This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 9 arm-level events and 3 clinical features across 16 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 | ||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | |
1Q GAIN MUTATION ANALYSIS | 3 (19%) | 13 |
0.48 (1.00) |
0.711 (1.00) |
0.55 (1.00) |
3Q GAIN MUTATION ANALYSIS | 3 (19%) | 13 |
0.424 (1.00) |
0.964 (1.00) |
1 (1.00) |
7P GAIN MUTATION ANALYSIS | 4 (25%) | 12 |
0.333 (1.00) |
0.649 (1.00) |
1 (1.00) |
7Q GAIN MUTATION ANALYSIS | 3 (19%) | 13 |
0.578 (1.00) |
0.996 (1.00) |
1 (1.00) |
11Q GAIN MUTATION ANALYSIS | 4 (25%) | 12 |
0.302 (1.00) |
0.417 (1.00) |
0.585 (1.00) |
18P GAIN MUTATION ANALYSIS | 3 (19%) | 13 |
0.388 (1.00) |
0.0557 (1.00) |
1 (1.00) |
18Q GAIN MUTATION ANALYSIS | 3 (19%) | 13 |
0.388 (1.00) |
0.0557 (1.00) |
1 (1.00) |
21Q GAIN MUTATION ANALYSIS | 5 (31%) | 11 |
0.784 (1.00) |
0.649 (1.00) |
1 (1.00) |
XQ LOSS MUTATION ANALYSIS | 3 (19%) | 13 |
0.0143 (0.386) |
0.944 (1.00) |
0.213 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = DLBC-TP.clin.merged.picked.txt
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Number of patients = 16
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Number of significantly arm-level cnvs = 9
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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 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 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.