This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 77 arm-level events and 3 clinical features across 56 patients, no significant finding detected with Q value < 0.25.
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No arm-level cnvs related to clinical features.
Clinical Features |
Time to Death |
AGE | RACE | ||
nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | |
1p gain | 24 (43%) | 32 |
0.627 (1.00) |
0.169 (1.00) |
0.273 (1.00) |
1q gain | 31 (55%) | 25 |
0.228 (1.00) |
0.509 (1.00) |
0.148 (1.00) |
2p gain | 23 (41%) | 33 |
0.0947 (1.00) |
0.526 (1.00) |
1 (1.00) |
2q gain | 21 (38%) | 35 |
0.852 (1.00) |
0.635 (1.00) |
0.874 (1.00) |
3p gain | 12 (21%) | 44 |
0.536 (1.00) |
0.322 (1.00) |
0.0948 (1.00) |
3q gain | 23 (41%) | 33 |
0.546 (1.00) |
0.309 (1.00) |
0.676 (1.00) |
4p gain | 9 (16%) | 47 |
0.435 (1.00) |
0.738 (1.00) |
0.163 (1.00) |
4q gain | 3 (5%) | 53 |
0.875 (1.00) |
0.985 (1.00) |
1 (1.00) |
5p gain | 23 (41%) | 33 |
0.788 (1.00) |
0.683 (1.00) |
0.877 (1.00) |
5q gain | 8 (14%) | 48 |
0.859 (1.00) |
0.833 (1.00) |
0.242 (1.00) |
6p gain | 30 (54%) | 26 |
0.846 (1.00) |
0.114 (1.00) |
0.451 (1.00) |
6q gain | 27 (48%) | 29 |
0.99 (1.00) |
0.178 (1.00) |
0.6 (1.00) |
7p gain | 21 (38%) | 35 |
0.785 (1.00) |
0.192 (1.00) |
0.236 (1.00) |
7q gain | 17 (30%) | 39 |
0.697 (1.00) |
0.532 (1.00) |
0.866 (1.00) |
8p gain | 19 (34%) | 37 |
0.0938 (1.00) |
0.401 (1.00) |
0.307 (1.00) |
8q gain | 30 (54%) | 26 |
0.142 (1.00) |
0.336 (1.00) |
0.45 (1.00) |
9p gain | 6 (11%) | 50 |
0.943 (1.00) |
0.353 (1.00) |
0.701 (1.00) |
10p gain | 21 (38%) | 35 |
0.464 (1.00) |
0.78 (1.00) |
0.665 (1.00) |
10q gain | 17 (30%) | 39 |
0.275 (1.00) |
0.865 (1.00) |
0.353 (1.00) |
11p gain | 5 (9%) | 51 |
0.979 (1.00) |
0.752 (1.00) |
1 (1.00) |
11q gain | 7 (12%) | 49 |
0.94 (1.00) |
0.457 (1.00) |
1 (1.00) |
12p gain | 22 (39%) | 34 |
0.188 (1.00) |
0.45 (1.00) |
0.285 (1.00) |
12q gain | 11 (20%) | 45 |
0.969 (1.00) |
0.584 (1.00) |
0.395 (1.00) |
13q gain | 15 (27%) | 41 |
0.338 (1.00) |
0.505 (1.00) |
0.732 (1.00) |
14q gain | 7 (12%) | 49 |
0.037 (1.00) |
0.766 (1.00) |
0.558 (1.00) |
15q gain | 4 (7%) | 52 |
0.505 (1.00) |
0.166 (1.00) |
0.203 (1.00) |
16p gain | 10 (18%) | 46 |
0.513 (1.00) |
0.416 (1.00) |
0.395 (1.00) |
16q gain | 6 (11%) | 50 |
0.368 (1.00) |
0.853 (1.00) |
0.492 (1.00) |
17p gain | 9 (16%) | 47 |
0.25 (1.00) |
0.396 (1.00) |
0.285 (1.00) |
17q gain | 18 (32%) | 38 |
0.834 (1.00) |
0.765 (1.00) |
0.75 (1.00) |
18p gain | 18 (32%) | 38 |
0.391 (1.00) |
0.854 (1.00) |
0.651 (1.00) |
18q gain | 14 (25%) | 42 |
0.579 (1.00) |
0.985 (1.00) |
0.857 (1.00) |
19p gain | 24 (43%) | 32 |
0.631 (1.00) |
0.637 (1.00) |
0.598 (1.00) |
19q gain | 28 (50%) | 28 |
0.839 (1.00) |
0.522 (1.00) |
0.593 (1.00) |
20p gain | 37 (66%) | 19 |
0.646 (1.00) |
0.209 (1.00) |
0.441 (1.00) |
20q gain | 44 (79%) | 12 |
0.726 (1.00) |
0.697 (1.00) |
0.193 (1.00) |
21q gain | 18 (32%) | 38 |
0.493 (1.00) |
0.752 (1.00) |
0.259 (1.00) |
22q gain | 8 (14%) | 48 |
0.0868 (1.00) |
0.038 (1.00) |
0.273 (1.00) |
xq gain | 14 (25%) | 42 |
0.649 (1.00) |
0.32 (1.00) |
0.151 (1.00) |
1p loss | 9 (16%) | 47 |
0.562 (1.00) |
0.584 (1.00) |
0.614 (1.00) |
1q loss | 9 (16%) | 47 |
0.355 (1.00) |
0.454 (1.00) |
0.613 (1.00) |
3p loss | 20 (36%) | 36 |
0.209 (1.00) |
0.784 (1.00) |
0.517 (1.00) |
3q loss | 14 (25%) | 42 |
0.378 (1.00) |
0.302 (1.00) |
0.731 (1.00) |
4p loss | 31 (55%) | 25 |
0.244 (1.00) |
0.98 (1.00) |
1 (1.00) |
4q loss | 33 (59%) | 23 |
0.151 (1.00) |
0.537 (1.00) |
0.517 (1.00) |
5p loss | 9 (16%) | 47 |
0.116 (1.00) |
0.118 (1.00) |
0.612 (1.00) |
5q loss | 18 (32%) | 38 |
0.592 (1.00) |
0.533 (1.00) |
0.748 (1.00) |
6p loss | 5 (9%) | 51 |
0.12 (1.00) |
0.508 (1.00) |
0.405 (1.00) |
6q loss | 7 (12%) | 49 |
0.309 (1.00) |
0.682 (1.00) |
0.181 (1.00) |
7p loss | 13 (23%) | 43 |
0.963 (1.00) |
0.734 (1.00) |
1 (1.00) |
7q loss | 13 (23%) | 43 |
0.121 (1.00) |
0.923 (1.00) |
0.715 (1.00) |
8p loss | 23 (41%) | 33 |
0.773 (1.00) |
0.653 (1.00) |
0.222 (1.00) |
8q loss | 9 (16%) | 47 |
0.856 (1.00) |
0.577 (1.00) |
0.283 (1.00) |
9p loss | 34 (61%) | 22 |
0.475 (1.00) |
0.118 (1.00) |
0.528 (1.00) |
9q loss | 39 (70%) | 17 |
0.214 (1.00) |
0.178 (1.00) |
0.413 (1.00) |
10p loss | 23 (41%) | 33 |
0.681 (1.00) |
0.214 (1.00) |
0.265 (1.00) |
10q loss | 21 (38%) | 35 |
0.22 (1.00) |
0.748 (1.00) |
0.282 (1.00) |
11p loss | 26 (46%) | 30 |
0.0271 (1.00) |
0.23 (1.00) |
0.882 (1.00) |
11q loss | 24 (43%) | 32 |
0.431 (1.00) |
0.573 (1.00) |
0.769 (1.00) |
12p loss | 13 (23%) | 43 |
0.377 (1.00) |
0.907 (1.00) |
0.302 (1.00) |
12q loss | 14 (25%) | 42 |
0.366 (1.00) |
0.191 (1.00) |
0.731 (1.00) |
13q loss | 26 (46%) | 30 |
0.851 (1.00) |
0.831 (1.00) |
1 (1.00) |
14q loss | 27 (48%) | 29 |
0.372 (1.00) |
0.533 (1.00) |
0.0855 (1.00) |
15q loss | 32 (57%) | 24 |
0.598 (1.00) |
0.797 (1.00) |
0.357 (1.00) |
16p loss | 32 (57%) | 24 |
0.495 (1.00) |
0.868 (1.00) |
0.514 (1.00) |
16q loss | 37 (66%) | 19 |
0.136 (1.00) |
0.965 (1.00) |
0.327 (1.00) |
17p loss | 34 (61%) | 22 |
0.19 (1.00) |
0.574 (1.00) |
0.244 (1.00) |
17q loss | 17 (30%) | 39 |
0.0378 (1.00) |
0.562 (1.00) |
0.74 (1.00) |
18p loss | 18 (32%) | 38 |
0.64 (1.00) |
0.699 (1.00) |
0.258 (1.00) |
18q loss | 20 (36%) | 36 |
0.265 (1.00) |
0.515 (1.00) |
0.0359 (1.00) |
19p loss | 15 (27%) | 41 |
0.888 (1.00) |
0.505 (1.00) |
0.742 (1.00) |
19q loss | 13 (23%) | 43 |
0.59 (1.00) |
0.437 (1.00) |
0.397 (1.00) |
20p loss | 7 (12%) | 49 |
0.915 (1.00) |
0.823 (1.00) |
0.558 (1.00) |
20q loss | 4 (7%) | 52 |
0.888 (1.00) |
0.886 (1.00) |
1 (1.00) |
21q loss | 17 (30%) | 39 |
0.164 (1.00) |
0.175 (1.00) |
1 (1.00) |
22q loss | 33 (59%) | 23 |
0.322 (1.00) |
0.683 (1.00) |
0.102 (1.00) |
xq loss | 19 (34%) | 37 |
0.612 (1.00) |
0.965 (1.00) |
0.655 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = UCS-TP.merged_data.txt
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Number of patients = 56
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Number of significantly arm-level cnvs = 77
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Number of selected clinical features = 3
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Exclude regions that fewer than K tumors have mutations, K = 3
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
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 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.