This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 4 genes and 8 clinical features across 109 patients, no significant finding detected with Q value < 0.25.
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No gene mutations related to clinical features.
Clinical Features |
Time to Death |
AGE |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
PATHOLOGY M STAGE |
GENDER |
KARNOFSKY PERFORMANCE SCORE |
||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | |
CDC27 | 4 (4%) | 105 |
0.586 (1.00) |
0.776 (1.00) |
0.364 (1.00) |
0.774 (1.00) |
0.0746 (1.00) |
0.587 (1.00) |
||
IL32 | 4 (4%) | 105 |
0.105 (1.00) |
0.352 (1.00) |
0.578 (1.00) |
0.774 (1.00) |
0.214 (1.00) |
0.307 (1.00) |
||
NF2 | 7 (6%) | 102 |
0.755 (1.00) |
0.298 (1.00) |
0.123 (1.00) |
0.372 (1.00) |
0.0472 (1.00) |
0.0357 (0.821) |
1 (1.00) |
|
PPARGC1B | 3 (3%) | 106 |
0.747 (1.00) |
0.394 (1.00) |
0.351 (1.00) |
1 (1.00) |
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Mutation data file = KIRP-TP.mutsig.cluster.txt
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Clinical data file = KIRP-TP.clin.merged.picked.txt
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Number of patients = 109
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Number of significantly mutated genes = 4
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Number of selected clinical features = 8
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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 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.