This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 43 arm-level events and 3 clinical features across 57 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 43 arm-level events and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.
|
Clinical Features |
AGE | GENDER | RACE | ||
| nCNV (%) | nWild-Type | Wilcoxon-test | Fisher's exact test | Fisher's exact test | |
| 1p gain | 3 (5%) | 54 |
0.217 (1.00) |
0.279 (1.00) |
|
| 1q gain | 6 (11%) | 51 |
0.907 (1.00) |
1 (1.00) |
1 (1.00) |
| 5p gain | 3 (5%) | 54 |
0.334 (1.00) |
0.545 (1.00) |
1 (1.00) |
| 5q gain | 3 (5%) | 54 |
1 (1.00) |
0.545 (1.00) |
1 (1.00) |
| 7p gain | 11 (19%) | 46 |
0.856 (1.00) |
0.168 (1.00) |
0.556 (1.00) |
| 7q gain | 9 (16%) | 48 |
0.991 (1.00) |
0.0536 (1.00) |
0.614 (1.00) |
| 8p gain | 5 (9%) | 52 |
0.778 (1.00) |
0.647 (1.00) |
0.647 (1.00) |
| 8q gain | 6 (11%) | 51 |
0.658 (1.00) |
1 (1.00) |
1 (1.00) |
| 10p gain | 5 (9%) | 52 |
0.0515 (1.00) |
1 (1.00) |
1 (1.00) |
| 10q gain | 5 (9%) | 52 |
0.0515 (1.00) |
1 (1.00) |
1 (1.00) |
| 12p gain | 4 (7%) | 53 |
0.888 (1.00) |
1 (1.00) |
1 (1.00) |
| 12q gain | 5 (9%) | 52 |
0.544 (1.00) |
0.647 (1.00) |
1 (1.00) |
| 13q gain | 4 (7%) | 53 |
0.731 (1.00) |
0.607 (1.00) |
1 (1.00) |
| 18p gain | 3 (5%) | 54 |
0.0829 (1.00) |
1 (1.00) |
1 (1.00) |
| 18q gain | 5 (9%) | 52 |
0.767 (1.00) |
0.647 (1.00) |
1 (1.00) |
| 19p gain | 7 (12%) | 50 |
0.609 (1.00) |
1 (1.00) |
0.718 (1.00) |
| 19q gain | 5 (9%) | 52 |
0.429 (1.00) |
1 (1.00) |
1 (1.00) |
| 20p gain | 4 (7%) | 53 |
0.9 (1.00) |
0.607 (1.00) |
1 (1.00) |
| 20q gain | 3 (5%) | 54 |
0.453 (1.00) |
0.279 (1.00) |
1 (1.00) |
| 1p loss | 33 (58%) | 24 |
0.378 (1.00) |
0.411 (1.00) |
0.836 (1.00) |
| 1q loss | 5 (9%) | 52 |
0.438 (1.00) |
1 (1.00) |
0.645 (1.00) |
| 2p loss | 3 (5%) | 54 |
0.391 (1.00) |
1 (1.00) |
0.461 (1.00) |
| 2q loss | 3 (5%) | 54 |
0.734 (1.00) |
0.279 (1.00) |
0.46 (1.00) |
| 3p loss | 28 (49%) | 29 |
0.774 (1.00) |
0.783 (1.00) |
0.19 (1.00) |
| 3q loss | 39 (68%) | 18 |
0.973 (1.00) |
0.14 (1.00) |
1 (1.00) |
| 4p loss | 5 (9%) | 52 |
0.413 (1.00) |
0.332 (1.00) |
1 (1.00) |
| 4q loss | 6 (11%) | 51 |
0.376 (1.00) |
0.654 (1.00) |
0.705 (1.00) |
| 5p loss | 3 (5%) | 54 |
0.217 (1.00) |
0.279 (1.00) |
1 (1.00) |
| 5q loss | 4 (7%) | 53 |
0.118 (1.00) |
0.607 (1.00) |
0.565 (1.00) |
| 6q loss | 7 (12%) | 50 |
0.111 (1.00) |
0.0447 (1.00) |
1 (1.00) |
| 8p loss | 5 (9%) | 52 |
0.374 (1.00) |
0.647 (1.00) |
0.347 (1.00) |
| 8q loss | 3 (5%) | 54 |
0.0928 (1.00) |
1 (1.00) |
1 (1.00) |
| 9p loss | 4 (7%) | 53 |
0.223 (1.00) |
0.286 (1.00) |
0.562 (1.00) |
| 9q loss | 5 (9%) | 52 |
0.652 (1.00) |
0.151 (1.00) |
0.348 (1.00) |
| 11p loss | 16 (28%) | 41 |
0.763 (1.00) |
0.216 (1.00) |
0.219 (1.00) |
| 11q loss | 16 (28%) | 41 |
0.894 (1.00) |
0.216 (1.00) |
0.693 (1.00) |
| 14q loss | 10 (18%) | 47 |
0.564 (1.00) |
0.298 (1.00) |
1 (1.00) |
| 16p loss | 3 (5%) | 54 |
0.858 (1.00) |
0.279 (1.00) |
1 (1.00) |
| 17p loss | 21 (37%) | 36 |
0.551 (1.00) |
0.778 (1.00) |
0.255 (1.00) |
| 17q loss | 9 (16%) | 48 |
0.801 (1.00) |
0.705 (1.00) |
1 (1.00) |
| 21q loss | 15 (26%) | 42 |
0.793 (1.00) |
0.537 (1.00) |
0.348 (1.00) |
| 22q loss | 23 (40%) | 34 |
0.474 (1.00) |
0.585 (1.00) |
0.458 (1.00) |
| xq loss | 20 (35%) | 37 |
0.88 (1.00) |
0.383 (1.00) |
0.0283 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = PCPG-TP.merged_data.txt
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Number of patients = 57
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Number of significantly arm-level cnvs = 43
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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 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.