This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and molecular subtypes.
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.
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2p gain cnv correlated to 'CN_CNMF'.
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5p gain cnv correlated to 'CN_CNMF'.
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19q gain cnv correlated to 'CN_CNMF'.
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3p loss cnv correlated to 'MIRSEQ_MATURE_CNMF'.
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4p loss cnv correlated to 'CN_CNMF'.
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4q loss cnv correlated to 'CN_CNMF'.
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5q loss cnv correlated to 'CN_CNMF'.
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17p loss cnv correlated to 'CN_CNMF'.
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) |
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'

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'

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'

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'

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'

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'

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'

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'

-
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
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
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
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.
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.