Rectum Adenocarcinoma: Correlation between copy number variations of arm-level result and molecular subtypes
(primary solid tumor cohort)
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
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 10 molecular subtypes across 162 patients, 10 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'.

  • 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 10 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 10 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
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
6p gain 27 (17%) 135 0.432
(1.00)
0.263
(1.00)
3.03e-05
(0.0202)
0.00823
(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)
6q gain 27 (17%) 135 0.432
(1.00)
0.263
(1.00)
7.74e-05
(0.0516)
0.0597
(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)
7p gain 90 (56%) 72 0.00256
(1.00)
0.00222
(1.00)
1.28e-06
(0.000856)
0.0155
(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)
7q gain 79 (49%) 83 0.00631
(1.00)
0.00494
(1.00)
6.47e-07
(0.000434)
0.0926
(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)
8q gain 72 (44%) 90 0.00878
(1.00)
0.019
(1.00)
3.13e-08
(2.11e-05)
0.523
(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)
13q gain 102 (63%) 60 0.225
(1.00)
0.175
(1.00)
1.28e-07
(8.6e-05)
0.362
(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)
16q gain 30 (19%) 132 0.356
(1.00)
0.474
(1.00)
0.000114
(0.0761)
0.532
(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)
20q gain 132 (81%) 30 0.0858
(1.00)
0.0909
(1.00)
0.000265
(0.176)
0.226
(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)
18p loss 114 (70%) 48 0.0181
(1.00)
0.0163
(1.00)
1.17e-05
(0.00782)
0.425
(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)
18q loss 126 (78%) 36 0.0195
(1.00)
0.0148
(1.00)
9.16e-07
(0.000614)
0.37
(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)
1p gain 4 (2%) 158 0.708
(1.00)
0.481
(1.00)
0.616
(1.00)
0.108
(1.00)
0.15
(1.00)
0.748
(1.00)
1q gain 27 (17%) 135 0.724
(1.00)
0.923
(1.00)
0.0332
(1.00)
0.16
(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)
2p gain 20 (12%) 142 0.197
(1.00)
0.427
(1.00)
0.157
(1.00)
0.697
(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)
2q gain 21 (13%) 141 0.0305
(1.00)
0.0789
(1.00)
0.701
(1.00)
0.797
(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)
3p gain 17 (10%) 145 0.833
(1.00)
0.671
(1.00)
0.0252
(1.00)
0.605
(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)
3q gain 23 (14%) 139 1
(1.00)
0.808
(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)
4p gain 4 (2%) 158 0.391
(1.00)
1
(1.00)
0.463
(1.00)
0.587
(1.00)
0.366
(1.00)
0.501
(1.00)
4q gain 3 (2%) 159 0.286
(1.00)
0.476
(1.00)
0.515
(1.00)
0.78
(1.00)
0.512
(1.00)
5p gain 20 (12%) 142 0.832
(1.00)
1
(1.00)
0.072
(1.00)
0.108
(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)
5q gain 12 (7%) 150 0.543
(1.00)
0.418
(1.00)
0.056
(1.00)
0.0444
(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)
8p gain 27 (17%) 135 0.631
(1.00)
0.238
(1.00)
0.00775
(1.00)
0.662
(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)
9p gain 35 (22%) 127 0.0354
(1.00)
0.201
(1.00)
0.482
(1.00)
0.0493
(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)
9q gain 26 (16%) 136 0.0426
(1.00)
0.271
(1.00)
0.135
(1.00)
0.44
(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)
10p gain 8 (5%) 154 0.309
(1.00)
0.515
(1.00)
0.245
(1.00)
0.481
(1.00)
0.727
(1.00)
0.558
(1.00)
0.391
(1.00)
0.87
(1.00)
10q gain 4 (2%) 158 0.444
(1.00)
1
(1.00)
0.927
(1.00)
0.631
(1.00)
0.788
(1.00)
11p gain 21 (13%) 141 0.0363
(1.00)
0.0609
(1.00)
0.0813
(1.00)
0.333
(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)
11q gain 17 (10%) 145 0.0848
(1.00)
0.14
(1.00)
0.00131
(0.867)
0.024
(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)
12p gain 28 (17%) 134 0.582
(1.00)
0.381
(1.00)
0.0201
(1.00)
0.797
(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)
12q gain 19 (12%) 143 1
(1.00)
1
(1.00)
0.32
(1.00)
0.458
(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)
14q gain 4 (2%) 158 0.708
(1.00)
0.176
(1.00)
0.463
(1.00)
0.494
(1.00)
0.111
(1.00)
0.19
(1.00)
16p gain 29 (18%) 133 0.245
(1.00)
0.356
(1.00)
0.0101
(1.00)
0.455
(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)
17p gain 3 (2%) 159 0.111
(1.00)
17q gain 18 (11%) 144 0.189
(1.00)
0.606
(1.00)
0.0397
(1.00)
0.909
(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)
18p gain 7 (4%) 155 0.282
(1.00)
0.774
(1.00)
0.118
(1.00)
0.299
(1.00)
1
(1.00)
0.792
(1.00)
0.757
(1.00)
1
(1.00)
18q gain 4 (2%) 158 0.708
(1.00)
0.113
(1.00)
1
(1.00)
0.792
(1.00)
0.735
(1.00)
0.501
(1.00)
19p gain 20 (12%) 142 0.00723
(1.00)
0.115
(1.00)
0.00524
(1.00)
0.703
(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)
19q gain 23 (14%) 139 0.0202
(1.00)
0.0827
(1.00)
0.00568
(1.00)
0.614
(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)
20p gain 95 (59%) 67 0.717
(1.00)
0.466
(1.00)
0.147
(1.00)
0.619
(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)
21q gain 7 (4%) 155 0.502
(1.00)
0.107
(1.00)
0.467
(1.00)
0.818
(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)
22q gain 5 (3%) 157 0.191
(1.00)
0.107
(1.00)
0.563
(1.00)
0.318
(1.00)
0.0654
(1.00)
0.631
(1.00)
0.788
(1.00)
Xq gain 7 (4%) 155 1
(1.00)
0.829
(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)
1p loss 21 (13%) 141 0.604
(1.00)
1
(1.00)
0.0239
(1.00)
0.395
(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)
1q loss 7 (4%) 155 0.833
(1.00)
0.671
(1.00)
0.536
(1.00)
0.622
(1.00)
0.57
(1.00)
0.752
(1.00)
0.52
(1.00)
0.87
(1.00)
2p loss 5 (3%) 157 0.426
(1.00)
0.62
(1.00)
0.413
(1.00)
0.682
(1.00)
0.12
(1.00)
0.0965
(1.00)
0.0741
(1.00)
3p loss 8 (5%) 154 1
(1.00)
1
(1.00)
0.486
(1.00)
0.461
(1.00)
0.807
(1.00)
0.707
(1.00)
0.754
(1.00)
0.775
(1.00)
3q loss 4 (2%) 158 0.463
(1.00)
1
(1.00)
0.293
(1.00)
0.413
(1.00)
1
(1.00)
4p loss 39 (24%) 123 0.365
(1.00)
0.94
(1.00)
0.002
(1.00)
0.867
(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)
4q loss 46 (28%) 116 0.591
(1.00)
1
(1.00)
0.000856
(0.566)
0.841
(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)
5p loss 11 (7%) 151 0.151
(1.00)
0.25
(1.00)
0.745
(1.00)
0.368
(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)
5q loss 23 (14%) 139 0.0173
(1.00)
0.0609
(1.00)
0.204
(1.00)
0.108
(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)
6p loss 8 (5%) 154 0.38
(1.00)
0.829
(1.00)
0.572
(1.00)
0.0133
(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)
6q loss 14 (9%) 148 1
(1.00)
0.606
(1.00)
0.269
(1.00)
0.268
(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)
8p loss 56 (35%) 106 0.642
(1.00)
0.373
(1.00)
0.0258
(1.00)
0.697
(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)
8q loss 3 (2%) 159 0.486
(1.00)
0.785
(1.00)
0.927
(1.00)
0.527
(1.00)
1
(1.00)
9p loss 12 (7%) 150 0.662
(1.00)
0.758
(1.00)
0.101
(1.00)
0.883
(1.00)
0.784
(1.00)
0.593
(1.00)
0.965
(1.00)
1
(1.00)
9q loss 11 (7%) 151 0.607
(1.00)
0.315
(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)
10p loss 17 (10%) 145 0.0447
(1.00)
0.0149
(1.00)
0.353
(1.00)
0.614
(1.00)
0.0351
(1.00)
0.118
(1.00)
0.485
(1.00)
1
(1.00)
0.91
(1.00)
1
(1.00)
10q loss 21 (13%) 141 0.136
(1.00)
0.317
(1.00)
0.0692
(1.00)
0.582
(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)
11p loss 19 (12%) 143 0.0628
(1.00)
0.131
(1.00)
0.0303
(1.00)
0.0434
(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)
11q loss 23 (14%) 139 0.0628
(1.00)
0.131
(1.00)
0.0221
(1.00)
0.117
(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)
12p loss 12 (7%) 150 0.731
(1.00)
0.848
(1.00)
0.331
(1.00)
0.0753
(1.00)
0.405
(1.00)
0.0638
(1.00)
0.871
(1.00)
0.535
(1.00)
12q loss 8 (5%) 154 1
(1.00)
0.625
(1.00)
0.541
(1.00)
1
(1.00)
0.368
(1.00)
0.541
(1.00)
0.483
(1.00)
0.57
(1.00)
13q loss 5 (3%) 157 0.092
(1.00)
0.186
(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)
14q loss 51 (31%) 111 0.805
(1.00)
0.252
(1.00)
0.000406
(0.269)
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)
15q loss 58 (36%) 104 0.785
(1.00)
0.227
(1.00)
0.00229
(1.00)
0.45
(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)
16p loss 6 (4%) 156 0.191
(1.00)
0.488
(1.00)
0.225
(1.00)
0.481
(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)
16q loss 10 (6%) 152 0.0202
(1.00)
0.12
(1.00)
0.00539
(1.00)
0.441
(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)
17p loss 89 (55%) 73 0.866
(1.00)
1
(1.00)
0.252
(1.00)
0.606
(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)
17q loss 15 (9%) 147 0.614
(1.00)
0.362
(1.00)
0.303
(1.00)
0.134
(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)
19p loss 7 (4%) 155 0.265
(1.00)
0.368
(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)
19q loss 7 (4%) 155 0.494
(1.00)
0.105
(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)
20p loss 18 (11%) 144 1
(1.00)
1
(1.00)
0.162
(1.00)
0.65
(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)
21q loss 43 (27%) 119 0.879
(1.00)
0.676
(1.00)
0.25
(1.00)
0.596
(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)
22q loss 42 (26%) 120 0.887
(1.00)
0.739
(1.00)
0.5
(1.00)
0.311
(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)
Xq loss 6 (4%) 156 0.771
(1.00)
0.37
(1.00)
0.283
(1.00)
0.481
(1.00)
0.855
(1.00)
0.739
(1.00)
0.843
(1.00)
0.843
(1.00)
'6p gain mutation analysis' versus 'CN_CNMF'

P value = 3.03e-05 (Chi-square test), Q value = 0.02

Table S1.  Gene #11: '6p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S1.  Get High-res Image Gene #11: '6p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'6q gain mutation analysis' versus 'CN_CNMF'

P value = 7.74e-05 (Chi-square test), Q value = 0.052

Table S2.  Gene #12: '6q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S2.  Get High-res Image Gene #12: '6q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'7p gain mutation analysis' versus 'CN_CNMF'

P value = 1.28e-06 (Chi-square test), Q value = 0.00086

Table S3.  Gene #13: '7p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S3.  Get High-res Image Gene #13: '7p gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'7q gain mutation analysis' versus 'CN_CNMF'

P value = 6.47e-07 (Chi-square test), Q value = 0.00043

Table S4.  Gene #14: '7q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S4.  Get High-res Image Gene #14: '7q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'8q gain mutation analysis' versus 'CN_CNMF'

P value = 3.13e-08 (Chi-square test), Q value = 2.1e-05

Table S5.  Gene #16: '8q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S5.  Get High-res Image Gene #16: '8q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'13q gain mutation analysis' versus 'CN_CNMF'

P value = 1.28e-07 (Chi-square test), Q value = 8.6e-05

Table S6.  Gene #25: '13q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S6.  Get High-res Image Gene #25: '13q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'16q gain mutation analysis' versus 'CN_CNMF'

P value = 0.000114 (Chi-square test), Q value = 0.076

Table S7.  Gene #28: '16q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S7.  Get High-res Image Gene #28: '16q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'20q gain mutation analysis' versus 'CN_CNMF'

P value = 0.000265 (Chi-square test), Q value = 0.18

Table S8.  Gene #36: '20q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S8.  Get High-res Image Gene #36: '20q gain mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'18p loss mutation analysis' versus 'CN_CNMF'

P value = 1.17e-05 (Chi-square test), Q value = 0.0078

Table S9.  Gene #68: '18p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S9.  Get High-res Image Gene #68: '18p loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

'18q loss mutation analysis' versus 'CN_CNMF'

P value = 9.16e-07 (Chi-square test), Q value = 0.00061

Table S10.  Gene #69: '18q loss mutation analysis' versus Clinical Feature #3: 'CN_CNMF'

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

Figure S10.  Get High-res Image Gene #69: '18q loss mutation analysis' versus Clinical Feature #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 = 10

  • 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)