Ovarian Serous Cystadenocarcinoma: Correlation between copy number variations of arm-level result and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

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

Summary

Testing the association between copy number variation 80 arm-level results and 7 clinical features across 554 patients, 8 significant findings detected with Q value < 0.25.

  • 2p gain cnv correlated to 'AGE'.

  • 3q gain cnv correlated to 'AGE'.

  • 10p gain cnv correlated to 'AGE'.

  • 12p gain cnv correlated to 'AGE'.

  • 12q gain cnv correlated to 'AGE'.

  • 20p gain cnv correlated to 'AGE'.

  • 20q gain cnv correlated to 'AGE'.

  • 9q loss cnv correlated to 'AGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 80 arm-level results and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 8 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE PRIMARY
SITE
OF
DISEASE 		KARNOFSKY
PERFORMANCE
SCORE 		TUMOR
STAGE 		RADIATIONS
RADIATION
REGIMENINDICATION 		NEOADJUVANT
THERAPY
nCNV (%) nWild-Type logrank test t-test Fisher's exact test t-test Fisher's exact test Fisher's exact test Fisher's exact test
2p gain 124 (22%) 430 0.0314
(1.00) 		2.19e-07
(0.000121) 		1
(1.00) 		0.956
(1.00) 		0.966
(1.00) 		0.533
(1.00) 		0.696
(1.00)
3q gain 204 (37%) 350 0.596
(1.00) 		1.56e-06
(0.000863) 		0.127
(1.00) 		0.671
(1.00) 		0.493
(1.00) 		0.301
(1.00) 		0.143
(1.00)
10p gain 133 (24%) 421 0.163
(1.00) 		3.74e-05
(0.0205) 		0.111
(1.00) 		0.152
(1.00) 		0.794
(1.00) 		1
(1.00) 		0.128
(1.00)
12p gain 201 (36%) 353 0.277
(1.00) 		3e-08
(1.67e-05) 		0.124
(1.00) 		0.0302
(1.00) 		0.00495
(1.00) 		1
(1.00) 		0.0416
(1.00)
12q gain 116 (21%) 438 0.137
(1.00) 		3.23e-06
(0.00177) 		0.61
(1.00) 		0.0312
(1.00) 		0.0712
(1.00) 		0.507
(1.00) 		0.285
(1.00)
20p gain 229 (41%) 325 0.157
(1.00) 		2.5e-06
(0.00137) 		1
(1.00) 		0.105
(1.00) 		0.196
(1.00) 		0.572
(1.00) 		0.272
(1.00)
20q gain 266 (48%) 288 0.114
(1.00) 		7.92e-07
(0.000438) 		1
(1.00) 		0.229
(1.00) 		0.0879
(1.00) 		0.61
(1.00) 		0.515
(1.00)
9q loss 236 (43%) 318 0.518
(1.00) 		2.45e-06
(0.00135) 		0.152
(1.00) 		0.779
(1.00) 		0.244
(1.00) 		0.578
(1.00) 		0.742
(1.00)
1p gain 103 (19%) 451 0.246
(1.00) 		0.681
(1.00) 		0.159
(1.00) 		0.126
(1.00) 		0.261
(1.00) 		0.0903
(1.00) 		0.889
(1.00)
1q gain 154 (28%) 400 0.351
(1.00) 		0.024
(1.00) 		0.0673
(1.00) 		0.0331
(1.00) 		0.217
(1.00) 		1
(1.00) 		0.809
(1.00)
2q gain 98 (18%) 456 0.11
(1.00) 		0.000479
(0.262) 		1
(1.00) 		0.69
(1.00) 		0.66
(1.00) 		0.0823
(1.00) 		0.477
(1.00)
3p gain 107 (19%) 447 0.49
(1.00) 		0.0546
(1.00) 		0.577
(1.00) 		0.73
(1.00) 		0.811
(1.00) 		1
(1.00) 		0.274
(1.00)
4p gain 30 (5%) 524 0.28
(1.00) 		0.568
(1.00) 		1
(1.00) 		0.921
(1.00) 		0.781
(1.00) 		1
(1.00) 		0.145
(1.00)
4q gain 17 (3%) 537 0.528
(1.00) 		0.0825
(1.00) 		1
(1.00) 		0.921
(1.00) 		0.389
(1.00) 		1
(1.00) 		0.0254
(1.00)
5p gain 139 (25%) 415 0.384
(1.00) 		0.294
(1.00) 		0.121
(1.00) 		0.791
(1.00) 		0.871
(1.00) 		1
(1.00) 		0.707
(1.00)
5q gain 31 (6%) 523 0.307
(1.00) 		0.927
(1.00) 		0.206
(1.00) 		0.137
(1.00) 		0.852
(1.00) 		1
(1.00) 		1
(1.00)
6p gain 121 (22%) 433 0.37
(1.00) 		0.00538
(1.00) 		0.628
(1.00) 		0.0337
(1.00) 		0.0697
(1.00) 		1
(1.00) 		0.598
(1.00)
6q gain 61 (11%) 493 0.501
(1.00) 		0.0798
(1.00) 		1
(1.00) 		0.195
(1.00) 		0.0138
(1.00) 		1
(1.00) 		0.862
(1.00)
7p gain 121 (22%) 433 0.175
(1.00) 		0.00094
(0.513) 		1
(1.00) 		0.292
(1.00) 		0.41
(1.00) 		1
(1.00) 		0.188
(1.00)
7q gain 144 (26%) 410 0.246
(1.00) 		0.00699
(1.00) 		0.701
(1.00) 		0.276
(1.00) 		0.281
(1.00) 		0.572
(1.00) 		0.106
(1.00)
8p gain 86 (16%) 468 0.0817
(1.00) 		0.699
(1.00) 		1
(1.00) 		0.894
(1.00) 		0.416
(1.00) 		0.398
(1.00) 		0.372
(1.00)
8q gain 203 (37%) 351 0.168
(1.00) 		0.596
(1.00) 		0.25
(1.00) 		0.946
(1.00) 		0.223
(1.00) 		1
(1.00) 		0.0546
(1.00)
9p gain 59 (11%) 495 0.74
(1.00) 		0.788
(1.00) 		1
(1.00) 		0.95
(1.00) 		0.686
(1.00) 		1
(1.00) 		0.597
(1.00)
9q gain 26 (5%) 528 0.943
(1.00) 		0.742
(1.00) 		1
(1.00) 		0.43
(1.00) 		1
(1.00) 		1
(1.00)
10q gain 69 (12%) 485 0.383
(1.00) 		0.0268
(1.00) 		0.414
(1.00) 		0.32
(1.00) 		0.37
(1.00) 		1
(1.00) 		0.0308
(1.00)
11p gain 40 (7%) 514 0.574
(1.00) 		0.86
(1.00) 		1
(1.00) 		0.852
(1.00) 		0.117
(1.00) 		1
(1.00) 		0.033
(1.00)
11q gain 60 (11%) 494 0.265
(1.00) 		0.376
(1.00) 		1
(1.00) 		0.657
(1.00) 		0.265
(1.00) 		1
(1.00) 		0.219
(1.00)
13q gain 38 (7%) 516 0.719
(1.00) 		0.0339
(1.00) 		1
(1.00) 		0.333
(1.00) 		0.192
(1.00) 		0.279
(1.00)
14q gain 33 (6%) 521 0.311
(1.00) 		0.78
(1.00) 		1
(1.00) 		0.497
(1.00) 		0.537
(1.00) 		0.169
(1.00) 		0.818
(1.00)
15q gain 24 (4%) 530 0.855
(1.00) 		0.348
(1.00) 		1
(1.00) 		0.0315
(1.00) 		1
(1.00) 		0.424
(1.00)
16p gain 32 (6%) 522 0.61
(1.00) 		0.511
(1.00) 		1
(1.00) 		0.232
(1.00) 		0.198
(1.00) 		1
(1.00) 		0.485
(1.00)
16q gain 16 (3%) 538 0.666
(1.00) 		0.756
(1.00) 		1
(1.00) 		0.357
(1.00) 		1
(1.00) 		0.748
(1.00)
17p gain 10 (2%) 544 0.161
(1.00) 		0.0805
(1.00) 		1
(1.00) 		0.767
(1.00) 		0.562
(1.00) 		1
(1.00) 		0.221
(1.00)
17q gain 23 (4%) 531 0.0947
(1.00) 		0.182
(1.00) 		1
(1.00) 		0.614
(1.00) 		0.574
(1.00) 		1
(1.00) 		0.28
(1.00)
18p gain 75 (14%) 479 0.165
(1.00) 		0.16
(1.00) 		1
(1.00) 		0.341
(1.00) 		0.0077
(1.00) 		1
(1.00) 		0.114
(1.00)
18q gain 41 (7%) 513 0.261
(1.00) 		0.332
(1.00) 		1
(1.00) 		0.252
(1.00) 		0.0727
(1.00) 		0.206
(1.00) 		0.0598
(1.00)
19p gain 97 (18%) 457 0.33
(1.00) 		0.0138
(1.00) 		1
(1.00) 		0.996
(1.00) 		0.26
(1.00) 		0.439
(1.00) 		0.474
(1.00)
19q gain 90 (16%) 464 0.146
(1.00) 		0.0138
(1.00) 		1
(1.00) 		0.649
(1.00) 		0.282
(1.00) 		0.413
(1.00) 		0.768
(1.00)
21q gain 68 (12%) 486 0.00788
(1.00) 		0.0129
(1.00) 		0.409
(1.00) 		0.566
(1.00) 		0.676
(1.00) 		1
(1.00) 		0.74
(1.00)
22q gain 9 (2%) 545 0.6
(1.00) 		0.29
(1.00) 		1
(1.00) 		0.39
(1.00) 		1
(1.00) 		0.38
(1.00)
Xq gain 33 (6%) 521 0.0955
(1.00) 		0.348
(1.00) 		1
(1.00) 		0.137
(1.00) 		0.974
(1.00) 		1
(1.00) 		0.651
(1.00)
1p loss 34 (6%) 520 0.838
(1.00) 		0.409
(1.00) 		1
(1.00) 		0.296
(1.00) 		0.708
(1.00) 		0.173
(1.00) 		0.496
(1.00)
1q loss 22 (4%) 532 0.333
(1.00) 		0.0871
(1.00) 		1
(1.00) 		0.22
(1.00) 		0.359
(1.00) 		0.115
(1.00) 		0.0465
(1.00)
2p loss 26 (5%) 528 0.579
(1.00) 		0.847
(1.00) 		1
(1.00) 		0.0731
(1.00) 		0.472
(1.00) 		1
(1.00) 		0.8
(1.00)
2q loss 30 (5%) 524 0.72
(1.00) 		0.452
(1.00) 		0.2
(1.00) 		0.0731
(1.00) 		0.343
(1.00) 		1
(1.00) 		0.477
(1.00)
3p loss 58 (10%) 496 0.178
(1.00) 		0.162
(1.00) 		0.358
(1.00) 		0.321
(1.00) 		0.682
(1.00) 		0.283
(1.00) 		0.286
(1.00)
3q loss 23 (4%) 531 0.136
(1.00) 		0.847
(1.00) 		0.156
(1.00) 		0.607
(1.00) 		0.171
(1.00) 		0.12
(1.00) 		0.784
(1.00)
4p loss 251 (45%) 303 0.248
(1.00) 		0.302
(1.00) 		0.165
(1.00) 		0.106
(1.00) 		0.469
(1.00) 		0.593
(1.00) 		1
(1.00)
4q loss 292 (53%) 262 0.398
(1.00) 		0.172
(1.00) 		0.751
(1.00) 		0.952
(1.00) 		0.338
(1.00) 		1
(1.00) 		0.586
(1.00)
5p loss 82 (15%) 472 0.229
(1.00) 		0.0152
(1.00) 		0.474
(1.00) 		0.227
(1.00) 		0.334
(1.00) 		1
(1.00) 		0.878
(1.00)
5q loss 167 (30%) 387 0.555
(1.00) 		0.0843
(1.00) 		0.351
(1.00) 		0.42
(1.00) 		0.287
(1.00) 		1
(1.00) 		0.0759
(1.00)
6p loss 120 (22%) 434 0.0993
(1.00) 		0.00325
(1.00) 		0.624
(1.00) 		0.647
(1.00) 		0.45
(1.00) 		0.52
(1.00) 		0.895
(1.00)
6q loss 188 (34%) 366 0.401
(1.00) 		0.00682
(1.00) 		0.114
(1.00) 		0.309
(1.00) 		0.371
(1.00) 		1
(1.00) 		1
(1.00)
7p loss 90 (16%) 464 0.098
(1.00) 		0.999
(1.00) 		0.509
(1.00) 		0.0336
(1.00) 		0.276
(1.00) 		1
(1.00) 		0.659
(1.00)
7q loss 49 (9%) 505 0.0253
(1.00) 		0.248
(1.00) 		0.31
(1.00) 		0.00339
(1.00) 		0.953
(1.00) 		0.243
(1.00) 		0.565
(1.00)
8p loss 226 (41%) 328 0.375
(1.00) 		0.0218
(1.00) 		0.144
(1.00) 		0.0388
(1.00) 		0.455
(1.00) 		1
(1.00) 		0.825
(1.00)
8q loss 60 (11%) 494 0.587
(1.00) 		0.0177
(1.00) 		1
(1.00) 		0.331
(1.00) 		0.0266
(1.00) 		1
(1.00) 		0.73
(1.00)
9p loss 208 (38%) 346 0.641
(1.00) 		0.0321
(1.00) 		0.13
(1.00) 		0.479
(1.00) 		0.484
(1.00) 		0.56
(1.00) 		0.144
(1.00)
10p loss 61 (11%) 493 0.693
(1.00) 		0.0863
(1.00) 		1
(1.00) 		0.753
(1.00) 		0.893
(1.00) 		0.296
(1.00) 		0.729
(1.00)
10q loss 86 (16%) 468 0.899
(1.00) 		0.245
(1.00) 		1
(1.00) 		0.72
(1.00) 		0.412
(1.00) 		0.00363
(1.00) 		0.233
(1.00)
11p loss 140 (25%) 414 0.685
(1.00) 		0.0258
(1.00) 		1
(1.00) 		0.741
(1.00) 		0.345
(1.00) 		1
(1.00) 		0.454
(1.00)
11q loss 107 (19%) 447 0.818
(1.00) 		0.147
(1.00) 		0.577
(1.00) 		0.17
(1.00) 		0.34
(1.00) 		0.475
(1.00) 		0.679
(1.00)
12p loss 49 (9%) 505 0.813
(1.00) 		0.12
(1.00) 		1
(1.00) 		0.113
(1.00) 		0.0214
(1.00) 		1
(1.00) 		0.126
(1.00)
12q loss 72 (13%) 482 0.294
(1.00) 		0.446
(1.00) 		1
(1.00) 		0.695
(1.00) 		0.0055
(1.00) 		1
(1.00) 		0.194
(1.00)
13q loss 251 (45%) 303 0.307
(1.00) 		0.84
(1.00) 		0.457
(1.00) 		0.496
(1.00) 		0.207
(1.00) 		1
(1.00) 		0.444
(1.00)
14q loss 160 (29%) 394 0.413
(1.00) 		0.0967
(1.00) 		1
(1.00) 		0.706
(1.00) 		0.0456
(1.00) 		0.561
(1.00) 		0.633
(1.00)
15q loss 213 (38%) 341 0.89
(1.00) 		0.0447
(1.00) 		0.273
(1.00) 		0.65
(1.00) 		0.113
(1.00) 		0.562
(1.00) 		0.182
(1.00)
16p loss 258 (47%) 296 0.122
(1.00) 		0.94
(1.00) 		0.467
(1.00) 		0.259
(1.00) 		0.226
(1.00) 		0.6
(1.00) 		0.232
(1.00)
16q loss 346 (62%) 208 0.192
(1.00) 		0.00304
(1.00) 		0.779
(1.00) 		0.0536
(1.00) 		0.391
(1.00) 		1
(1.00) 		0.18
(1.00)
17p loss 416 (75%) 138 0.919
(1.00) 		0.0213
(1.00) 		0.26
(1.00) 		0.883
(1.00) 		0.155
(1.00) 		1
(1.00) 		0.802
(1.00)
17q loss 320 (58%) 234 0.358
(1.00) 		0.0657
(1.00) 		1
(1.00) 		0.0547
(1.00) 		0.674
(1.00) 		0.267
(1.00) 		0.0481
(1.00)
18p loss 179 (32%) 375 0.533
(1.00) 		0.815
(1.00) 		0.791
(1.00) 		0.752
(1.00) 		0.108
(1.00) 		0.245
(1.00) 		0.0271
(1.00)
18q loss 225 (41%) 329 0.53
(1.00) 		0.838
(1.00) 		0.767
(1.00) 		0.65
(1.00) 		0.386
(1.00) 		0.569
(1.00) 		0.0461
(1.00)
19p loss 138 (25%) 416 0.952
(1.00) 		0.115
(1.00) 		0.683
(1.00) 		0.549
(1.00) 		0.52
(1.00) 		1
(1.00) 		0.451
(1.00)
19q loss 138 (25%) 416 0.691
(1.00) 		0.73
(1.00) 		0.683
(1.00) 		0.482
(1.00) 		0.681
(1.00) 		1
(1.00) 		0.315
(1.00)
20p loss 35 (6%) 519 0.0308
(1.00) 		0.22
(1.00) 		1
(1.00) 		0.00339
(1.00) 		0.765
(1.00) 		0.178
(1.00) 		0.122
(1.00)
20q loss 21 (4%) 533 0.0397
(1.00) 		0.796
(1.00) 		1
(1.00) 		0.00339
(1.00) 		0.656
(1.00) 		1
(1.00) 		0.779
(1.00)
21q loss 145 (26%) 409 0.654
(1.00) 		0.955
(1.00) 		1
(1.00) 		0.508
(1.00) 		0.524
(1.00) 		1
(1.00) 		0.265
(1.00)
22q loss 376 (68%) 178 0.281
(1.00) 		0.179
(1.00) 		0.386
(1.00) 		0.954
(1.00) 		0.188
(1.00) 		0.555
(1.00) 		1
(1.00)
Xq loss 100 (18%) 454 0.261
(1.00) 		0.761
(1.00) 		0.55
(1.00) 		0.485
(1.00) 		0.868
(1.00) 		1
(1.00) 		0.673
(1.00)
'2p gain mutation analysis' versus 'AGE'

P value = 2.19e-07 (t-test), Q value = 0.00012

Table S1.  Gene #3: '2p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 543 59.8 (11.6)
2P GAIN MUTATED 122 64.5 (11.0)
2P GAIN WILD-TYPE 421 58.4 (11.4)

Figure S1.  Get High-res Image Gene #3: '2p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'3q gain mutation analysis' versus 'AGE'

P value = 1.56e-06 (t-test), Q value = 0.00086

Table S2.  Gene #6: '3q gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 543 59.8 (11.6)
3Q GAIN MUTATED 201 62.8 (11.1)
3Q GAIN WILD-TYPE 342 58.0 (11.5)

Figure S2.  Get High-res Image Gene #6: '3q gain mutation analysis' versus Clinical Feature #2: 'AGE'

'10p gain mutation analysis' versus 'AGE'

P value = 3.74e-05 (t-test), Q value = 0.02

Table S3.  Gene #19: '10p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 543 59.8 (11.6)
10P GAIN MUTATED 129 63.3 (10.7)
10P GAIN WILD-TYPE 414 58.7 (11.7)

Figure S3.  Get High-res Image Gene #19: '10p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'12p gain mutation analysis' versus 'AGE'

P value = 3e-08 (t-test), Q value = 1.7e-05

Table S4.  Gene #23: '12p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 543 59.8 (11.6)
12P GAIN MUTATED 196 63.4 (11.2)
12P GAIN WILD-TYPE 347 57.7 (11.4)

Figure S4.  Get High-res Image Gene #23: '12p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'12q gain mutation analysis' versus 'AGE'

P value = 3.23e-06 (t-test), Q value = 0.0018

Table S5.  Gene #24: '12q gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 543 59.8 (11.6)
12Q GAIN MUTATED 112 64.1 (10.5)
12Q GAIN WILD-TYPE 431 58.6 (11.6)

Figure S5.  Get High-res Image Gene #24: '12q gain mutation analysis' versus Clinical Feature #2: 'AGE'

'20p gain mutation analysis' versus 'AGE'

P value = 2.5e-06 (t-test), Q value = 0.0014

Table S6.  Gene #36: '20p gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 543 59.8 (11.6)
20P GAIN MUTATED 225 62.5 (11.4)
20P GAIN WILD-TYPE 318 57.8 (11.4)

Figure S6.  Get High-res Image Gene #36: '20p gain mutation analysis' versus Clinical Feature #2: 'AGE'

'20q gain mutation analysis' versus 'AGE'

P value = 7.92e-07 (t-test), Q value = 0.00044

Table S7.  Gene #37: '20q gain mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 543 59.8 (11.6)
20Q GAIN MUTATED 261 62.3 (11.7)
20Q GAIN WILD-TYPE 282 57.4 (11.0)

Figure S7.  Get High-res Image Gene #37: '20q gain mutation analysis' versus Clinical Feature #2: 'AGE'

'9q loss mutation analysis' versus 'AGE'

P value = 2.45e-06 (t-test), Q value = 0.0014

Table S8.  Gene #58: '9q loss mutation analysis' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 543 59.8 (11.6)
9Q LOSS MUTATED 230 62.5 (11.3)
9Q LOSS WILD-TYPE 313 57.8 (11.5)

Figure S8.  Get High-res Image Gene #58: '9q loss mutation analysis' versus Clinical Feature #2: 'AGE'

Methods & Data
Input

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

  • Clinical data file = OV.clin.merged.picked.txt

  • Number of patients = 554

  • Number of significantly arm-level cnvs = 80

  • Number of selected clinical features = 7

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

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R

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

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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)
[4] 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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle