This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between 'NFE2L2 MUTATION ANALYSIS' and 2 clinical features across 12 patients, no significant finding detected with Q value < 0.25.
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No gene mutations related to clinical features.
Table 1. Get Full Table Overview of the association between mutation status of 1 genes and 2 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.
|
Clinical Features |
Time to Death |
AGE | ||
| nMutated (%) | nWild-Type | logrank test | t-test | |
| NFE2L2 | 3 (25%) | 9 |
0.317 (0.635) |
0.391 (0.635) |
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Mutation data file = CESC.mutsig.cluster.txt
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Clinical data file = CESC.clin.merged.picked.txt
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Number of patients = 12
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Number of significantly mutated genes = 1: 'NFE2L2 MUTATION ANALYSIS'
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Number of selected clinical features = 2
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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 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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.