Correlation between copy number variations of arm-level result and molecular subtypes
Rectum Adenocarcinoma (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1416V42
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1416V42
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and subtypes.

Summary

Testing the association between copy number variation 75 arm-level results and 12 molecular subtypes across 162 patients, 11 significant findings detected with Q value < 0.25.

  • 6p gain cnv correlated to 'CN_CNMF'.

  • 6q gain cnv correlated to 'CN_CNMF'.

  • 7p gain cnv correlated to 'CN_CNMF'.

  • 7q gain cnv correlated to 'CN_CNMF'.

  • 8q gain cnv correlated to 'CN_CNMF'.

  • 13q gain cnv correlated to 'CN_CNMF' and 'MIRSEQ_MATURE_CNMF'.

  • 16q gain cnv correlated to 'CN_CNMF'.

  • 20q gain cnv correlated to 'CN_CNMF'.

  • 18p loss cnv correlated to 'CN_CNMF'.

  • 18q loss cnv correlated to 'CN_CNMF'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 75 arm-level results and 12 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 11 significant findings detected.

Molecular
subtypes 		MRNA
CNMF 		MRNA
CHIERARCHICAL 		CN
CNMF 		METHLYATION
CNMF 		RPPA
CNMF 		RPPA
CHIERARCHICAL 		MRNASEQ
CNMF 		MRNASEQ
CHIERARCHICAL 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRSEQ
MATURE
CNMF 		MIRSEQ
MATURE
CHIERARCHICAL
nCNV (%) nWild-Type Fisher's exact test Fisher's exact test Chi-square test Fisher's exact test Fisher's exact test Chi-square test Chi-square test Fisher's exact test Chi-square test Fisher's exact test Fisher's exact test Chi-square test
13q gain 0 (0%) 60 0.186
(1.00) 		0.568
(1.00) 		1.28e-07
(0.000103) 		0.211
(1.00) 		0.973
(1.00) 		0.552
(1.00) 		0.309
(1.00) 		0.585
(1.00) 		0.0316
(1.00) 		0.108
(1.00) 		5.42e-05
(0.0431) 		0.0236
(1.00)
6p gain 0 (0%) 135 0.393
(1.00) 		0.0248
(1.00) 		3.03e-05
(0.0242) 		0.0158
(1.00) 		0.353
(1.00) 		0.145
(1.00) 		0.614
(1.00) 		1
(1.00) 		0.748
(1.00) 		0.694
(1.00) 		0.434
(1.00) 		0.894
(1.00)
6q gain 0 (0%) 135 0.393
(1.00) 		0.0248
(1.00) 		7.74e-05
(0.0616) 		0.169
(1.00) 		0.353
(1.00) 		0.145
(1.00) 		0.44
(1.00) 		0.903
(1.00) 		0.861
(1.00) 		0.694
(1.00) 		0.55
(1.00) 		0.732
(1.00)
7p gain 0 (0%) 72 0.00125
(0.988) 		0.00168
(1.00) 		1.28e-06
(0.00102) 		0.0114
(1.00) 		0.563
(1.00) 		0.922
(1.00) 		0.62
(1.00) 		0.763
(1.00) 		0.151
(1.00) 		0.0302
(1.00) 		0.0845
(1.00) 		0.199
(1.00)
7q gain 0 (0%) 83 0.00284
(1.00) 		0.00441
(1.00) 		6.47e-07
(0.000518) 		0.0908
(1.00) 		0.293
(1.00) 		0.742
(1.00) 		0.412
(1.00) 		0.293
(1.00) 		0.651
(1.00) 		0.354
(1.00) 		0.516
(1.00) 		0.61
(1.00)
8q gain 0 (0%) 90 0.00692
(1.00) 		0.0793
(1.00) 		3.13e-08
(2.52e-05) 		0.287
(1.00) 		0.326
(1.00) 		0.497
(1.00) 		0.709
(1.00) 		0.816
(1.00) 		0.488
(1.00) 		0.288
(1.00) 		0.871
(1.00) 		0.824
(1.00)
16q gain 0 (0%) 132 0.243
(1.00) 		1
(1.00) 		0.000114
(0.0908) 		0.81
(1.00) 		0.733
(1.00) 		0.755
(1.00) 		0.786
(1.00) 		0.588
(1.00) 		0.0397
(1.00) 		0.0964
(1.00) 		0.0104
(1.00) 		0.0625
(1.00)
20q gain 0 (0%) 30 0.0253
(1.00) 		0.446
(1.00) 		0.000265
(0.21) 		0.325
(1.00) 		0.277
(1.00) 		0.551
(1.00) 		0.383
(1.00) 		0.717
(1.00) 		0.791
(1.00) 		0.465
(1.00) 		0.0969
(1.00) 		0.997
(1.00)
18p loss 0 (0%) 48 0.0413
(1.00) 		0.411
(1.00) 		1.17e-05
(0.00934) 		0.249
(1.00) 		0.376
(1.00) 		0.691
(1.00) 		0.141
(1.00) 		1
(1.00) 		0.582
(1.00) 		0.406
(1.00) 		0.051
(1.00) 		0.0256
(1.00)
18q loss 0 (0%) 36 0.0284
(1.00) 		0.112
(1.00) 		9.16e-07
(0.000733) 		0.182
(1.00) 		0.448
(1.00) 		0.708
(1.00) 		0.0628
(1.00) 		0.531
(1.00) 		0.613
(1.00) 		0.668
(1.00) 		0.00996
(1.00) 		0.0208
(1.00)
1p gain 0 (0%) 158 0.708
(1.00) 		0.475
(1.00) 		0.616
(1.00) 		0.108
(1.00) 		0.15
(1.00) 		0.748
(1.00)
1q gain 0 (0%) 135 0.671
(1.00) 		1
(1.00) 		0.0332
(1.00) 		0.233
(1.00) 		0.169
(1.00) 		0.402
(1.00) 		0.998
(1.00) 		0.0358
(1.00) 		0.43
(1.00) 		0.761
(1.00) 		0.06
(1.00) 		0.322
(1.00)
2p gain 0 (0%) 142 0.108
(1.00) 		0.359
(1.00) 		0.157
(1.00) 		0.93
(1.00) 		0.679
(1.00) 		0.709
(1.00) 		0.0292
(1.00) 		0.0995
(1.00) 		0.553
(1.00) 		0.346
(1.00) 		0.457
(1.00) 		0.208
(1.00)
2q gain 0 (0%) 141 0.0141
(1.00) 		0.0931
(1.00) 		0.701
(1.00) 		0.74
(1.00) 		0.288
(1.00) 		0.881
(1.00) 		0.0118
(1.00) 		0.138
(1.00) 		0.108
(1.00) 		0.726
(1.00) 		0.0234
(1.00) 		0.164
(1.00)
3p gain 0 (0%) 145 0.832
(1.00) 		1
(1.00) 		0.0252
(1.00) 		0.48
(1.00) 		0.109
(1.00) 		0.775
(1.00) 		0.846
(1.00) 		1
(1.00) 		0.344
(1.00) 		0.0138
(1.00) 		0.143
(1.00) 		0.457
(1.00)
3q gain 0 (0%) 139 1
(1.00) 		0.894
(1.00) 		0.00832
(1.00) 		1
(1.00) 		0.428
(1.00) 		0.323
(1.00) 		0.955
(1.00) 		1
(1.00) 		0.518
(1.00) 		0.041
(1.00) 		0.422
(1.00) 		0.356
(1.00)
4p gain 0 (0%) 158 0.391
(1.00) 		0.475
(1.00) 		0.463
(1.00) 		0.587
(1.00) 		0.366
(1.00) 		0.501
(1.00) 		0.314
(1.00) 		0.379
(1.00)
4q gain 0 (0%) 159 0.286
(1.00) 		0.476
(1.00) 		0.515
(1.00) 		0.78
(1.00) 		0.512
(1.00)
5p gain 0 (0%) 142 0.759
(1.00) 		0.911
(1.00) 		0.072
(1.00) 		0.0783
(1.00) 		0.326
(1.00) 		0.87
(1.00) 		0.306
(1.00) 		1
(1.00) 		0.331
(1.00) 		0.68
(1.00) 		0.0298
(1.00) 		0.217
(1.00)
5q gain 0 (0%) 150 0.37
(1.00) 		0.314
(1.00) 		0.056
(1.00) 		0.0414
(1.00) 		0.066
(1.00) 		0.271
(1.00) 		0.182
(1.00) 		0.431
(1.00) 		0.397
(1.00) 		0.447
(1.00) 		0.0262
(1.00) 		0.117
(1.00)
8p gain 0 (0%) 135 0.475
(1.00) 		0.421
(1.00) 		0.00775
(1.00) 		0.704
(1.00) 		0.358
(1.00) 		0.0544
(1.00) 		0.909
(1.00) 		0.885
(1.00) 		0.94
(1.00) 		0.499
(1.00) 		0.385
(1.00) 		0.368
(1.00)
9p gain 0 (0%) 127 0.0304
(1.00) 		0.191
(1.00) 		0.482
(1.00) 		0.0824
(1.00) 		0.894
(1.00) 		0.178
(1.00) 		0.608
(1.00) 		0.0746
(1.00) 		0.153
(1.00) 		0.712
(1.00) 		0.0639
(1.00) 		0.346
(1.00)
9q gain 0 (0%) 136 0.0368
(1.00) 		0.195
(1.00) 		0.135
(1.00) 		0.541
(1.00) 		0.803
(1.00) 		0.21
(1.00) 		0.722
(1.00) 		0.668
(1.00) 		0.306
(1.00) 		0.465
(1.00) 		0.172
(1.00) 		0.51
(1.00)
10p gain 0 (0%) 154 1
(1.00) 		0.436
(1.00) 		0.245
(1.00) 		0.475
(1.00) 		0.727
(1.00) 		0.558
(1.00) 		0.391
(1.00) 		0.87
(1.00)
10q gain 0 (0%) 158 0.444
(1.00) 		1
(1.00) 		0.927
(1.00) 		0.631
(1.00) 		0.788
(1.00)
11p gain 0 (0%) 141 0.0498
(1.00) 		0.0646
(1.00) 		0.0813
(1.00) 		0.209
(1.00) 		0.436
(1.00) 		0.72
(1.00) 		0.216
(1.00) 		0.0835
(1.00) 		0.464
(1.00) 		0.15
(1.00) 		0.0584
(1.00) 		0.042
(1.00)
11q gain 0 (0%) 145 0.127
(1.00) 		0.0799
(1.00) 		0.00131
(1.00) 		0.0118
(1.00) 		0.784
(1.00) 		0.42
(1.00) 		0.324
(1.00) 		0.253
(1.00) 		0.936
(1.00) 		0.796
(1.00) 		0.213
(1.00) 		0.73
(1.00)
12p gain 0 (0%) 134 0.815
(1.00) 		0.753
(1.00) 		0.0201
(1.00) 		0.74
(1.00) 		0.389
(1.00) 		0.0598
(1.00) 		0.642
(1.00) 		0.191
(1.00) 		0.297
(1.00) 		0.164
(1.00) 		0.494
(1.00) 		0.151
(1.00)
12q gain 0 (0%) 143 1
(1.00) 		0.602
(1.00) 		0.32
(1.00) 		0.449
(1.00) 		0.451
(1.00) 		0.0778
(1.00) 		0.501
(1.00) 		0.23
(1.00) 		0.342
(1.00) 		0.485
(1.00) 		0.333
(1.00) 		0.161
(1.00)
14q gain 0 (0%) 158 0.708
(1.00) 		0.678
(1.00) 		0.463
(1.00) 		0.494
(1.00) 		0.111
(1.00) 		0.19
(1.00)
16p gain 0 (0%) 133 0.208
(1.00) 		0.766
(1.00) 		0.0101
(1.00) 		0.673
(1.00) 		0.784
(1.00) 		0.681
(1.00) 		0.941
(1.00) 		0.903
(1.00) 		0.449
(1.00) 		0.332
(1.00) 		0.0501
(1.00) 		0.106
(1.00)
17p gain 0 (0%) 159 0.111
(1.00)
17q gain 0 (0%) 144 0.239
(1.00) 		0.31
(1.00) 		0.0397
(1.00) 		0.904
(1.00) 		0.57
(1.00) 		0.825
(1.00) 		0.208
(1.00) 		0.0334
(1.00) 		0.409
(1.00) 		0.31
(1.00) 		0.132
(1.00) 		0.257
(1.00)
18p gain 0 (0%) 155 0.517
(1.00) 		1
(1.00) 		0.118
(1.00) 		0.363
(1.00) 		1
(1.00) 		0.792
(1.00) 		0.757
(1.00) 		1
(1.00) 		1
(1.00) 		0.733
(1.00)
18q gain 0 (0%) 158 0.708
(1.00) 		0.181
(1.00) 		1
(1.00) 		0.792
(1.00) 		0.735
(1.00) 		0.501
(1.00) 		1
(1.00) 		0.733
(1.00)
19p gain 0 (0%) 142 0.00448
(1.00) 		0.0565
(1.00) 		0.00524
(1.00) 		0.759
(1.00) 		0.799
(1.00) 		0.609
(1.00) 		0.377
(1.00) 		0.431
(1.00) 		0.357
(1.00) 		0.232
(1.00) 		0.205
(1.00) 		0.255
(1.00)
19q gain 0 (0%) 139 0.0122
(1.00) 		0.118
(1.00) 		0.00568
(1.00) 		0.558
(1.00) 		0.258
(1.00) 		0.181
(1.00) 		0.476
(1.00) 		0.779
(1.00) 		0.407
(1.00) 		0.788
(1.00) 		0.564
(1.00) 		0.853
(1.00)
20p gain 0 (0%) 67 0.333
(1.00) 		0.217
(1.00) 		0.147
(1.00) 		0.641
(1.00) 		0.0715
(1.00) 		0.662
(1.00) 		0.275
(1.00) 		0.58
(1.00) 		0.577
(1.00) 		0.19
(1.00) 		0.666
(1.00) 		0.389
(1.00)
21q gain 0 (0%) 155 0.769
(1.00) 		0.0107
(1.00) 		0.467
(1.00) 		0.817
(1.00) 		0.728
(1.00) 		0.61
(1.00) 		0.496
(1.00) 		0.447
(1.00) 		0.307
(1.00) 		1
(1.00) 		0.119
(1.00) 		0.313
(1.00)
22q gain 0 (0%) 157 0.297
(1.00) 		0.24
(1.00) 		0.563
(1.00) 		0.318
(1.00) 		0.0654
(1.00) 		0.631
(1.00) 		0.788
(1.00)
Xq gain 0 (0%) 155 1
(1.00) 		0.82
(1.00) 		0.591
(1.00) 		1
(1.00) 		0.762
(1.00) 		0.701
(1.00) 		0.744
(1.00) 		0.87
(1.00) 		0.778
(1.00) 		0.733
(1.00)
1p loss 0 (0%) 141 0.745
(1.00) 		0.14
(1.00) 		0.0239
(1.00) 		0.426
(1.00) 		0.474
(1.00) 		0.154
(1.00) 		0.453
(1.00) 		0.191
(1.00) 		0.387
(1.00) 		0.463
(1.00) 		0.966
(1.00) 		0.0667
(1.00)
1q loss 0 (0%) 155 0.832
(1.00) 		0.542
(1.00) 		0.536
(1.00) 		0.612
(1.00) 		0.57
(1.00) 		0.752
(1.00) 		0.52
(1.00) 		0.87
(1.00) 		0.594
(1.00) 		0.379
(1.00)
2p loss 0 (0%) 157 0.511
(1.00) 		0.28
(1.00) 		0.413
(1.00) 		0.682
(1.00) 		0.12
(1.00) 		0.0965
(1.00) 		0.0741
(1.00)
3p loss 0 (0%) 154 1
(1.00) 		0.208
(1.00) 		0.486
(1.00) 		0.457
(1.00) 		0.807
(1.00) 		0.707
(1.00) 		0.754
(1.00) 		0.775
(1.00) 		0.236
(1.00) 		0.693
(1.00)
3q loss 0 (0%) 158 0.463
(1.00) 		1
(1.00) 		0.293
(1.00) 		0.413
(1.00) 		1
(1.00)
4p loss 0 (0%) 123 0.61
(1.00) 		0.431
(1.00) 		0.002
(1.00) 		0.781
(1.00) 		0.633
(1.00) 		0.468
(1.00) 		0.395
(1.00) 		0.019
(1.00) 		0.783
(1.00) 		1
(1.00) 		0.965
(1.00) 		0.805
(1.00)
4q loss 0 (0%) 116 0.845
(1.00) 		0.315
(1.00) 		0.000856
(0.677) 		0.399
(1.00) 		0.424
(1.00) 		0.491
(1.00) 		0.277
(1.00) 		0.0471
(1.00) 		0.885
(1.00) 		0.818
(1.00) 		0.955
(1.00) 		0.65
(1.00)
5p loss 0 (0%) 151 0.183
(1.00) 		0.229
(1.00) 		0.745
(1.00) 		0.274
(1.00) 		0.903
(1.00) 		0.712
(1.00) 		0.376
(1.00) 		0.779
(1.00) 		0.438
(1.00) 		0.127
(1.00) 		0.014
(1.00) 		0.0905
(1.00)
5q loss 0 (0%) 139 0.0238
(1.00) 		0.0169
(1.00) 		0.204
(1.00) 		0.167
(1.00) 		0.643
(1.00) 		0.812
(1.00) 		0.344
(1.00) 		0.347
(1.00) 		0.774
(1.00) 		0.16
(1.00) 		0.334
(1.00) 		0.327
(1.00)
6p loss 0 (0%) 154 0.316
(1.00) 		0.82
(1.00) 		0.572
(1.00) 		0.013
(1.00) 		0.0971
(1.00) 		0.705
(1.00) 		0.668
(1.00) 		1
(1.00) 		0.757
(1.00) 		0.87
(1.00) 		1
(1.00) 		0.615
(1.00)
6q loss 0 (0%) 148 0.745
(1.00) 		0.31
(1.00) 		0.269
(1.00) 		0.367
(1.00) 		0.31
(1.00) 		0.843
(1.00) 		0.479
(1.00) 		0.534
(1.00) 		0.186
(1.00) 		0.135
(1.00) 		0.397
(1.00) 		0.365
(1.00)
8p loss 0 (0%) 106 0.457
(1.00) 		0.702
(1.00) 		0.0258
(1.00) 		0.609
(1.00) 		0.511
(1.00) 		0.603
(1.00) 		0.123
(1.00) 		0.93
(1.00) 		0.716
(1.00) 		0.102
(1.00) 		0.708
(1.00) 		0.0729
(1.00)
8q loss 0 (0%) 159 0.486
(1.00) 		0.785
(1.00) 		0.927
(1.00) 		0.527
(1.00) 		1
(1.00)
9p loss 0 (0%) 150 0.866
(1.00) 		0.865
(1.00) 		0.101
(1.00) 		0.413
(1.00) 		0.784
(1.00) 		0.593
(1.00) 		0.965
(1.00) 		1
(1.00) 		0.878
(1.00) 		0.329
(1.00)
9q loss 0 (0%) 151 0.544
(1.00) 		0.888
(1.00) 		0.265
(1.00) 		1
(1.00) 		0.614
(1.00) 		0.694
(1.00) 		0.645
(1.00) 		0.317
(1.00) 		1
(1.00) 		0.712
(1.00)
10p loss 0 (0%) 145 0.0429
(1.00) 		0.0428
(1.00) 		0.353
(1.00) 		0.558
(1.00) 		0.0351
(1.00) 		0.118
(1.00) 		0.485
(1.00) 		1
(1.00) 		0.91
(1.00) 		1
(1.00) 		0.723
(1.00) 		0.571
(1.00)
10q loss 0 (0%) 141 0.132
(1.00) 		0.346
(1.00) 		0.0692
(1.00) 		0.494
(1.00) 		0.0814
(1.00) 		0.282
(1.00) 		0.0476
(1.00) 		0.668
(1.00) 		0.97
(1.00) 		0.894
(1.00) 		0.403
(1.00) 		0.512
(1.00)
11p loss 0 (0%) 143 0.116
(1.00) 		0.441
(1.00) 		0.0303
(1.00) 		0.091
(1.00) 		0.158
(1.00) 		0.0724
(1.00) 		0.187
(1.00) 		0.0658
(1.00) 		0.856
(1.00) 		0.661
(1.00) 		0.872
(1.00) 		0.284
(1.00)
11q loss 0 (0%) 139 0.116
(1.00) 		0.441
(1.00) 		0.0221
(1.00) 		0.118
(1.00) 		0.304
(1.00) 		0.126
(1.00) 		0.14
(1.00) 		0.00666
(1.00) 		0.101
(1.00) 		0.221
(1.00) 		0.216
(1.00) 		0.111
(1.00)
12p loss 0 (0%) 150 0.729
(1.00) 		0.84
(1.00) 		0.331
(1.00) 		0.00876
(1.00) 		0.405
(1.00) 		0.0638
(1.00) 		0.871
(1.00) 		0.535
(1.00) 		0.247
(1.00) 		0.374
(1.00)
12q loss 0 (0%) 154 1
(1.00) 		0.601
(1.00) 		0.541
(1.00) 		0.367
(1.00) 		0.368
(1.00) 		0.541
(1.00) 		0.483
(1.00) 		0.57
(1.00) 		0.581
(1.00) 		0.195
(1.00)
13q loss 0 (0%) 157 0.092
(1.00) 		0.111
(1.00) 		0.133
(1.00) 		0.608
(1.00) 		0.858
(1.00) 		1
(1.00) 		0.345
(1.00) 		0.843
(1.00) 		0.268
(1.00) 		0.336
(1.00)
14q loss 0 (0%) 111 0.62
(1.00) 		0.305
(1.00) 		0.000406
(0.321) 		1
(1.00) 		0.545
(1.00) 		0.176
(1.00) 		0.659
(1.00) 		0.561
(1.00) 		0.965
(1.00) 		0.371
(1.00) 		0.395
(1.00) 		0.49
(1.00)
15q loss 0 (0%) 104 0.618
(1.00) 		0.0406
(1.00) 		0.00229
(1.00) 		0.645
(1.00) 		0.156
(1.00) 		0.153
(1.00) 		0.636
(1.00) 		0.81
(1.00) 		0.566
(1.00) 		0.714
(1.00) 		0.0721
(1.00) 		0.0544
(1.00)
16p loss 0 (0%) 156 0.297
(1.00) 		0.24
(1.00) 		0.225
(1.00) 		0.475
(1.00) 		0.878
(1.00) 		0.505
(1.00) 		0.858
(1.00) 		0.447
(1.00) 		0.568
(1.00) 		1
(1.00) 		1
(1.00) 		0.733
(1.00)
16q loss 0 (0%) 152 0.0381
(1.00) 		0.155
(1.00) 		0.00539
(1.00) 		0.516
(1.00) 		0.807
(1.00) 		0.501
(1.00) 		0.747
(1.00) 		0.17
(1.00) 		0.612
(1.00) 		0.603
(1.00) 		1
(1.00) 		0.727
(1.00)
17p loss 0 (0%) 73 0.866
(1.00) 		0.905
(1.00) 		0.252
(1.00) 		0.806
(1.00) 		0.763
(1.00) 		0.458
(1.00) 		0.141
(1.00) 		0.0437
(1.00) 		0.615
(1.00) 		0.893
(1.00) 		0.351
(1.00) 		0.215
(1.00)
17q loss 0 (0%) 147 0.36
(1.00) 		0.436
(1.00) 		0.303
(1.00) 		0.443
(1.00) 		0.614
(1.00) 		0.555
(1.00) 		0.67
(1.00) 		0.305
(1.00) 		0.336
(1.00) 		0.736
(1.00) 		0.0292
(1.00) 		0.00971
(1.00)
19p loss 0 (0%) 155 0.265
(1.00) 		0.427
(1.00) 		0.368
(1.00) 		0.424
(1.00) 		0.209
(1.00) 		1
(1.00) 		0.143
(1.00) 		0.141
(1.00) 		0.878
(1.00) 		0.633
(1.00)
19q loss 0 (0%) 155 0.494
(1.00) 		0.1
(1.00) 		0.571
(1.00) 		0.568
(1.00) 		0.371
(1.00) 		1
(1.00) 		0.701
(1.00) 		0.351
(1.00) 		1
(1.00) 		0.402
(1.00)
20p loss 0 (0%) 144 1
(1.00) 		0.917
(1.00) 		0.162
(1.00) 		0.645
(1.00) 		0.319
(1.00) 		0.777
(1.00) 		0.747
(1.00) 		0.534
(1.00) 		0.0305
(1.00) 		0.0916
(1.00) 		0.0738
(1.00) 		0.248
(1.00)
21q loss 0 (0%) 119 0.936
(1.00) 		0.815
(1.00) 		0.25
(1.00) 		0.807
(1.00) 		0.328
(1.00) 		0.625
(1.00) 		0.867
(1.00) 		0.543
(1.00) 		0.099
(1.00) 		0.663
(1.00) 		0.531
(1.00) 		0.145
(1.00)
22q loss 0 (0%) 120 0.833
(1.00) 		0.324
(1.00) 		0.5
(1.00) 		0.166
(1.00) 		0.455
(1.00) 		0.457
(1.00) 		0.475
(1.00) 		0.362
(1.00) 		0.119
(1.00) 		0.788
(1.00) 		0.86
(1.00) 		0.158
(1.00)
Xq loss 0 (0%) 156 0.769
(1.00) 		0.601
(1.00) 		0.283
(1.00) 		0.475
(1.00) 		0.855
(1.00) 		0.739
(1.00) 		0.843
(1.00) 		0.843
(1.00) 		0.594
(1.00) 		0.336
(1.00)
'6p gain' versus 'CN_CNMF'

P value = 3.03e-05 (Chi-square test), Q value = 0.024

Table S1.  Gene #11: '6p gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
6P GAIN CNV 4 11 4 2 6
6P GAIN WILD-TYPE 3 21 42 53 16

Figure S1.  Get High-res Image Gene #11: '6p gain' versus Molecular Subtype #3: 'CN_CNMF'

'6q gain' versus 'CN_CNMF'

P value = 7.74e-05 (Chi-square test), Q value = 0.062

Table S2.  Gene #12: '6q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
6Q GAIN CNV 3 12 4 2 6
6Q GAIN WILD-TYPE 4 20 42 53 16

Figure S2.  Get High-res Image Gene #12: '6q gain' versus Molecular Subtype #3: 'CN_CNMF'

'7p gain' versus 'CN_CNMF'

P value = 1.28e-06 (Chi-square test), Q value = 0.001

Table S3.  Gene #13: '7p gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
7P GAIN CNV 5 29 26 16 14
7P GAIN WILD-TYPE 2 3 20 39 8

Figure S3.  Get High-res Image Gene #13: '7p gain' versus Molecular Subtype #3: 'CN_CNMF'

'7q gain' versus 'CN_CNMF'

P value = 6.47e-07 (Chi-square test), Q value = 0.00052

Table S4.  Gene #14: '7q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
7Q GAIN CNV 3 27 23 12 14
7Q GAIN WILD-TYPE 4 5 23 43 8

Figure S4.  Get High-res Image Gene #14: '7q gain' versus Molecular Subtype #3: 'CN_CNMF'

'8q gain' versus 'CN_CNMF'

P value = 3.13e-08 (Chi-square test), Q value = 2.5e-05

Table S5.  Gene #16: '8q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
8Q GAIN CNV 1 15 37 11 8
8Q GAIN WILD-TYPE 6 17 9 44 14

Figure S5.  Get High-res Image Gene #16: '8q gain' versus Molecular Subtype #3: 'CN_CNMF'

'13q gain' versus 'CN_CNMF'

P value = 1.28e-07 (Chi-square test), Q value = 1e-04

Table S6.  Gene #25: '13q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
13Q GAIN CNV 5 29 27 20 21
13Q GAIN WILD-TYPE 2 3 19 35 1

Figure S6.  Get High-res Image Gene #25: '13q gain' versus Molecular Subtype #3: 'CN_CNMF'

'13q gain' versus 'MIRSEQ_MATURE_CNMF'

P value = 5.42e-05 (Fisher's exact test), Q value = 0.043

Table S7.  Gene #25: '13q gain' versus Molecular Subtype #11: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 14 19 27 14
13Q GAIN CNV 5 18 23 6
13Q GAIN WILD-TYPE 9 1 4 8

Figure S7.  Get High-res Image Gene #25: '13q gain' versus Molecular Subtype #11: 'MIRSEQ_MATURE_CNMF'

'16q gain' versus 'CN_CNMF'

P value = 0.000114 (Chi-square test), Q value = 0.091

Table S8.  Gene #28: '16q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
16Q GAIN CNV 3 11 7 1 8
16Q GAIN WILD-TYPE 4 21 39 54 14

Figure S8.  Get High-res Image Gene #28: '16q gain' versus Molecular Subtype #3: 'CN_CNMF'

'20q gain' versus 'CN_CNMF'

P value = 0.000265 (Chi-square test), Q value = 0.21

Table S9.  Gene #36: '20q gain' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
20Q GAIN CNV 7 32 36 36 21
20Q GAIN WILD-TYPE 0 0 10 19 1

Figure S9.  Get High-res Image Gene #36: '20q gain' versus Molecular Subtype #3: 'CN_CNMF'

'18p loss' versus 'CN_CNMF'

P value = 1.17e-05 (Chi-square test), Q value = 0.0093

Table S10.  Gene #68: '18p loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
18P LOSS CNV 7 31 34 26 16
18P LOSS WILD-TYPE 0 1 12 29 6

Figure S10.  Get High-res Image Gene #68: '18p loss' versus Molecular Subtype #3: 'CN_CNMF'

'18q loss' versus 'CN_CNMF'

P value = 9.16e-07 (Chi-square test), Q value = 0.00073

Table S11.  Gene #69: '18q loss' versus Molecular Subtype #3: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 7 32 46 55 22
18Q LOSS CNV 7 32 40 29 18
18Q LOSS WILD-TYPE 0 0 6 26 4

Figure S11.  Get High-res Image Gene #69: '18q loss' versus Molecular Subtype #3: 'CN_CNMF'

Methods & Data
Input

  • Mutation data file = broad_values_by_arm.mutsig.cluster.txt

  • Molecular subtypes file = READ-TP.transferedmergedcluster.txt

  • Number of patients = 162

  • Number of significantly arm-level cnvs = 75

  • Number of molecular subtypes = 12

  • Exclude genes that fewer than K tumors have mutations, K = 3

Fisher's exact test

For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[2] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[3] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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