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 2 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.
Table 1. Get Full Table Overview of the association between significant copy number variation of 77 arm-level events and 2 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.
|
Clinical Features |
Time to Death |
AGE | ||
| nCNV (%) | nWild-Type | logrank test | t-test | |
| 1p gain | 24 (43%) | 32 |
0.627 (1.00) |
0.187 (1.00) |
| 1q gain | 31 (55%) | 25 |
0.328 (1.00) |
0.336 (1.00) |
| 2p gain | 23 (41%) | 33 |
0.111 (1.00) |
0.902 (1.00) |
| 2q gain | 21 (38%) | 35 |
0.778 (1.00) |
0.823 (1.00) |
| 3p gain | 12 (21%) | 44 |
0.505 (1.00) |
0.402 (1.00) |
| 3q gain | 23 (41%) | 33 |
0.684 (1.00) |
0.274 (1.00) |
| 4p gain | 9 (16%) | 47 |
0.527 (1.00) |
0.807 (1.00) |
| 4q gain | 3 (5%) | 53 |
0.852 (1.00) |
0.867 (1.00) |
| 5p gain | 23 (41%) | 33 |
0.995 (1.00) |
0.673 (1.00) |
| 5q gain | 8 (14%) | 48 |
0.921 (1.00) |
0.741 (1.00) |
| 6p gain | 30 (54%) | 26 |
0.896 (1.00) |
0.154 (1.00) |
| 6q gain | 27 (48%) | 29 |
0.948 (1.00) |
0.238 (1.00) |
| 7p gain | 21 (38%) | 35 |
0.948 (1.00) |
0.149 (1.00) |
| 7q gain | 17 (30%) | 39 |
0.743 (1.00) |
0.636 (1.00) |
| 8p gain | 19 (34%) | 37 |
0.0776 (1.00) |
0.435 (1.00) |
| 8q gain | 30 (54%) | 26 |
0.141 (1.00) |
0.285 (1.00) |
| 9p gain | 6 (11%) | 50 |
0.871 (1.00) |
0.508 (1.00) |
| 10p gain | 21 (38%) | 35 |
0.5 (1.00) |
0.492 (1.00) |
| 10q gain | 17 (30%) | 39 |
0.295 (1.00) |
0.826 (1.00) |
| 11p gain | 5 (9%) | 51 |
0.956 (1.00) |
0.646 (1.00) |
| 11q gain | 7 (12%) | 49 |
0.962 (1.00) |
0.464 (1.00) |
| 12p gain | 22 (39%) | 34 |
0.212 (1.00) |
0.482 (1.00) |
| 12q gain | 11 (20%) | 45 |
0.928 (1.00) |
0.648 (1.00) |
| 13q gain | 15 (27%) | 41 |
0.31 (1.00) |
0.753 (1.00) |
| 14q gain | 7 (12%) | 49 |
0.0221 (1.00) |
0.866 (1.00) |
| 15q gain | 4 (7%) | 52 |
0.484 (1.00) |
0.0223 (1.00) |
| 16p gain | 10 (18%) | 46 |
0.613 (1.00) |
0.221 (1.00) |
| 16q gain | 6 (11%) | 50 |
0.481 (1.00) |
0.7 (1.00) |
| 17p gain | 9 (16%) | 47 |
0.228 (1.00) |
0.236 (1.00) |
| 17q gain | 18 (32%) | 38 |
0.779 (1.00) |
0.816 (1.00) |
| 18p gain | 18 (32%) | 38 |
0.412 (1.00) |
0.822 (1.00) |
| 18q gain | 14 (25%) | 42 |
0.537 (1.00) |
0.698 (1.00) |
| 19p gain | 24 (43%) | 32 |
0.671 (1.00) |
0.33 (1.00) |
| 19q gain | 28 (50%) | 28 |
0.944 (1.00) |
0.206 (1.00) |
| 20p gain | 37 (66%) | 19 |
0.729 (1.00) |
0.175 (1.00) |
| 20q gain | 44 (79%) | 12 |
0.765 (1.00) |
0.605 (1.00) |
| 21q gain | 18 (32%) | 38 |
0.558 (1.00) |
0.988 (1.00) |
| 22q gain | 8 (14%) | 48 |
0.093 (1.00) |
0.0307 (1.00) |
| xq gain | 14 (25%) | 42 |
0.597 (1.00) |
0.221 (1.00) |
| 1p loss | 9 (16%) | 47 |
0.761 (1.00) |
0.695 (1.00) |
| 1q loss | 9 (16%) | 47 |
0.52 (1.00) |
0.498 (1.00) |
| 3p loss | 20 (36%) | 36 |
0.19 (1.00) |
0.586 (1.00) |
| 3q loss | 14 (25%) | 42 |
0.347 (1.00) |
0.394 (1.00) |
| 4p loss | 31 (55%) | 25 |
0.325 (1.00) |
0.747 (1.00) |
| 4q loss | 33 (59%) | 23 |
0.212 (1.00) |
0.802 (1.00) |
| 5p loss | 9 (16%) | 47 |
0.0718 (1.00) |
0.163 (1.00) |
| 5q loss | 18 (32%) | 38 |
0.541 (1.00) |
0.5 (1.00) |
| 6p loss | 5 (9%) | 51 |
0.176 (1.00) |
0.542 (1.00) |
| 6q loss | 7 (12%) | 49 |
0.424 (1.00) |
0.487 (1.00) |
| 7p loss | 13 (23%) | 43 |
0.93 (1.00) |
0.823 (1.00) |
| 7q loss | 13 (23%) | 43 |
0.127 (1.00) |
0.989 (1.00) |
| 8p loss | 23 (41%) | 33 |
0.66 (1.00) |
0.568 (1.00) |
| 8q loss | 9 (16%) | 47 |
0.821 (1.00) |
0.405 (1.00) |
| 9p loss | 34 (61%) | 22 |
0.504 (1.00) |
0.177 (1.00) |
| 9q loss | 39 (70%) | 17 |
0.312 (1.00) |
0.206 (1.00) |
| 10p loss | 23 (41%) | 33 |
0.568 (1.00) |
0.117 (1.00) |
| 10q loss | 21 (38%) | 35 |
0.313 (1.00) |
0.706 (1.00) |
| 11p loss | 26 (46%) | 30 |
0.0457 (1.00) |
0.158 (1.00) |
| 11q loss | 24 (43%) | 32 |
0.537 (1.00) |
0.364 (1.00) |
| 12p loss | 13 (23%) | 43 |
0.452 (1.00) |
0.953 (1.00) |
| 12q loss | 14 (25%) | 42 |
0.387 (1.00) |
0.222 (1.00) |
| 13q loss | 26 (46%) | 30 |
0.983 (1.00) |
0.655 (1.00) |
| 14q loss | 27 (48%) | 29 |
0.517 (1.00) |
0.407 (1.00) |
| 15q loss | 32 (57%) | 24 |
0.736 (1.00) |
0.598 (1.00) |
| 16p loss | 32 (57%) | 24 |
0.539 (1.00) |
0.938 (1.00) |
| 16q loss | 37 (66%) | 19 |
0.153 (1.00) |
0.884 (1.00) |
| 17p loss | 34 (61%) | 22 |
0.159 (1.00) |
0.787 (1.00) |
| 17q loss | 17 (30%) | 39 |
0.0417 (1.00) |
0.636 (1.00) |
| 18p loss | 18 (32%) | 38 |
0.597 (1.00) |
0.99 (1.00) |
| 18q loss | 20 (36%) | 36 |
0.278 (1.00) |
0.754 (1.00) |
| 19p loss | 15 (27%) | 41 |
0.971 (1.00) |
0.393 (1.00) |
| 19q loss | 13 (23%) | 43 |
0.563 (1.00) |
0.196 (1.00) |
| 20p loss | 7 (12%) | 49 |
0.889 (1.00) |
0.664 (1.00) |
| 20q loss | 4 (7%) | 52 |
0.864 (1.00) |
0.891 (1.00) |
| 21q loss | 17 (30%) | 39 |
0.179 (1.00) |
0.151 (1.00) |
| 22q loss | 33 (59%) | 23 |
0.319 (1.00) |
0.611 (1.00) |
| xq loss | 19 (34%) | 37 |
0.39 (1.00) |
0.909 (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 = 2
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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 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
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.