This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.
Testing the association between copy number variation 37 focal events and 4 clinical features across 35 patients, no significant finding detected with Q value < 0.25.
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No focal cnvs related to clinical features.
Clinical Features |
Time to Death |
AGE | GENDER | RACE | ||
nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | |
amp 1q24 2 | 11 (31%) | 24 |
0.175 (1.00) |
0.213 (1.00) |
1 (1.00) |
0.414 (1.00) |
amp 2p16 1 | 9 (26%) | 26 |
0.138 (1.00) |
0.521 (1.00) |
0.443 (1.00) |
0.586 (1.00) |
amp 3q27 3 | 10 (29%) | 25 |
0.247 (1.00) |
0.956 (1.00) |
0.711 (1.00) |
0.797 (1.00) |
amp 3q29 | 9 (26%) | 26 |
0.26 (1.00) |
0.558 (1.00) |
0.443 (1.00) |
1 (1.00) |
amp 5p13 1 | 4 (11%) | 31 |
0.461 (1.00) |
0.5 (1.00) |
0.603 (1.00) |
0.646 (1.00) |
amp 7p22 3 | 11 (31%) | 24 |
0.114 (1.00) |
0.569 (1.00) |
0.471 (1.00) |
1 (1.00) |
amp 7q21 3 | 10 (29%) | 25 |
0.357 (1.00) |
0.855 (1.00) |
0.711 (1.00) |
0.478 (1.00) |
amp 8q24 23 | 8 (23%) | 27 |
0.469 (1.00) |
0.906 (1.00) |
0.228 (1.00) |
0.407 (1.00) |
amp 12q21 1 | 9 (26%) | 26 |
0.508 (1.00) |
0.242 (1.00) |
0.443 (1.00) |
0.204 (1.00) |
amp 16p12 2 | 8 (23%) | 27 |
0.725 (1.00) |
0.504 (1.00) |
0.691 (1.00) |
1 (1.00) |
amp 18q21 2 | 11 (31%) | 24 |
0.991 (1.00) |
0.499 (1.00) |
1 (1.00) |
0.00269 (0.395) |
amp 19q13 43 | 7 (20%) | 28 |
0.0451 (1.00) |
0.62 (1.00) |
0.402 (1.00) |
0.524 (1.00) |
amp xq27 3 | 6 (17%) | 29 |
0.486 (1.00) |
1 (1.00) |
1 (1.00) |
0.32 (1.00) |
del 1p36 32 | 8 (23%) | 27 |
0.877 (1.00) |
0.529 (1.00) |
1 (1.00) |
0.559 (1.00) |
del 1p13 1 | 10 (29%) | 25 |
0.332 (1.00) |
0.235 (1.00) |
0.146 (1.00) |
0.799 (1.00) |
del 1q43 | 9 (26%) | 26 |
0.417 (1.00) |
0.108 (1.00) |
0.443 (1.00) |
0.59 (1.00) |
del 2q23 1 | 6 (17%) | 29 |
0.783 (1.00) |
0.584 (1.00) |
0.177 (1.00) |
0.236 (1.00) |
del 3p21 31 | 3 (9%) | 32 |
0.125 (1.00) |
0.603 (1.00) |
0.626 (1.00) |
|
del 4q35 1 | 8 (23%) | 27 |
0.298 (1.00) |
0.443 (1.00) |
1 (1.00) |
0.41 (1.00) |
del 6p21 32 | 4 (11%) | 31 |
0.559 (1.00) |
0.938 (1.00) |
1 (1.00) |
0.399 (1.00) |
del 6q14 1 | 12 (34%) | 23 |
0.14 (1.00) |
0.59 (1.00) |
0.489 (1.00) |
0.822 (1.00) |
del 6q22 32 | 10 (29%) | 25 |
0.647 (1.00) |
0.826 (1.00) |
0.471 (1.00) |
0.615 (1.00) |
del 6q23 3 | 8 (23%) | 27 |
0.584 (1.00) |
0.937 (1.00) |
0.443 (1.00) |
0.182 (1.00) |
del 8p23 2 | 7 (20%) | 28 |
0.589 (1.00) |
0.563 (1.00) |
0.0877 (1.00) |
0.522 (1.00) |
del 8q12 1 | 7 (20%) | 28 |
0.235 (1.00) |
0.386 (1.00) |
1 (1.00) |
1 (1.00) |
del 9p21 3 | 13 (37%) | 22 |
0.712 (1.00) |
0.539 (1.00) |
0.733 (1.00) |
0.219 (1.00) |
del 10q23 31 | 5 (14%) | 30 |
0.479 (1.00) |
0.31 (1.00) |
0.338 (1.00) |
0.698 (1.00) |
del 12p13 2 | 7 (20%) | 28 |
0.445 (1.00) |
0.397 (1.00) |
0.402 (1.00) |
0.523 (1.00) |
del 13q14 2 | 3 (9%) | 32 |
0.123 (1.00) |
0.637 (1.00) |
0.229 (1.00) |
0.629 (1.00) |
del 15q21 1 | 9 (26%) | 26 |
0.956 (1.00) |
0.821 (1.00) |
0.121 (1.00) |
0.442 (1.00) |
del 16p13 13 | 4 (11%) | 31 |
0.661 (1.00) |
0.678 (1.00) |
0.104 (1.00) |
0.649 (1.00) |
del 16q23 1 | 5 (14%) | 30 |
0.661 (1.00) |
0.62 (1.00) |
0.338 (1.00) |
0.436 (1.00) |
del 17p13 1 | 6 (17%) | 29 |
0.582 (1.00) |
0.742 (1.00) |
0.177 (1.00) |
0.718 (1.00) |
del 17q24 1 | 3 (9%) | 32 |
0.0114 (1.00) |
0.86 (1.00) |
1 (1.00) |
0.625 (1.00) |
del 19p13 2 | 3 (9%) | 32 |
0.623 (1.00) |
0.68 (1.00) |
0.603 (1.00) |
0.627 (1.00) |
del 19q13 2 | 3 (9%) | 32 |
0.559 (1.00) |
0.768 (1.00) |
1 (1.00) |
1 (1.00) |
del 22q13 31 | 5 (14%) | 30 |
0.589 (1.00) |
0.396 (1.00) |
0.338 (1.00) |
0.436 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = DLBC-TP.merged_data.txt
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Number of patients = 35
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Number of significantly focal cnvs = 37
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Number of selected clinical features = 4
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Exclude genes 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.