  We will use a pre-defined SQL query in which key strings will be replaced according to the values specified in the &ldquo;replacements&rdquo; list.</p>

<pre><code class="r"># Set the desired tables to query.
expressionTable = &quot;isb-cgc:tcga_201510_alpha.mRNA_UNC_HiSeq_RSEM&quot;
proteinTable = &quot;isb-cgc:tcga_201510_alpha.Protein_RPPA_data&quot;
cohortTable = &quot;isb-cgc:tcga_cohorts.BRCA&quot;

# Do not correlate unless there are at least this many observations available
minimumNumberOfObservations = 30
</code></pre>

<p>We&#39;ll pause for a moment here and have a look at the size of our cohort table:</p>

<pre><code class="r">cohortInfo &lt;- get_table(&quot;isb-cgc&quot;,&quot;tcga_cohorts&quot;,&quot;BRCA&quot;)
cohortInfo$description
</code></pre>

<pre><code>[1] &quot;This BRCA cohort is a curated list of patients and samples from the TCGA BRCA study.  It contains 1088 patients and 2275 samples.  You may join this table with other tables in the isb-cgc:tcga_201507_alpha dataset to perform further analysis on this cohort.&quot;
</code></pre>

<pre><code class="r">cohortInfo$numRows
</code></pre>

<pre><code>[1] &quot;2275&quot;
</code></pre>

<pre><code class="r">ptm1 &lt;- proc.time()

# Now we are ready to run the query.
result = DisplayAndDispatchQuery (
             file.path(sqlDir, &quot;protein-mrna-spearman-correlation.sql&quot;),
             project=project,
             replacements=list(&quot;_EXPRESSION_TABLE_&quot;=expressionTable,
                               &quot;_PROTEIN_TABLE_&quot;=proteinTable,
                               &quot;_COHORT_TABLE_&quot;=cohortTable,
                               &quot;_MINIMUM_NUMBER_OF_OBSERVATIONS_&quot;=minimumNumberOfObservations) )
</code></pre>

<pre><code># This query performs a Spearman (ranked) correlation between the protein- and the mRNA-expression data
# partitioned based on gene and protein.  The correlation is done by the BigQuery CORR function, but first
# we need to use the RANK function to turn expression values into ranks.

#### QUESTION: should the COUNT(1) be COUNT(2) instead ???

SELECT
  gene,
  protein,
  COUNT(1) AS num_observations,
  CORR(expr_rank, prot_rank) AS spearman_corr
FROM (
  # in order to do a ranke-based (Spearman) correlation, we need to turn the expression values into ranks
  SELECT
    barcode,
    gene,
    protein,
    RANK() OVER (PARTITION BY gene, protein ORDER BY log2_count ASC) AS expr_rank,
    RANK() OVER (PARTITION BY gene, protein ORDER BY protein_expression ASC) AS prot_rank,
  FROM (
    # here we need the sample identifier, the gene symbol, the protein name, and the two expression values
    # that come out of the JOIN defined below
    SELECT
      feat1.SampleBarcode AS barcode,
      Gene_Name AS gene,
      Protein_Name AS protein,
      protein_expression,
      log2_count,
    FROM (
      # on the left side of the JOIN, we SELECT the sample identifier, the gene symbol, the protein name,
      # and the protein expression value, but only for samples that are in our specified &quot;cohort&quot;
      SELECT
        SampleBarcode,
        Gene_Name,
        Protein_Name,
        protein_expression
      FROM
        [isb-cgc:tcga_201510_alpha.Protein_RPPA_data]
      WHERE
        SampleBarcode IN (
        SELECT
          SampleBarcode
        FROM
          [isb-cgc:tcga_cohorts.BRCA] ) ) feat1
    JOIN EACH (
      # on the right side of the JOIN, we SELECT the sample identifier, the gene symbol, and the gene
      # expression value (to which we apply a LOG2() operation), again only for samples in our &quot;cohort&quot;
      SELECT
        SampleBarcode,
        HGNC_gene_symbol,
        LOG2(normalized_count+1) AS log2_count
      FROM
        [isb-cgc:tcga_201510_alpha.mRNA_UNC_HiSeq_RSEM]
      WHERE
        SampleBarcode IN (
        SELECT
          SampleBarcode
        FROM
          [isb-cgc:tcga_cohorts.BRCA] ) ) feat2
    ON
      # the JOIN needs to line up rows where the sample identifier and the gene symbol match
      feat1.SampleBarcode = feat2.SampleBarcode
      AND feat1.Gene_Name = feat2.HGNC_gene_symbol))
GROUP BY
  gene,
  protein
HAVING
  # although we will compute correlations for all genes and proteins, we only want to keep values where
  # the correlation was based on at least 30 observations
  num_observations &gt;= 30
ORDER BY
  # and finally we want to order the ouputs from largest positive correlation, descending to the most negative
  # correlation
  spearman_corr DESC
</code></pre>

<pre><code class="r">ptm2 &lt;- proc.time() - ptm1
cat(&quot;Wall-clock time for BigQuery:&quot;,ptm2[3])
</code></pre>

<pre><code>Wall-clock time for BigQuery: 2.377
</code></pre>

<p>Number of rows returned by this query: <code>nrow(result)</code>.</p>

<p>The result is a table with one row for each (gene,protein) pair for which at least 30 data values exist for the specified cohort.  The (gene,protein) pair is defined by a gene symbol and a protein name.  In many cases the gene symbol and the protein name may be identical, but for some genes the RPPA dataset may contain expression values for more than one post-translationally-modified protein product from a particular gene.</p>

<pre><code class="r">head(result)
</code></pre>

<pre><code>##     gene  protein num_observations spearman_corr
## 1   ESR1 ER-alpha              922     0.8552661
## 2   BCL2    Bcl-2              922     0.8022549
## 3  GATA3    GATA3              922     0.7479918
## 4  PREX1   P-REX1              922     0.7412556
## 5   FASN     FASN              922     0.7299020
## 6 IGFBP2   IGFBP2              922     0.7262222
</code></pre>

<pre><code class="r">library(ggplot2)

# Use ggplot to create a histogram overlaid with a transparent kernel density curve
ggplot(result, aes(x=spearman_corr)) +
     # use &#39;density&#39; instead of &#39;count&#39; for the histogram
     geom_histogram(aes(y=..density..),
                   binwidth=.05,
                   colour=&quot;black&quot;, fill=&quot;white&quot;) +
     # and overlay with a transparent density plot
     geom_density(alpha=.2, fill=&quot;#FF6666&quot;) +
     # and add a vertical line at x=0 to emphasize that most correlations are positive
     geom_vline(xintercept=0)
</code></pre>

<p><img src="data:image/png;base64,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" title="plot of chunk spearman_density" alt="plot of chunk spearman_density" style="display: block; margin: auto;" /></p>

<h2>Spearman Correlation in R</h2>

<p>Now let&#39;s reproduce one of the results directly in R.  The highest correlation value was for the (ESR1,ER-alpha) (gene,protein) pair, so that&#39;s the pair that we will use for our validation test.</p>

<h3>Retrieve Expression Data</h3>

<p>First we retrieve the mRNA expression data for a specific gene and only for samples in our cohort.</p>

<pre><code class="r"># Set the desired gene to query.
gene = &quot;ESR1&quot;

expressionData = DisplayAndDispatchQuery(file.path(sqlDir, &quot;expression-data-by-cohort.sql&quot;),
                                         project=project,
                                         replacements=list(&quot;_EXPRESSION_TABLE_&quot;=expressionTable,
                                                           &quot;_COHORT_TABLE_&quot;=cohortTable,
                                                           &quot;_GENE_&quot;=gene))
</code></pre>

<pre><code># Retrieve expression data for a particular gene for samples within a cohort.
SELECT
  SampleBarcode,
  HGNC_gene_symbol,
  normalized_count
FROM [isb-cgc:tcga_201510_alpha.mRNA_UNC_HiSeq_RSEM]
WHERE
  HGNC_gene_symbol = &#39;ESR1&#39;
  AND SampleBarcode IN (
  SELECT
    SampleBarcode
  FROM
    [isb-cgc:tcga_cohorts.BRCA] )
ORDER BY
  SampleBarcode
</code></pre>

<p>Number of rows returned by this query: <code>nrow(expressionData)</code>.</p>

<pre><code class="r">head(expressionData)
</code></pre>

<pre><code>##      SampleBarcode HGNC_gene_symbol normalized_count
## 1 TCGA-3C-AAAU-01A             ESR1        3457.9620
## 2 TCGA-3C-AALI-01A             ESR1          68.5155
## 3 TCGA-3C-AALJ-01A             ESR1        7482.3209
## 4 TCGA-3C-AALK-01A             ESR1        2485.3124
## 5 TCGA-4H-AAAK-01A             ESR1        5518.2979
## 6 TCGA-5L-AAT0-01A             ESR1        6592.1689
</code></pre>

<h3>Retrieve Protein Data</h3>

<p>Next, we retrieve the protein data for a specific (gene,protein) pair, and again only for samples in our cohort.</p>

<pre><code class="r">protein = &quot;ER-alpha&quot;

proteinData = DisplayAndDispatchQuery(file.path(sqlDir, &quot;protein-data-by-cohort.sql&quot;),
                                      project=project,
                                      replacements=list(&quot;_PROTEIN_TABLE_&quot;=proteinTable,
                                                        &quot;_COHORT_TABLE_&quot;=cohortTable,
                                                        &quot;_GENE_&quot;=gene,
                                                        &quot;_PROTEIN_&quot;=protein))
</code></pre>

<pre><code># Retrieve protein data for a particular gene for samples within a cohort.
SELECT
  SampleBarcode,
  Gene_Name,
  Protein_Name,
  protein_expression
FROM
  [isb-cgc:tcga_201510_alpha.Protein_RPPA_data]
WHERE
  Gene_Name = &#39;ESR1&#39;
  AND Protein_Name = &#39;ER-alpha&#39;
  AND SampleBarcode IN (
  SELECT
    SampleBarcode
  FROM
    [isb-cgc:tcga_cohorts.BRCA] )
ORDER BY
  SampleBarcode
</code></pre>

<p>Number of rows returned by this query: <code>nrow(proteinData)</code>.</p>

<pre><code class="r">head(proteinData)
</code></pre>

<pre><code>##      SampleBarcode Gene_Name Protein_Name protein_expression
## 1 TCGA-3C-AALI-01A      ESR1     ER-alpha         -0.7433601
## 2 TCGA-3C-AALK-01A      ESR1     ER-alpha          0.0404196
## 3 TCGA-4H-AAAK-01A      ESR1     ER-alpha          1.3058341
## 4 TCGA-5T-A9QA-01A      ESR1     ER-alpha          1.7900436
## 5 TCGA-A1-A0SF-01A      ESR1     ER-alpha          1.1791140
## 6 TCGA-A1-A0SH-01A      ESR1     ER-alpha         -1.5588764
</code></pre>

<p>Since protein data is not typically available for as many samples as mRNA expression data, the returned &ldquo;expressionData&rdquo; table is likely to be quite a bit bigger than the &ldquo;proteinData&rdquo; table.  The next step is an inner join of these two tables:</p>

<pre><code class="r">library(dplyr)

data = inner_join(expressionData, proteinData)
</code></pre>

<pre><code>## Joining by: &quot;SampleBarcode&quot;
</code></pre>

<pre><code class="r">dim(data)
</code></pre>

<pre><code>## [1] 922   6
</code></pre>

<pre><code class="r">head(arrange(data, normalized_count))
</code></pre>

<pre><code>##      SampleBarcode HGNC_gene_symbol normalized_count Gene_Name
## 1 TCGA-AQ-A54N-01A             ESR1           2.7343      ESR1
## 2 TCGA-A2-A3XV-01A             ESR1           3.8710      ESR1
## 3 TCGA-E2-A158-01A             ESR1           4.4555      ESR1
## 4 TCGA-AN-A0G0-01A             ESR1           5.8621      ESR1
## 5 TCGA-AR-A1AH-01A             ESR1           6.0368      ESR1
## 6 TCGA-AR-A0TP-01A             ESR1           6.1550      ESR1
##   Protein_Name protein_expression
## 1     ER-alpha          -2.417648
## 2     ER-alpha          -2.432641
## 3     ER-alpha          -3.637536
## 4     ER-alpha          -2.958320
## 5     ER-alpha          -2.591913
## 6     ER-alpha          -2.540526
</code></pre>

<p>The dimension of the resulting table should match the number of observations indicated in our original BigQuery result, and now we perform a Spearman correlation on the two data vectors:</p>

<pre><code class="r">cor(x=data$normalized_count, y=data$protein_expression, method=&quot;spearman&quot;)
</code></pre>

<pre><code>## [1] 0.8552696
</code></pre>

<pre><code class="r">qplot(data=data, y=log2(normalized_count), x=protein_expression, geom=c(&quot;point&quot;,&quot;smooth&quot;),
      xlab=&quot;protein level&quot;, ylab=&quot;log2 mRNA level&quot;)
</code></pre>

<pre><code>## geom_smooth: method=&quot;auto&quot; and size of largest group is &lt;1000, so using loess. Use &#39;method = x&#39; to change the smoothing method.
</code></pre>

<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p>

<p>The R-based Spearman correlation matches the BigQuery result.</p>

<h2>Provenance</h2>

<pre><code class="r">sessionInfo()
</code></pre>

<pre><code>R version 3.2.1 (2015-06-18)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] ISBCGCExamples_0.1 ggplot2_1.0.1      scales_0.3.0
[4] bigrquery_0.1.0    dplyr_0.4.3

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.2      knitr_1.11       magrittr_1.5     MASS_7.3-44
 [5] munsell_0.4.2    colorspace_1.2-6 R6_2.1.1         stringr_1.0.0
 [9] httr_1.0.0       plyr_1.8.3       tools_3.2.1      parallel_3.2.1
[13] grid_3.2.1       gtable_0.1.2     DBI_0.3.1        lazyeval_0.1.10
[17] assertthat_0.1   digest_0.6.8     reshape2_1.4.1   formatR_1.2.1
[21] curl_0.9.3       mime_0.4         evaluate_0.8     labeling_0.3
[25] stringi_1.0-1    jsonlite_0.9.17  markdown_0.7.7   proto_0.3-10
</code></pre>

</body>

</html>