Correlation between copy number variations of arm-level result and molecular subtypes
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 32 arm-level results and 10 molecular subtypes across 197 patients, 23 significant findings detected with Q value < 0.25.

  • 7p gain cnv correlated to 'CN_CNMF',  'MIRSEQ_CHIERARCHICAL', and 'MIRSEQ_MATURE_CNMF'.

  • 7q gain cnv correlated to 'CN_CNMF' and 'MIRSEQ_MATURE_CNMF'.

  • 8p gain cnv correlated to 'CN_CNMF' and 'RPPA_CHIERARCHICAL'.

  • 8q gain cnv correlated to 'CN_CNMF',  'RPPA_CNMF',  'RPPA_CHIERARCHICAL',  'MIRSEQ_CNMF',  'MIRSEQ_CHIERARCHICAL', and 'MIRSEQ_MATURE_CNMF'.

  • 6q loss cnv correlated to 'CN_CNMF'.

  • 8p loss cnv correlated to 'METHLYATION_CNMF',  'MRNASEQ_CNMF',  'MRNASEQ_CHIERARCHICAL', and 'MIRSEQ_CNMF'.

  • 13q loss cnv correlated to 'CN_CNMF'.

  • 16q loss cnv correlated to 'CN_CNMF' and 'MIRSEQ_CHIERARCHICAL'.

  • 17p 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 32 arm-level results and 10 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 23 significant findings detected.

Molecular
subtypes 		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 Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Chi-square test Fisher's exact test Chi-square test Fisher's exact test
8q gain 0 (0%) 173 2.28e-08
(7.14e-06) 		0.129
(1.00) 		0.00025
(0.0747) 		0.000107
(0.0323) 		0.00976
(1.00) 		0.00615
(1.00) 		0.000351
(0.104) 		1.01e-05
(0.00308) 		4.53e-06
(0.00139) 		0.061
(1.00)
8p loss 0 (0%) 149 0.00345
(0.98) 		4.24e-06
(0.0013) 		0.556
(1.00) 		0.824
(1.00) 		0.000407
(0.12) 		0.000413
(0.122) 		1.66e-05
(0.00504) 		0.482
(1.00) 		0.0092
(1.00) 		0.0842
(1.00)
7p gain 0 (0%) 180 9.28e-08
(2.9e-05) 		0.0299
(1.00) 		0.0455
(1.00) 		0.115
(1.00) 		0.041
(1.00) 		0.0349
(1.00) 		0.0141
(1.00) 		0.000276
(0.0821) 		0.000205
(0.0614) 		0.139
(1.00)
7q gain 0 (0%) 181 4.16e-07
(0.000129) 		0.0247
(1.00) 		0.0866
(1.00) 		0.219
(1.00) 		0.0626
(1.00) 		0.124
(1.00) 		0.0107
(1.00) 		0.00177
(0.511) 		0.000104
(0.0313) 		0.316
(1.00)
8p gain 0 (0%) 186 1.93e-06
(0.000596) 		0.109
(1.00) 		0.000875
(0.255) 		0.000425
(0.125) 		0.0403
(1.00) 		0.0688
(1.00) 		0.00748
(1.00) 		0.00609
(1.00) 		0.00366
(1.00) 		0.32
(1.00)
16q loss 0 (0%) 169 8.64e-09
(2.71e-06) 		0.00343
(0.976) 		0.415
(1.00) 		0.109
(1.00) 		0.0304
(1.00) 		0.00252
(0.721) 		0.00917
(1.00) 		0.000463
(0.136) 		0.0357
(1.00) 		0.243
(1.00)
6q loss 0 (0%) 188 0.000736
(0.215) 		0.0656
(1.00) 		0.488
(1.00) 		0.629
(1.00) 		0.0824
(1.00) 		0.0149
(1.00) 		0.306
(1.00) 		0.15
(1.00) 		0.386
(1.00) 		0.722
(1.00)
13q loss 0 (0%) 182 3.11e-06
(0.000957) 		0.0541
(1.00) 		0.00606
(1.00) 		0.0194
(1.00) 		0.0098
(1.00) 		0.0211
(1.00) 		0.243
(1.00) 		0.144
(1.00) 		0.262
(1.00) 		0.543
(1.00)
17p loss 0 (0%) 173 6.49e-07
(0.000201) 		0.00999
(1.00) 		0.0175
(1.00) 		0.0282
(1.00) 		0.0187
(1.00) 		0.00155
(0.449) 		0.0383
(1.00) 		0.043
(1.00) 		0.0242
(1.00) 		0.322
(1.00)
18q loss 0 (0%) 171 7.98e-05
(0.0242) 		0.233
(1.00) 		0.0059
(1.00) 		0.00786
(1.00) 		0.113
(1.00) 		0.0341
(1.00) 		0.383
(1.00) 		0.0304
(1.00) 		0.133
(1.00) 		0.334
(1.00)
1p gain 0 (0%) 194 0.0497
(1.00) 		0.0233
(1.00) 		0.781
(1.00) 		0.636
(1.00) 		0.109
(1.00) 		0.185
(1.00) 		0.566
(1.00) 		0.267
(1.00) 		0.00368
(1.00) 		0.736
(1.00)
1q gain 0 (0%) 192 0.00405
(1.00) 		0.227
(1.00) 		0.189
(1.00) 		0.129
(1.00) 		0.376
(1.00) 		0.118
(1.00) 		0.405
(1.00) 		0.272
(1.00) 		0.0807
(1.00) 		0.693
(1.00)
3p gain 0 (0%) 192 0.553
(1.00) 		0.445
(1.00) 		0.189
(1.00) 		0.129
(1.00) 		0.131
(1.00) 		0.118
(1.00) 		0.0404
(1.00) 		0.0242
(1.00) 		0.459
(1.00) 		0.693
(1.00)
3q gain 0 (0%) 190 0.2
(1.00) 		0.299
(1.00) 		0.0748
(1.00) 		0.0499
(1.00) 		0.0186
(1.00) 		0.0482
(1.00) 		0.162
(1.00) 		0.00242
(0.695) 		0.0164
(1.00) 		0.413
(1.00)
9p gain 0 (0%) 194 0.0497
(1.00) 		0.384
(1.00) 		0.775
(1.00) 		0.0572
(1.00) 		0.26
(1.00) 		0.501
(1.00) 		0.867
(1.00) 		1
(1.00)
9q gain 0 (0%) 189 0.00404
(1.00) 		0.0854
(1.00) 		0.0479
(1.00) 		0.0638
(1.00) 		0.134
(1.00) 		0.176
(1.00) 		0.389
(1.00) 		0.0915
(1.00) 		0.0231
(1.00) 		0.535
(1.00)
10q gain 0 (0%) 193 0.187
(1.00) 		0.462
(1.00) 		0.113
(1.00) 		0.11
(1.00) 		0.328
(1.00) 		0.131
(1.00) 		0.65
(1.00) 		0.0572
(1.00) 		0.874
(1.00) 		0.625
(1.00)
12q gain 0 (0%) 194 0.556
(1.00) 		0.195
(1.00) 		0.484
(1.00) 		1
(1.00) 		0.528
(1.00) 		1
(1.00) 		0.935
(1.00) 		0.585
(1.00) 		0.535
(1.00) 		0.736
(1.00)
16p gain 0 (0%) 194 0.0497
(1.00) 		0.384
(1.00) 		0.781
(1.00) 		0.636
(1.00) 		0.775
(1.00) 		0.0572
(1.00) 		0.566
(1.00) 		0.585
(1.00) 		0.837
(1.00) 		0.736
(1.00)
16q gain 0 (0%) 194 0.0497
(1.00) 		0.384
(1.00) 		0.781
(1.00) 		0.636
(1.00) 		0.775
(1.00) 		0.0572
(1.00) 		0.566
(1.00) 		0.585
(1.00) 		0.837
(1.00) 		0.736
(1.00)
5q loss 0 (0%) 192 0.00405
(1.00) 		0.0362
(1.00) 		0.452
(1.00) 		0.318
(1.00) 		0.131
(1.00) 		0.0472
(1.00) 		0.405
(1.00) 		0.0104
(1.00) 		0.602
(1.00) 		1
(1.00)
8q loss 0 (0%) 193 0.187
(1.00) 		0.317
(1.00) 		0.561
(1.00) 		1
(1.00) 		0.328
(1.00) 		0.131
(1.00) 		0.263
(1.00) 		0.448
(1.00) 		0.605
(1.00) 		0.362
(1.00)
10p loss 0 (0%) 190 0.0109
(1.00) 		0.0897
(1.00) 		0.0748
(1.00) 		0.0499
(1.00) 		0.181
(1.00) 		0.0401
(1.00) 		0.22
(1.00) 		0.0268
(1.00) 		0.246
(1.00) 		0.413
(1.00)
10q loss 0 (0%) 190 0.0109
(1.00) 		0.562
(1.00) 		0.189
(1.00) 		0.129
(1.00) 		0.511
(1.00) 		0.15
(1.00) 		0.254
(1.00) 		0.446
(1.00) 		0.246
(1.00) 		0.413
(1.00)
12p loss 0 (0%) 186 0.011
(1.00) 		0.0644
(1.00) 		0.231
(1.00) 		0.629
(1.00) 		0.034
(1.00) 		0.0208
(1.00) 		0.241
(1.00) 		0.271
(1.00) 		0.348
(1.00) 		0.691
(1.00)
15q loss 0 (0%) 194 0.0497
(1.00) 		0.0233
(1.00) 		0.781
(1.00) 		0.636
(1.00) 		0.109
(1.00) 		0.185
(1.00) 		0.553
(1.00) 		0.0452
(1.00) 		0.00377
(1.00) 		0.512
(1.00)
16p loss 0 (0%) 193 0.018
(1.00) 		0.00649
(1.00) 		0.0353
(1.00) 		0.178
(1.00) 		0.00923
(1.00) 		0.0452
(1.00) 		0.00377
(1.00) 		0.0468
(1.00)
18p loss 0 (0%) 178 0.00197
(0.568) 		0.177
(1.00) 		0.0263
(1.00) 		0.0279
(1.00) 		0.47
(1.00) 		0.117
(1.00) 		0.166
(1.00) 		0.129
(1.00) 		0.0274
(1.00) 		0.0638
(1.00)
20p loss 0 (0%) 192 0.553
(1.00) 		0.0362
(1.00) 		0.537
(1.00) 		0.736
(1.00) 		0.131
(1.00) 		0.304
(1.00) 		0.263
(1.00) 		0.0283
(1.00) 		0.459
(1.00) 		0.268
(1.00)
21q loss 0 (0%) 192 0.0598
(1.00) 		0.273
(1.00) 		0.484
(1.00) 		0.777
(1.00) 		0.131
(1.00) 		0.147
(1.00) 		0.753
(1.00) 		0.187
(1.00) 		0.769
(1.00) 		0.835
(1.00)
22q loss 0 (0%) 192 0.0186
(1.00) 		0.273
(1.00) 		0.837
(1.00) 		0.684
(1.00) 		0.376
(1.00) 		0.118
(1.00) 		0.787
(1.00) 		1
(1.00) 		0.491
(1.00) 		1
(1.00)
Xq loss 0 (0%) 194 0.123
(1.00) 		0.0233
(1.00) 		0.109
(1.00) 		0.185
(1.00) 		0.349
(1.00) 		0.585
(1.00) 		0.311
(1.00) 		0.26
(1.00)
'7p gain' versus 'CN_CNMF'

P value = 9.28e-08 (Fisher's exact test), Q value = 2.9e-05

Table S1.  Gene #5: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
7P GAIN CNV 2 0 15
7P GAIN WILD-TYPE 26 108 46

Figure S1.  Get High-res Image Gene #5: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'

'7p gain' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000276 (Fisher's exact test), Q value = 0.082

Table S2.  Gene #5: '7p gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 34 21 74 67
7P GAIN CNV 1 8 5 3
7P GAIN WILD-TYPE 33 13 69 64

Figure S2.  Get High-res Image Gene #5: '7p gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

'7p gain' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000205 (Chi-square test), Q value = 0.061

Table S3.  Gene #5: '7p gain' versus Molecular Subtype #9: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 73 18 12 15 78
7P GAIN CNV 6 0 0 6 5
7P GAIN WILD-TYPE 67 18 12 9 73

Figure S3.  Get High-res Image Gene #5: '7p gain' versus Molecular Subtype #9: 'MIRSEQ_MATURE_CNMF'

'7q gain' versus 'CN_CNMF'

P value = 4.16e-07 (Fisher's exact test), Q value = 0.00013

Table S4.  Gene #6: '7q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
7Q GAIN CNV 2 0 14
7Q GAIN WILD-TYPE 26 108 47

Figure S4.  Get High-res Image Gene #6: '7q gain' versus Molecular Subtype #1: 'CN_CNMF'

'7q gain' versus 'MIRSEQ_MATURE_CNMF'

P value = 0.000104 (Chi-square test), Q value = 0.031

Table S5.  Gene #6: '7q gain' versus Molecular Subtype #9: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 73 18 12 15 78
7Q GAIN CNV 5 0 0 6 5
7Q GAIN WILD-TYPE 68 18 12 9 73

Figure S5.  Get High-res Image Gene #6: '7q gain' versus Molecular Subtype #9: 'MIRSEQ_MATURE_CNMF'

'8p gain' versus 'CN_CNMF'

P value = 1.93e-06 (Fisher's exact test), Q value = 6e-04

Table S6.  Gene #7: '8p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
8P GAIN CNV 0 0 11
8P GAIN WILD-TYPE 28 108 50

Figure S6.  Get High-res Image Gene #7: '8p gain' versus Molecular Subtype #1: 'CN_CNMF'

'8p gain' versus 'RPPA_CHIERARCHICAL'

P value = 0.000425 (Fisher's exact test), Q value = 0.12

Table S7.  Gene #7: '8p gain' versus Molecular Subtype #4: 'RPPA_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 51 58 45
8P GAIN CNV 0 8 0
8P GAIN WILD-TYPE 51 50 45

Figure S7.  Get High-res Image Gene #7: '8p gain' versus Molecular Subtype #4: 'RPPA_CHIERARCHICAL'

'8q gain' versus 'CN_CNMF'

P value = 2.28e-08 (Fisher's exact test), Q value = 7.1e-06

Table S8.  Gene #8: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
8Q GAIN CNV 2 2 20
8Q GAIN WILD-TYPE 26 106 41

Figure S8.  Get High-res Image Gene #8: '8q gain' versus Molecular Subtype #1: 'CN_CNMF'

'8q gain' versus 'RPPA_CNMF'

P value = 0.00025 (Fisher's exact test), Q value = 0.075

Table S9.  Gene #8: '8q gain' versus Molecular Subtype #3: 'RPPA_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 41 56 57
8Q GAIN CNV 0 4 14
8Q GAIN WILD-TYPE 41 52 43

Figure S9.  Get High-res Image Gene #8: '8q gain' versus Molecular Subtype #3: 'RPPA_CNMF'

'8q gain' versus 'RPPA_CHIERARCHICAL'

P value = 0.000107 (Fisher's exact test), Q value = 0.032

Table S10.  Gene #8: '8q gain' versus Molecular Subtype #4: 'RPPA_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 51 58 45
8Q GAIN CNV 0 14 4
8Q GAIN WILD-TYPE 51 44 41

Figure S10.  Get High-res Image Gene #8: '8q gain' versus Molecular Subtype #4: 'RPPA_CHIERARCHICAL'

'8q gain' versus 'MIRSEQ_CNMF'

P value = 0.000351 (Chi-square test), Q value = 0.1

Table S11.  Gene #8: '8q gain' versus Molecular Subtype #7: 'MIRSEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 52 39 17 14 74
8Q GAIN CNV 4 1 7 0 11
8Q GAIN WILD-TYPE 48 38 10 14 63

Figure S11.  Get High-res Image Gene #8: '8q gain' versus Molecular Subtype #7: 'MIRSEQ_CNMF'

'8q gain' versus 'MIRSEQ_CHIERARCHICAL'

P value = 1.01e-05 (Fisher's exact test), Q value = 0.0031

Table S12.  Gene #8: '8q gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 34 21 74 67
8Q GAIN CNV 2 9 11 1
8Q GAIN WILD-TYPE 32 12 63 66

Figure S12.  Get High-res Image Gene #8: '8q gain' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

'8q gain' versus 'MIRSEQ_MATURE_CNMF'

P value = 4.53e-06 (Chi-square test), Q value = 0.0014

Table S13.  Gene #8: '8q gain' versus Molecular Subtype #9: 'MIRSEQ_MATURE_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 73 18 12 15 78
8Q GAIN CNV 4 1 0 8 10
8Q GAIN WILD-TYPE 69 17 12 7 68

Figure S13.  Get High-res Image Gene #8: '8q gain' versus Molecular Subtype #9: 'MIRSEQ_MATURE_CNMF'

'6q loss' versus 'CN_CNMF'

P value = 0.000736 (Fisher's exact test), Q value = 0.21

Table S14.  Gene #16: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
6Q LOSS CNV 2 0 7
6Q LOSS WILD-TYPE 26 108 54

Figure S14.  Get High-res Image Gene #16: '6q loss' versus Molecular Subtype #1: 'CN_CNMF'

'8p loss' versus 'METHLYATION_CNMF'

P value = 4.24e-06 (Fisher's exact test), Q value = 0.0013

Table S15.  Gene #17: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 57 65 75
8P LOSS CNV 11 5 32
8P LOSS WILD-TYPE 46 60 43

Figure S15.  Get High-res Image Gene #17: '8p loss' versus Molecular Subtype #2: 'METHLYATION_CNMF'

'8p loss' versus 'MRNASEQ_CNMF'

P value = 0.000407 (Fisher's exact test), Q value = 0.12

Table S16.  Gene #17: '8p loss' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 67 60 67
8P LOSS CNV 12 8 28
8P LOSS WILD-TYPE 55 52 39

Figure S16.  Get High-res Image Gene #17: '8p loss' versus Molecular Subtype #5: 'MRNASEQ_CNMF'

'8p loss' versus 'MRNASEQ_CHIERARCHICAL'

P value = 0.000413 (Fisher's exact test), Q value = 0.12

Table S17.  Gene #17: '8p loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 49 68 77
8P LOSS CNV 10 28 10
8P LOSS WILD-TYPE 39 40 67

Figure S17.  Get High-res Image Gene #17: '8p loss' versus Molecular Subtype #6: 'MRNASEQ_CHIERARCHICAL'

'8p loss' versus 'MIRSEQ_CNMF'

P value = 1.66e-05 (Chi-square test), Q value = 0.005

Table S18.  Gene #17: '8p loss' versus Molecular Subtype #7: 'MIRSEQ_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4 CLUS_5
ALL 52 39 17 14 74
8P LOSS CNV 24 2 2 0 20
8P LOSS WILD-TYPE 28 37 15 14 54

Figure S18.  Get High-res Image Gene #17: '8p loss' versus Molecular Subtype #7: 'MIRSEQ_CNMF'

'13q loss' versus 'CN_CNMF'

P value = 3.11e-06 (Fisher's exact test), Q value = 0.00096

Table S19.  Gene #22: '13q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
13Q LOSS CNV 3 0 12
13Q LOSS WILD-TYPE 25 108 49

Figure S19.  Get High-res Image Gene #22: '13q loss' versus Molecular Subtype #1: 'CN_CNMF'

'16q loss' versus 'CN_CNMF'

P value = 8.64e-09 (Fisher's exact test), Q value = 2.7e-06

Table S20.  Gene #25: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
16Q LOSS CNV 1 4 23
16Q LOSS WILD-TYPE 27 104 38

Figure S20.  Get High-res Image Gene #25: '16q loss' versus Molecular Subtype #1: 'CN_CNMF'

'16q loss' versus 'MIRSEQ_CHIERARCHICAL'

P value = 0.000463 (Fisher's exact test), Q value = 0.14

Table S21.  Gene #25: '16q loss' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

nPatients CLUS_1 CLUS_2 CLUS_3 CLUS_4
ALL 34 21 74 67
16Q LOSS CNV 2 9 12 4
16Q LOSS WILD-TYPE 32 12 62 63

Figure S21.  Get High-res Image Gene #25: '16q loss' versus Molecular Subtype #8: 'MIRSEQ_CHIERARCHICAL'

'17p loss' versus 'CN_CNMF'

P value = 6.49e-07 (Fisher's exact test), Q value = 2e-04

Table S22.  Gene #26: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
17P LOSS CNV 0 5 19
17P LOSS WILD-TYPE 28 103 42

Figure S22.  Get High-res Image Gene #26: '17p loss' versus Molecular Subtype #1: 'CN_CNMF'

'18q loss' versus 'CN_CNMF'

P value = 7.98e-05 (Fisher's exact test), Q value = 0.024

Table S23.  Gene #28: '18q loss' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2 CLUS_3
ALL 28 108 61
18Q LOSS CNV 1 7 18
18Q LOSS WILD-TYPE 27 101 43

Figure S23.  Get High-res Image Gene #28: '18q loss' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input

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

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

  • Number of patients = 197

  • Number of significantly arm-level cnvs = 32

  • 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.

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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