Correlation between copy number variations of arm-level result and molecular subtypes
Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (Primary solid tumor)
23 September 2013  |  analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C18050W3
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/C18050W3
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 74 arm-level results and 8 molecular subtypes across 155 patients, 7 significant findings detected with Q value < 0.25.

  • 3q gain cnv correlated to 'METHLYATION_CNMF',  'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_MATURE_CHIERARCHICAL'.

  • 18q gain cnv correlated to 'METHLYATION_CNMF' and 'MRNASEQ_CNMF'.

  • 3p loss cnv correlated to 'MRNASEQ_CHIERARCHICAL'.

  • 5q 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 74 arm-level results and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 7 significant findings detected.

Molecular
subtypes 		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
3q gain 0 (0%) 79 0.000454
(0.264) 		0.00027
(0.158) 		0.00302
(1.00) 		0.000328
(0.191) 		0.0274
(1.00) 		0.249
(1.00) 		0.148
(1.00) 		2.84e-06
(0.00167)
18q gain 0 (0%) 147 0.533
(1.00) 		1.18e-05
(0.00695) 		8.93e-05
(0.0524) 		0.0313
(1.00) 		0.391
(1.00) 		1
(1.00) 		0.226
(1.00) 		0.495
(1.00)
3p loss 0 (0%) 123 0.0218
(1.00) 		0.0285
(1.00) 		0.098
(1.00) 		0.000241
(0.141) 		0.00953
(1.00) 		0.000576
(0.335) 		0.00381
(1.00) 		0.00149
(0.86)
5q loss 0 (0%) 133 0.00028
(0.164) 		0.0243
(1.00) 		0.218
(1.00) 		0.068
(1.00) 		0.124
(1.00) 		0.0125
(1.00) 		0.0828
(1.00) 		0.0977
(1.00)
1p gain 0 (0%) 114 0.354
(1.00) 		0.254
(1.00) 		0.158
(1.00) 		0.544
(1.00) 		0.982
(1.00) 		0.828
(1.00) 		0.514
(1.00) 		0.675
(1.00)
1q gain 0 (0%) 97 0.0238
(1.00) 		0.635
(1.00) 		0.375
(1.00) 		0.249
(1.00) 		0.252
(1.00) 		0.0764
(1.00) 		0.896
(1.00) 		0.0599
(1.00)
2p gain 0 (0%) 132 0.0075
(1.00) 		0.0138
(1.00) 		0.351
(1.00) 		0.17
(1.00) 		0.563
(1.00) 		0.0567
(1.00) 		0.452
(1.00) 		0.16
(1.00)
2q gain 0 (0%) 149 0.0922
(1.00) 		0.0262
(1.00) 		0.205
(1.00) 		0.0139
(1.00) 		1
(1.00) 		0.637
(1.00) 		1
(1.00) 		0.862
(1.00)
3p gain 0 (0%) 134 0.956
(1.00) 		0.0373
(1.00) 		0.0813
(1.00) 		0.0682
(1.00) 		0.385
(1.00) 		1
(1.00) 		0.679
(1.00) 		0.107
(1.00)
4q gain 0 (0%) 152 0.192
(1.00) 		0.307
(1.00) 		0.19
(1.00) 		0.138
(1.00) 		0.487
(1.00) 		0.216
(1.00) 		0.362
(1.00) 		0.394
(1.00)
5p gain 0 (0%) 104 0.00322
(1.00) 		0.193
(1.00) 		0.561
(1.00) 		0.969
(1.00) 		0.857
(1.00) 		0.42
(1.00) 		0.542
(1.00) 		0.0586
(1.00)
5q gain 0 (0%) 140 0.888
(1.00) 		0.722
(1.00) 		0.442
(1.00) 		0.868
(1.00) 		0.759
(1.00) 		0.53
(1.00) 		0.707
(1.00) 		0.765
(1.00)
6p gain 0 (0%) 134 0.202
(1.00) 		0.0146
(1.00) 		0.0154
(1.00) 		0.0254
(1.00) 		0.345
(1.00) 		0.278
(1.00) 		0.165
(1.00) 		0.0133
(1.00)
6q gain 0 (0%) 145 0.505
(1.00) 		0.0763
(1.00) 		0.129
(1.00) 		0.196
(1.00) 		0.164
(1.00) 		0.0651
(1.00) 		0.347
(1.00) 		0.0183
(1.00)
7p gain 0 (0%) 146 0.915
(1.00) 		0.0358
(1.00) 		0.015
(1.00) 		0.0971
(1.00) 		0.316
(1.00) 		0.101
(1.00) 		0.00303
(1.00) 		0.172
(1.00)
7q gain 0 (0%) 143 0.163
(1.00) 		0.0845
(1.00) 		0.694
(1.00) 		0.536
(1.00) 		0.482
(1.00) 		1
(1.00) 		0.0918
(1.00) 		1
(1.00)
8p gain 0 (0%) 141 0.384
(1.00) 		0.254
(1.00) 		0.064
(1.00) 		0.0599
(1.00) 		0.394
(1.00) 		1
(1.00) 		0.155
(1.00) 		0.856
(1.00)
8q gain 0 (0%) 126 0.496
(1.00) 		0.272
(1.00) 		0.245
(1.00) 		0.613
(1.00) 		0.0649
(1.00) 		0.208
(1.00) 		0.0174
(1.00) 		0.0878
(1.00)
9p gain 0 (0%) 137 0.543
(1.00) 		0.044
(1.00) 		0.538
(1.00) 		0.00683
(1.00) 		0.412
(1.00) 		0.509
(1.00) 		0.691
(1.00) 		0.695
(1.00)
9q gain 0 (0%) 138 0.357
(1.00) 		0.178
(1.00) 		0.305
(1.00) 		0.00272
(1.00) 		0.103
(1.00) 		1
(1.00) 		0.748
(1.00) 		0.744
(1.00)
10p gain 0 (0%) 147 0.133
(1.00) 		0.457
(1.00) 		0.4
(1.00) 		1
(1.00) 		0.288
(1.00) 		1
(1.00) 		0.624
(1.00) 		0.495
(1.00)
10q gain 0 (0%) 152 0.112
(1.00) 		0.228
(1.00) 		0.427
(1.00) 		0.724
(1.00) 		0.487
(1.00) 		0.216
(1.00) 		0.623
(1.00) 		0.394
(1.00)
12p gain 0 (0%) 137 0.0448
(1.00) 		0.0101
(1.00) 		0.21
(1.00) 		0.00165
(0.951) 		0.257
(1.00) 		0.209
(1.00) 		0.425
(1.00) 		0.441
(1.00)
12q gain 0 (0%) 139 0.0173
(1.00) 		0.0161
(1.00) 		0.277
(1.00) 		0.0133
(1.00) 		0.244
(1.00) 		1
(1.00) 		0.647
(1.00) 		1
(1.00)
13q gain 0 (0%) 147 0.471
(1.00) 		0.802
(1.00) 		0.89
(1.00) 		0.617
(1.00) 		0.419
(1.00) 		0.365
(1.00) 		0.763
(1.00) 		0.44
(1.00)
14q gain 0 (0%) 140 0.00972
(1.00) 		0.187
(1.00) 		0.13
(1.00) 		0.287
(1.00) 		0.402
(1.00) 		0.487
(1.00) 		0.0983
(1.00) 		0.149
(1.00)
15q gain 0 (0%) 137 0.0865
(1.00) 		0.395
(1.00) 		0.892
(1.00) 		0.87
(1.00) 		0.844
(1.00) 		1
(1.00) 		1
(1.00) 		0.933
(1.00)
16p gain 0 (0%) 138 0.0265
(1.00) 		0.304
(1.00) 		0.797
(1.00) 		0.361
(1.00) 		0.394
(1.00) 		0.0609
(1.00) 		0.462
(1.00) 		0.185
(1.00)
16q gain 0 (0%) 145 0.847
(1.00) 		0.437
(1.00) 		0.57
(1.00) 		0.191
(1.00) 		0.179
(1.00) 		0.241
(1.00) 		0.17
(1.00) 		0.241
(1.00)
17p gain 0 (0%) 148 0.337
(1.00) 		0.309
(1.00) 		0.549
(1.00) 		0.25
(1.00) 		0.423
(1.00) 		0.0287
(1.00) 		0.372
(1.00) 		0.038
(1.00)
17q gain 0 (0%) 138 0.0395
(1.00) 		0.179
(1.00) 		0.0211
(1.00) 		0.00217
(1.00) 		0.394
(1.00) 		0.0208
(1.00) 		0.0549
(1.00) 		0.00438
(1.00)
18p gain 0 (0%) 139 0.448
(1.00) 		0.0134
(1.00) 		0.0155
(1.00) 		0.0994
(1.00) 		0.24
(1.00) 		0.345
(1.00) 		0.103
(1.00) 		0.117
(1.00)
19p gain 0 (0%) 142 0.219
(1.00) 		0.0246
(1.00) 		0.717
(1.00) 		0.187
(1.00) 		0.436
(1.00) 		1
(1.00) 		0.497
(1.00) 		0.845
(1.00)
19q gain 0 (0%) 128 0.172
(1.00) 		0.057
(1.00) 		0.273
(1.00) 		0.12
(1.00) 		0.126
(1.00) 		0.138
(1.00) 		0.0127
(1.00) 		0.0696
(1.00)
20p gain 0 (0%) 118 0.0194
(1.00) 		0.0792
(1.00) 		0.337
(1.00) 		0.0465
(1.00) 		0.946
(1.00) 		0.18
(1.00) 		0.709
(1.00) 		0.579
(1.00)
20q gain 0 (0%) 111 0.015
(1.00) 		0.0142
(1.00) 		0.848
(1.00) 		0.247
(1.00) 		0.778
(1.00) 		0.209
(1.00) 		0.97
(1.00) 		0.356
(1.00)
21q gain 0 (0%) 139 0.797
(1.00) 		0.19
(1.00) 		0.0116
(1.00) 		0.7
(1.00) 		0.332
(1.00) 		0.758
(1.00) 		0.246
(1.00) 		0.291
(1.00)
22q gain 0 (0%) 143 0.163
(1.00) 		0.031
(1.00) 		0.0013
(0.751) 		0.0287
(1.00) 		0.313
(1.00) 		1
(1.00) 		0.297
(1.00) 		0.308
(1.00)
Xq gain 0 (0%) 144 0.682
(1.00) 		0.904
(1.00) 		0.873
(1.00) 		0.16
(1.00) 		0.179
(1.00) 		0.241
(1.00) 		0.191
(1.00) 		0.536
(1.00)
1q loss 0 (0%) 152 0.278
(1.00) 		0.272
(1.00) 		0.186
(1.00) 		0.724
(1.00) 		0.839
(1.00) 		1
(1.00) 		0.781
(1.00) 		0.293
(1.00)
2p loss 0 (0%) 151 0.834
(1.00) 		0.517
(1.00) 		0.561
(1.00) 		0.624
(1.00) 		0.736
(1.00) 		0.553
(1.00) 		1
(1.00) 		0.775
(1.00)
2q loss 0 (0%) 147 0.314
(1.00) 		0.0162
(1.00) 		0.0537
(1.00) 		0.351
(1.00) 		1
(1.00) 		1
(1.00) 		0.825
(1.00) 		1
(1.00)
4p loss 0 (0%) 105 0.00185
(1.00) 		0.315
(1.00) 		0.25
(1.00) 		0.292
(1.00) 		0.351
(1.00) 		0.215
(1.00) 		0.273
(1.00) 		0.135
(1.00)
4q loss 0 (0%) 125 0.000678
(0.393) 		0.0384
(1.00) 		0.836
(1.00) 		0.122
(1.00) 		0.627
(1.00) 		1
(1.00) 		0.919
(1.00) 		0.66
(1.00)
5p loss 0 (0%) 152 0.379
(1.00) 		0.269
(1.00) 		0.223
(1.00) 		1
(1.00) 		0.356
(1.00) 		1
(1.00) 		1
(1.00) 		0.639
(1.00)
6p loss 0 (0%) 139 0.639
(1.00) 		0.0109
(1.00) 		0.0817
(1.00) 		0.0844
(1.00) 		0.775
(1.00) 		1
(1.00) 		0.872
(1.00) 		1
(1.00)
6q loss 0 (0%) 127 0.34
(1.00) 		0.145
(1.00) 		0.0234
(1.00) 		0.52
(1.00) 		0.373
(1.00) 		0.805
(1.00) 		0.393
(1.00) 		0.633
(1.00)
7p loss 0 (0%) 149 0.11
(1.00) 		0.594
(1.00) 		0.196
(1.00) 		0.122
(1.00) 		0.779
(1.00) 		0.365
(1.00) 		0.24
(1.00) 		0.587
(1.00)
7q loss 0 (0%) 140 0.215
(1.00) 		0.0205
(1.00) 		0.0327
(1.00) 		0.212
(1.00) 		0.106
(1.00) 		0.753
(1.00) 		0.21
(1.00) 		0.0245
(1.00)
8p loss 0 (0%) 126 0.685
(1.00) 		0.69
(1.00) 		0.203
(1.00) 		0.566
(1.00) 		0.878
(1.00) 		1
(1.00) 		0.713
(1.00) 		1
(1.00)
8q loss 0 (0%) 148 0.44
(1.00) 		0.14
(1.00) 		0.277
(1.00) 		0.515
(1.00) 		0.2
(1.00) 		0.0258
(1.00) 		0.0793
(1.00) 		0.0183
(1.00)
9p loss 0 (0%) 142 0.219
(1.00) 		0.759
(1.00) 		0.75
(1.00) 		0.665
(1.00) 		0.97
(1.00) 		1
(1.00) 		0.443
(1.00) 		0.411
(1.00)
9q loss 0 (0%) 144 0.165
(1.00) 		0.13
(1.00) 		0.21
(1.00) 		0.313
(1.00) 		0.366
(1.00) 		0.0864
(1.00) 		0.572
(1.00) 		0.182
(1.00)
10p loss 0 (0%) 130 0.321
(1.00) 		0.543
(1.00) 		0.164
(1.00) 		0.106
(1.00) 		0.765
(1.00) 		0.61
(1.00) 		0.665
(1.00) 		0.584
(1.00)
10q loss 0 (0%) 128 0.445
(1.00) 		0.746
(1.00) 		0.306
(1.00) 		0.171
(1.00) 		0.477
(1.00) 		0.459
(1.00) 		0.33
(1.00) 		0.282
(1.00)
11p loss 0 (0%) 122 0.0193
(1.00) 		0.314
(1.00) 		0.754
(1.00) 		0.199
(1.00) 		0.712
(1.00) 		0.627
(1.00) 		0.517
(1.00) 		0.21
(1.00)
11q loss 0 (0%) 119 0.0845
(1.00) 		0.182
(1.00) 		0.199
(1.00) 		0.0102
(1.00) 		0.979
(1.00) 		0.646
(1.00) 		0.895
(1.00) 		0.93
(1.00)
12p loss 0 (0%) 136 0.00983
(1.00) 		0.384
(1.00) 		0.833
(1.00) 		0.494
(1.00) 		0.589
(1.00) 		0.18
(1.00) 		0.542
(1.00) 		0.127
(1.00)
12q loss 0 (0%) 151 0.0662
(1.00) 		0.392
(1.00) 		0.71
(1.00) 		0.36
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00)
13q loss 0 (0%) 128 0.0226
(1.00) 		0.208
(1.00) 		0.239
(1.00) 		0.0137
(1.00) 		0.0177
(1.00) 		0.14
(1.00) 		0.145
(1.00) 		0.207
(1.00)
14q loss 0 (0%) 148 0.44
(1.00) 		0.117
(1.00) 		0.0508
(1.00) 		0.0139
(1.00) 		0.2
(1.00) 		0.0258
(1.00) 		0.127
(1.00) 		0.0766
(1.00)
15q loss 0 (0%) 146 0.112
(1.00) 		0.168
(1.00) 		0.496
(1.00) 		0.221
(1.00) 		0.772
(1.00) 		0.697
(1.00) 		1
(1.00) 		0.732
(1.00)
16p loss 0 (0%) 146 1
(1.00) 		0.0756
(1.00) 		0.0502
(1.00) 		0.00603
(1.00) 		0.304
(1.00) 		0.451
(1.00) 		0.379
(1.00) 		0.188
(1.00)
16q loss 0 (0%) 139 0.569
(1.00) 		0.00964
(1.00) 		0.0133
(1.00) 		0.000693
(0.401) 		0.134
(1.00) 		0.0138
(1.00) 		0.0136
(1.00) 		0.0313
(1.00)
17p loss 0 (0%) 124 0.0168
(1.00) 		0.92
(1.00) 		0.087
(1.00) 		0.106
(1.00) 		0.0202
(1.00) 		0.0173
(1.00) 		0.0455
(1.00) 		0.0374
(1.00)
17q loss 0 (0%) 149 1
(1.00) 		0.664
(1.00) 		0.641
(1.00) 		0.0761
(1.00) 		0.296
(1.00) 		0.321
(1.00) 		0.263
(1.00) 		0.376
(1.00)
18p loss 0 (0%) 138 0.205
(1.00) 		0.487
(1.00) 		0.0415
(1.00) 		0.229
(1.00) 		0.1
(1.00) 		0.0818
(1.00) 		0.0721
(1.00) 		0.0585
(1.00)
18q loss 0 (0%) 129 0.417
(1.00) 		0.0477
(1.00) 		0.00111
(0.644) 		0.00482
(1.00) 		0.0132
(1.00) 		0.00661
(1.00) 		0.0491
(1.00) 		0.00409
(1.00)
19p loss 0 (0%) 143 0.445
(1.00) 		0.102
(1.00) 		0.0806
(1.00) 		0.43
(1.00) 		0.551
(1.00) 		0.752
(1.00) 		0.295
(1.00) 		0.0477
(1.00)
19q loss 0 (0%) 149 0.582
(1.00) 		0.353
(1.00) 		0.609
(1.00) 		0.349
(1.00) 		0.779
(1.00) 		0.365
(1.00) 		0.763
(1.00) 		0.44
(1.00)
20p loss 0 (0%) 147 0.741
(1.00) 		0.206
(1.00) 		0.599
(1.00) 		0.857
(1.00) 		0.775
(1.00) 		0.43
(1.00) 		0.258
(1.00) 		0.434
(1.00)
21q loss 0 (0%) 142 0.0471
(1.00) 		0.348
(1.00) 		0.531
(1.00) 		0.102
(1.00) 		0.562
(1.00) 		1
(1.00) 		0.748
(1.00) 		0.932
(1.00)
22q loss 0 (0%) 138 1
(1.00) 		0.249
(1.00) 		0.211
(1.00) 		0.229
(1.00) 		0.759
(1.00) 		1
(1.00) 		0.425
(1.00) 		0.933
(1.00)
Xq loss 0 (0%) 152 0.779
(1.00) 		0.673
(1.00) 		0.496
(1.00) 		0.394
(1.00) 		1
(1.00)
'3q gain' versus 'METHLYATION_CNMF'

P value = 0.00027 (Chi-square test), Q value = 0.16

Table S1.  Gene #6: '3q gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7
ALL 25 24 17 25 28 26 10
3Q GAIN CNV 15 9 11 4 18 10 9
3Q GAIN WILD-TYPE 10 15 6 21 10 16 1

Figure S1.  Get High-res Image Gene #6: '3q gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'3q gain' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000328 (Fisher's exact test), Q value = 0.19

Table S2.  Gene #6: '3q gain' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 47 73 20
3Q GAIN CNV 14 41 16
3Q GAIN WILD-TYPE 33 32 4

Figure S2.  Get High-res Image Gene #6: '3q gain' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'3q gain' versus 'MIRSEQ_MATURE_CHIERARCHICAL'

P value = 2.84e-06 (Fisher's exact test), Q value = 0.0017

Table S3.  Gene #6: '3q gain' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 47 41 38
3Q GAIN CNV 35 9 16
3Q GAIN WILD-TYPE 12 32 22

Figure S3.  Get High-res Image Gene #6: '3q gain' versus Molecular Subtype #8: 'MIRSEQ_MATURE_CHIERARCHICAL'

'18q gain' versus 'METHLYATION_CNMF'

P value = 1.18e-05 (Chi-square test), Q value = 0.0069

Table S4.  Gene #30: '18q gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5 CLUS_6 CLUS_7
ALL 25 24 17 25 28 26 10
18Q GAIN CNV 0 3 0 1 0 0 4
18Q GAIN WILD-TYPE 25 21 17 24 28 26 6

Figure S4.  Get High-res Image Gene #30: '18q gain' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'18q gain' versus 'MRNASEQ_CNMF'

P value = 8.93e-05 (Chi-square test), Q value = 0.052

Table S5.  Gene #30: '18q gain' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 42 25 14 26 33
18Q GAIN CNV 0 6 0 1 0
18Q GAIN WILD-TYPE 42 19 14 25 33

Figure S5.  Get High-res Image Gene #30: '18q gain' versus Molecular Subtype #3: 'MRNASEQ_CNMF'

'3p loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000241 (Fisher's exact test), Q value = 0.14

Table S6.  Gene #41: '3p loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 47 73 20
3P LOSS CNV 2 24 6
3P LOSS WILD-TYPE 45 49 14

Figure S6.  Get High-res Image Gene #41: '3p loss' versus Molecular Subtype #4: 'MRNASEQ_CHIERARCHICAL'

'5q loss' versus 'CN_CNMF'

P value = 0.00028 (Fisher's exact test), Q value = 0.16

Table S7.  Gene #45: '5q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 54 43 58
5Q LOSS CNV 9 12 1
5Q LOSS WILD-TYPE 45 31 57

Figure S7.  Get High-res Image Gene #45: '5q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 155

  • Number of significantly arm-level cnvs = 74

  • 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)
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