This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 20 arm-level events and 4 clinical features across 25 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 | GENDER | RACE | ||
nCNV (%) | nWild-Type | logrank test | Wilcoxon-test | Fisher's exact test | Fisher's exact test | |
1q gain | 4 (16%) | 21 |
0.405 (1.00) |
0.458 (1.00) |
1 (1.00) |
0.257 (1.00) |
3p gain | 4 (16%) | 21 |
0.418 (1.00) |
0.882 (1.00) |
0.604 (1.00) |
0.386 (1.00) |
3q gain | 5 (20%) | 20 |
0.418 (1.00) |
0.634 (1.00) |
0.341 (1.00) |
0.153 (1.00) |
6p gain | 3 (12%) | 22 |
0.617 (1.00) |
0.503 (1.00) |
1 (1.00) |
0.602 (1.00) |
7p gain | 8 (32%) | 17 |
0.317 (1.00) |
0.62 (1.00) |
1 (1.00) |
0.778 (1.00) |
7q gain | 7 (28%) | 18 |
0.522 (1.00) |
0.379 (1.00) |
0.407 (1.00) |
0.563 (1.00) |
10p gain | 3 (12%) | 22 |
0.724 (1.00) |
0.357 (1.00) |
0.23 (1.00) |
0.607 (1.00) |
11p gain | 3 (12%) | 22 |
0.569 (1.00) |
0.558 (1.00) |
0.23 (1.00) |
1 (1.00) |
11q gain | 7 (28%) | 18 |
0.249 (1.00) |
0.102 (1.00) |
0.407 (1.00) |
1 (1.00) |
12p gain | 3 (12%) | 22 |
0.724 (1.00) |
0.0264 (1.00) |
1 (1.00) |
0.604 (1.00) |
12q gain | 3 (12%) | 22 |
0.724 (1.00) |
0.0264 (1.00) |
1 (1.00) |
0.606 (1.00) |
16p gain | 3 (12%) | 22 |
0.808 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
16q gain | 3 (12%) | 22 |
0.316 (1.00) |
0.615 (1.00) |
0.565 (1.00) |
1 (1.00) |
18p gain | 5 (20%) | 20 |
0.389 (1.00) |
0.434 (1.00) |
0.341 (1.00) |
0.152 (1.00) |
18q gain | 5 (20%) | 20 |
0.389 (1.00) |
0.434 (1.00) |
0.341 (1.00) |
0.153 (1.00) |
21q gain | 6 (24%) | 19 |
0.892 (1.00) |
0.799 (1.00) |
1 (1.00) |
1 (1.00) |
8p loss | 3 (12%) | 22 |
0.808 (1.00) |
0.315 (1.00) |
0.23 (1.00) |
0.111 (1.00) |
15q loss | 5 (20%) | 20 |
0.522 (1.00) |
0.759 (1.00) |
0.341 (1.00) |
0.69 (1.00) |
16q loss | 4 (16%) | 21 |
0.724 (1.00) |
1 (1.00) |
0.105 (1.00) |
0.389 (1.00) |
xq loss | 3 (12%) | 22 |
0.00468 (0.374) |
0.933 (1.00) |
0.23 (1.00) |
0.604 (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 = 25
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Number of significantly arm-level cnvs = 20
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Number of selected clinical features = 4
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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.