Correlation between copy number variations of arm-level result and molecular subtypes
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between copy number variations of arm-level result and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1F47MJR
Overview
Introduction

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

Summary

Testing the association between copy number variation 80 arm-level events and 8 molecular subtypes across 171 patients, 8 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 2p gain cnv correlated to 'CN_CNMF'.

  • 5p gain cnv correlated to 'CN_CNMF'.

  • 19q gain cnv correlated to 'CN_CNMF'.

  • 3p loss cnv correlated to 'MIRSEQ_MATURE_CNMF'.

  • 4p loss cnv correlated to 'CN_CNMF'.

  • 4q loss cnv correlated to 'CN_CNMF'.

  • 5q loss cnv correlated to 'CN_CNMF'.

  • 17p 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 80 arm-level events and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 8 significant findings detected.

Clinical
Features
CN
CNMF
METHLYATION
CNMF
MRNASEQ
CNMF
MRNASEQ
CHIERARCHICAL
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRSEQ
MATURE
CNMF
MIRSEQ
MATURE
CHIERARCHICAL
nCNV (%) nWild-Type Fisher's exact test Chi-square test Chi-square test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
2p gain 34 (20%) 137 0.000129
(0.082)
0.00973
(1.00)
0.166
(1.00)
0.106
(1.00)
0.0136
(1.00)
0.0253
(1.00)
0.233
(1.00)
0.123
(1.00)
5p gain 60 (35%) 111 7.51e-07
(0.000479)
0.0472
(1.00)
0.845
(1.00)
0.0946
(1.00)
0.272
(1.00)
0.28
(1.00)
0.473
(1.00)
0.125
(1.00)
19q gain 40 (23%) 131 9.2e-08
(5.89e-05)
0.0783
(1.00)
0.694
(1.00)
0.185
(1.00)
0.0221
(1.00)
0.68
(1.00)
0.0335
(1.00)
0.421
(1.00)
3p loss 44 (26%) 127 0.434
(1.00)
0.00966
(1.00)
0.0414
(1.00)
0.00103
(0.641)
0.000944
(0.589)
0.0171
(1.00)
3.47e-06
(0.00221)
0.00249
(1.00)
4p loss 68 (40%) 103 5.38e-06
(0.00342)
0.132
(1.00)
0.462
(1.00)
0.000423
(0.267)
0.286
(1.00)
0.198
(1.00)
0.289
(1.00)
0.13
(1.00)
4q loss 46 (27%) 125 4.59e-07
(0.000293)
0.537
(1.00)
0.405
(1.00)
0.0682
(1.00)
0.603
(1.00)
0.0297
(1.00)
0.546
(1.00)
0.887
(1.00)
5q loss 30 (18%) 141 0.000232
(0.147)
0.244
(1.00)
0.948
(1.00)
1
(1.00)
0.671
(1.00)
0.787
(1.00)
0.588
(1.00)
0.331
(1.00)
17p loss 49 (29%) 122 2.98e-05
(0.0189)
0.528
(1.00)
0.0489
(1.00)
0.197
(1.00)
0.0272
(1.00)
0.244
(1.00)
0.00798
(1.00)
0.0191
(1.00)
1p gain 56 (33%) 115 0.738
(1.00)
0.0203
(1.00)
0.0278
(1.00)
0.0739
(1.00)
0.748
(1.00)
0.389
(1.00)
0.853
(1.00)
0.677
(1.00)
1q gain 82 (48%) 89 0.0955
(1.00)
0.012
(1.00)
0.116
(1.00)
0.29
(1.00)
0.883
(1.00)
0.371
(1.00)
0.528
(1.00)
1
(1.00)
2q gain 19 (11%) 152 0.00133
(0.824)
0.00674
(1.00)
0.0393
(1.00)
0.0125
(1.00)
0.0308
(1.00)
0.0196
(1.00)
0.147
(1.00)
0.128
(1.00)
3p gain 37 (22%) 134 0.771
(1.00)
0.0647
(1.00)
0.0136
(1.00)
0.206
(1.00)
0.0322
(1.00)
0.0417
(1.00)
0.0774
(1.00)
0.00081
(0.508)
3q gain 102 (60%) 69 0.00863
(1.00)
0.00634
(1.00)
0.00295
(1.00)
0.002
(1.00)
0.344
(1.00)
0.000604
(0.38)
0.216
(1.00)
0.000785
(0.493)
4p gain 5 (3%) 166 0.848
(1.00)
0.941
(1.00)
0.573
(1.00)
0.612
(1.00)
0.836
(1.00)
0.258
(1.00)
0.835
(1.00)
0.552
(1.00)
4q gain 6 (4%) 165 0.421
(1.00)
0.644
(1.00)
0.238
(1.00)
0.186
(1.00)
0.226
(1.00)
0.096
(1.00)
0.194
(1.00)
0.276
(1.00)
5q gain 27 (16%) 144 0.00657
(1.00)
0.34
(1.00)
0.514
(1.00)
0.937
(1.00)
0.325
(1.00)
0.0691
(1.00)
0.343
(1.00)
0.562
(1.00)
6p gain 30 (18%) 141 0.151
(1.00)
0.217
(1.00)
0.0945
(1.00)
0.0819
(1.00)
0.622
(1.00)
0.228
(1.00)
0.116
(1.00)
0.218
(1.00)
6q gain 18 (11%) 153 0.0951
(1.00)
0.598
(1.00)
0.0975
(1.00)
0.344
(1.00)
0.556
(1.00)
0.682
(1.00)
0.36
(1.00)
0.677
(1.00)
7p gain 18 (11%) 153 0.154
(1.00)
0.144
(1.00)
0.0446
(1.00)
0.535
(1.00)
0.0357
(1.00)
0.61
(1.00)
0.0247
(1.00)
0.152
(1.00)
7q gain 18 (11%) 153 0.085
(1.00)
0.433
(1.00)
0.362
(1.00)
0.134
(1.00)
0.0222
(1.00)
0.188
(1.00)
0.0166
(1.00)
0.31
(1.00)
8p gain 25 (15%) 146 0.841
(1.00)
0.728
(1.00)
0.0391
(1.00)
0.457
(1.00)
0.621
(1.00)
0.568
(1.00)
0.883
(1.00)
0.735
(1.00)
8q gain 44 (26%) 127 0.434
(1.00)
0.449
(1.00)
0.407
(1.00)
0.19
(1.00)
0.557
(1.00)
0.252
(1.00)
0.382
(1.00)
0.317
(1.00)
9p gain 27 (16%) 144 0.802
(1.00)
0.0873
(1.00)
0.223
(1.00)
0.013
(1.00)
1
(1.00)
0.306
(1.00)
0.928
(1.00)
0.615
(1.00)
9q gain 27 (16%) 144 0.571
(1.00)
0.01
(1.00)
0.0452
(1.00)
0.0021
(1.00)
0.508
(1.00)
0.111
(1.00)
0.71
(1.00)
0.184
(1.00)
10p gain 12 (7%) 159 0.682
(1.00)
0.752
(1.00)
0.429
(1.00)
1
(1.00)
0.932
(1.00)
0.823
(1.00)
1
(1.00)
1
(1.00)
10q gain 7 (4%) 164 0.606
(1.00)
0.656
(1.00)
0.449
(1.00)
0.394
(1.00)
0.793
(1.00)
0.493
(1.00)
0.175
(1.00)
0.0663
(1.00)
11p gain 6 (4%) 165 0.576
(1.00)
0.861
(1.00)
0.25
(1.00)
0.0155
(1.00)
0.876
(1.00)
0.188
(1.00)
1
(1.00)
0.249
(1.00)
11q gain 7 (4%) 164 0.0307
(1.00)
0.551
(1.00)
0.0401
(1.00)
0.000461
(0.291)
0.178
(1.00)
0.106
(1.00)
0.39
(1.00)
0.175
(1.00)
12p gain 27 (16%) 144 0.597
(1.00)
0.13
(1.00)
0.108
(1.00)
0.499
(1.00)
0.0482
(1.00)
0.498
(1.00)
0.0962
(1.00)
0.365
(1.00)
12q gain 28 (16%) 143 0.268
(1.00)
0.496
(1.00)
0.05
(1.00)
0.287
(1.00)
0.104
(1.00)
0.662
(1.00)
0.141
(1.00)
0.463
(1.00)
13q gain 14 (8%) 157 0.536
(1.00)
0.647
(1.00)
0.744
(1.00)
0.316
(1.00)
0.229
(1.00)
0.108
(1.00)
0.42
(1.00)
0.0313
(1.00)
14q gain 25 (15%) 146 0.582
(1.00)
0.887
(1.00)
0.14
(1.00)
0.458
(1.00)
0.199
(1.00)
0.163
(1.00)
0.198
(1.00)
0.424
(1.00)
15q gain 31 (18%) 140 0.888
(1.00)
0.589
(1.00)
0.735
(1.00)
0.428
(1.00)
0.37
(1.00)
0.522
(1.00)
0.465
(1.00)
0.968
(1.00)
16p gain 22 (13%) 149 0.146
(1.00)
0.258
(1.00)
0.245
(1.00)
0.166
(1.00)
0.0161
(1.00)
0.212
(1.00)
0.0675
(1.00)
0.323
(1.00)
16q gain 18 (11%) 153 0.414
(1.00)
0.0605
(1.00)
0.582
(1.00)
0.273
(1.00)
0.0069
(1.00)
0.0912
(1.00)
0.0445
(1.00)
0.32
(1.00)
17p gain 11 (6%) 160 0.108
(1.00)
0.177
(1.00)
0.215
(1.00)
0.251
(1.00)
0.0255
(1.00)
0.319
(1.00)
0.0608
(1.00)
0.261
(1.00)
17q gain 27 (16%) 144 0.31
(1.00)
0.0815
(1.00)
0.0838
(1.00)
0.0828
(1.00)
0.0239
(1.00)
0.321
(1.00)
0.0272
(1.00)
0.118
(1.00)
18p gain 21 (12%) 150 0.228
(1.00)
0.0583
(1.00)
0.0114
(1.00)
0.166
(1.00)
0.8
(1.00)
0.427
(1.00)
0.748
(1.00)
0.375
(1.00)
18q gain 13 (8%) 158 0.496
(1.00)
0.262
(1.00)
0.000909
(0.568)
0.685
(1.00)
0.311
(1.00)
0.749
(1.00)
0.606
(1.00)
0.523
(1.00)
19p gain 23 (13%) 148 0.22
(1.00)
0.00428
(1.00)
0.843
(1.00)
0.92
(1.00)
0.157
(1.00)
0.941
(1.00)
0.0197
(1.00)
0.288
(1.00)
20p gain 55 (32%) 116 0.00578
(1.00)
0.151
(1.00)
0.0704
(1.00)
0.0981
(1.00)
0.712
(1.00)
0.134
(1.00)
0.509
(1.00)
0.269
(1.00)
20q gain 62 (36%) 109 0.0507
(1.00)
0.024
(1.00)
0.506
(1.00)
0.154
(1.00)
0.732
(1.00)
0.174
(1.00)
0.641
(1.00)
0.439
(1.00)
21q gain 26 (15%) 145 0.606
(1.00)
0.794
(1.00)
0.0209
(1.00)
0.42
(1.00)
0.773
(1.00)
0.709
(1.00)
0.753
(1.00)
0.527
(1.00)
22q gain 22 (13%) 149 0.363
(1.00)
0.561
(1.00)
0.00747
(1.00)
0.287
(1.00)
0.168
(1.00)
0.687
(1.00)
0.359
(1.00)
0.878
(1.00)
xq gain 24 (14%) 147 0.415
(1.00)
0.649
(1.00)
0.385
(1.00)
0.349
(1.00)
0.132
(1.00)
0.509
(1.00)
0.0982
(1.00)
0.159
(1.00)
1p loss 5 (3%) 166 0.115
(1.00)
0.4
(1.00)
0.301
(1.00)
0.523
(1.00)
0.856
(1.00)
0.0751
(1.00)
0.863
(1.00)
0.378
(1.00)
1q loss 5 (3%) 166 0.00725
(1.00)
0.133
(1.00)
0.125
(1.00)
0.243
(1.00)
0.629
(1.00)
0.927
(1.00)
0.625
(1.00)
0.626
(1.00)
2p loss 11 (6%) 160 0.92
(1.00)
0.338
(1.00)
0.887
(1.00)
0.582
(1.00)
0.354
(1.00)
0.427
(1.00)
0.358
(1.00)
0.462
(1.00)
2q loss 21 (12%) 150 0.892
(1.00)
0.342
(1.00)
0.0756
(1.00)
0.0567
(1.00)
0.583
(1.00)
0.173
(1.00)
0.476
(1.00)
0.527
(1.00)
3q loss 6 (4%) 165 0.0393
(1.00)
0.233
(1.00)
0.227
(1.00)
0.132
(1.00)
0.629
(1.00)
0.305
(1.00)
0.625
(1.00)
0.102
(1.00)
5p loss 9 (5%) 162 0.171
(1.00)
0.878
(1.00)
0.382
(1.00)
0.363
(1.00)
0.832
(1.00)
0.614
(1.00)
0.76
(1.00)
0.108
(1.00)
6p loss 25 (15%) 146 0.95
(1.00)
0.829
(1.00)
0.168
(1.00)
0.227
(1.00)
0.781
(1.00)
0.753
(1.00)
0.291
(1.00)
0.435
(1.00)
6q loss 41 (24%) 130 0.845
(1.00)
0.478
(1.00)
0.0461
(1.00)
0.905
(1.00)
0.594
(1.00)
0.634
(1.00)
0.0686
(1.00)
0.681
(1.00)
7p loss 16 (9%) 155 0.415
(1.00)
0.286
(1.00)
0.0468
(1.00)
0.101
(1.00)
0.0879
(1.00)
0.529
(1.00)
0.0596
(1.00)
0.153
(1.00)
7q loss 25 (15%) 146 0.0418
(1.00)
0.545
(1.00)
0.0951
(1.00)
0.184
(1.00)
0.0456
(1.00)
0.0189
(1.00)
0.0182
(1.00)
0.841
(1.00)
8p loss 46 (27%) 125 0.00187
(1.00)
0.0118
(1.00)
0.235
(1.00)
0.946
(1.00)
0.625
(1.00)
0.63
(1.00)
0.964
(1.00)
0.754
(1.00)
8q loss 15 (9%) 156 0.0592
(1.00)
0.318
(1.00)
0.0375
(1.00)
0.0191
(1.00)
0.229
(1.00)
0.0493
(1.00)
0.15
(1.00)
0.047
(1.00)
9p loss 31 (18%) 140 0.00117
(0.728)
0.259
(1.00)
0.902
(1.00)
0.15
(1.00)
0.798
(1.00)
0.915
(1.00)
0.452
(1.00)
0.83
(1.00)
9q loss 26 (15%) 145 0.0183
(1.00)
0.059
(1.00)
0.0415
(1.00)
0.00839
(1.00)
0.191
(1.00)
0.0842
(1.00)
0.161
(1.00)
0.0446
(1.00)
10p loss 38 (22%) 133 0.0318
(1.00)
0.415
(1.00)
0.184
(1.00)
0.264
(1.00)
0.363
(1.00)
0.0598
(1.00)
0.612
(1.00)
0.424
(1.00)
10q loss 44 (26%) 127 0.0317
(1.00)
0.35
(1.00)
0.0199
(1.00)
0.41
(1.00)
0.592
(1.00)
0.16
(1.00)
0.782
(1.00)
0.611
(1.00)
11p loss 52 (30%) 119 0.0397
(1.00)
0.223
(1.00)
0.373
(1.00)
0.14
(1.00)
0.796
(1.00)
0.582
(1.00)
0.318
(1.00)
0.546
(1.00)
11q loss 64 (37%) 107 0.000628
(0.395)
0.366
(1.00)
0.348
(1.00)
0.631
(1.00)
0.912
(1.00)
0.967
(1.00)
0.907
(1.00)
0.674
(1.00)
12p loss 24 (14%) 147 0.00199
(1.00)
0.0449
(1.00)
0.403
(1.00)
0.434
(1.00)
0.282
(1.00)
0.332
(1.00)
0.495
(1.00)
0.753
(1.00)
12q loss 6 (4%) 165 0.0393
(1.00)
0.209
(1.00)
0.979
(1.00)
0.72
(1.00)
0.876
(1.00)
0.243
(1.00)
0.774
(1.00)
0.875
(1.00)
13q loss 44 (26%) 127 0.269
(1.00)
0.12
(1.00)
0.178
(1.00)
0.00905
(1.00)
0.119
(1.00)
0.215
(1.00)
0.0226
(1.00)
0.369
(1.00)
14q loss 19 (11%) 152 0.00353
(1.00)
0.593
(1.00)
0.345
(1.00)
0.0618
(1.00)
0.2
(1.00)
0.218
(1.00)
0.234
(1.00)
0.128
(1.00)
15q loss 21 (12%) 150 0.0259
(1.00)
0.623
(1.00)
0.332
(1.00)
0.466
(1.00)
0.692
(1.00)
0.492
(1.00)
0.734
(1.00)
0.71
(1.00)
16p loss 20 (12%) 151 0.282
(1.00)
0.28
(1.00)
0.491
(1.00)
0.369
(1.00)
0.246
(1.00)
0.0928
(1.00)
0.147
(1.00)
0.345
(1.00)
16q loss 26 (15%) 145 0.714
(1.00)
0.0574
(1.00)
0.0957
(1.00)
0.109
(1.00)
0.148
(1.00)
0.00567
(1.00)
0.064
(1.00)
0.0527
(1.00)
17q loss 15 (9%) 156 0.00757
(1.00)
0.232
(1.00)
0.115
(1.00)
0.285
(1.00)
0.269
(1.00)
0.738
(1.00)
0.0942
(1.00)
0.204
(1.00)
18p loss 32 (19%) 139 0.121
(1.00)
0.218
(1.00)
0.15
(1.00)
0.264
(1.00)
0.61
(1.00)
0.123
(1.00)
0.468
(1.00)
0.397
(1.00)
18q loss 42 (25%) 129 0.0283
(1.00)
0.0199
(1.00)
0.0174
(1.00)
0.0281
(1.00)
0.409
(1.00)
0.00513
(1.00)
0.468
(1.00)
0.224
(1.00)
19p loss 33 (19%) 138 0.000845
(0.529)
0.0119
(1.00)
0.0255
(1.00)
0.151
(1.00)
0.205
(1.00)
0.0677
(1.00)
0.597
(1.00)
0.546
(1.00)
19q loss 15 (9%) 156 0.546
(1.00)
0.364
(1.00)
0.4
(1.00)
0.225
(1.00)
0.229
(1.00)
0.491
(1.00)
0.654
(1.00)
0.301
(1.00)
20p loss 12 (7%) 159 0.284
(1.00)
0.921
(1.00)
0.623
(1.00)
0.354
(1.00)
0.7
(1.00)
0.705
(1.00)
0.521
(1.00)
0.605
(1.00)
20q loss 5 (3%) 166 0.364
(1.00)
0.241
(1.00)
0.874
(1.00)
1
(1.00)
0.739
(1.00)
0.927
(1.00)
0.737
(1.00)
0.864
(1.00)
21q loss 25 (15%) 146 0.00137
(0.847)
0.234
(1.00)
0.805
(1.00)
0.0264
(1.00)
0.881
(1.00)
0.778
(1.00)
1
(1.00)
0.886
(1.00)
22q loss 37 (22%) 134 0.0153
(1.00)
0.0147
(1.00)
0.103
(1.00)
0.082
(1.00)
0.217
(1.00)
0.109
(1.00)
0.233
(1.00)
0.127
(1.00)
xq loss 35 (20%) 136 0.641
(1.00)
0.0241
(1.00)
0.199
(1.00)
0.165
(1.00)
0.0702
(1.00)
0.352
(1.00)
0.0668
(1.00)
0.165
(1.00)
'2p gain' versus 'CN_CNMF'

P value = 0.000129 (Fisher's exact test), Q value = 0.082

Table S1.  Gene #3: '2p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 58 36 77
2P GAIN MUTATED 13 15 6
2P GAIN WILD-TYPE 45 21 71

Figure S1.  Get High-res Image Gene #3: '2p gain' versus Molecular Subtype #1: 'CN_CNMF'

'5p gain' versus 'CN_CNMF'

P value = 7.51e-07 (Fisher's exact test), Q value = 0.00048

Table S2.  Gene #9: '5p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 58 36 77
5P GAIN MUTATED 21 25 14
5P GAIN WILD-TYPE 37 11 63

Figure S2.  Get High-res Image Gene #9: '5p gain' versus Molecular Subtype #1: 'CN_CNMF'

'19q gain' versus 'CN_CNMF'

P value = 9.2e-08 (Fisher's exact test), Q value = 5.9e-05

Table S3.  Gene #35: '19q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 58 36 77
19Q GAIN MUTATED 9 22 9
19Q GAIN WILD-TYPE 49 14 68

Figure S3.  Get High-res Image Gene #35: '19q gain' versus Molecular Subtype #1: 'CN_CNMF'

'3p loss' versus 'MIRSEQ_MATURE_CNMF'

P value = 3.47e-06 (Fisher's exact test), Q value = 0.0022

Table S4.  Gene #45: '3p loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 57 47 51
3P LOSS MUTATED 27 12 3
3P LOSS WILD-TYPE 30 35 48

Figure S4.  Get High-res Image Gene #45: '3p loss' versus Molecular Subtype #7: 'MIRSEQ_MATURE_CNMF'

'4p loss' versus 'CN_CNMF'

P value = 5.38e-06 (Fisher's exact test), Q value = 0.0034

Table S5.  Gene #47: '4p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 58 36 77
4P LOSS MUTATED 26 25 17
4P LOSS WILD-TYPE 32 11 60

Figure S5.  Get High-res Image Gene #47: '4p loss' versus Molecular Subtype #1: 'CN_CNMF'

'4q loss' versus 'CN_CNMF'

P value = 4.59e-07 (Fisher's exact test), Q value = 0.00029

Table S6.  Gene #48: '4q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 58 36 77
4Q LOSS MUTATED 15 22 9
4Q LOSS WILD-TYPE 43 14 68

Figure S6.  Get High-res Image Gene #48: '4q loss' versus Molecular Subtype #1: 'CN_CNMF'

'5q loss' versus 'CN_CNMF'

P value = 0.000232 (Fisher's exact test), Q value = 0.15

Table S7.  Gene #50: '5q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 58 36 77
5Q LOSS MUTATED 8 15 7
5Q LOSS WILD-TYPE 50 21 70

Figure S7.  Get High-res Image Gene #50: '5q loss' versus Molecular Subtype #1: 'CN_CNMF'

'17p loss' versus 'CN_CNMF'

P value = 2.98e-05 (Fisher's exact test), Q value = 0.019

Table S8.  Gene #70: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 58 36 77
17P LOSS MUTATED 20 19 10
17P LOSS WILD-TYPE 38 17 67

Figure S8.  Get High-res Image Gene #70: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input
  • Copy number data file = transformed.cor.cli.txt

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

  • Number of patients = 171

  • Number of significantly arm-level cnvs = 80

  • Number of molecular subtypes = 8

  • 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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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)