This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between 'CTNNB1 MUTATION ANALYSIS' and 5 clinical features across 10 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 |
GENDER | ||
nMutated (%) | nWild-Type | logrank test | t-test | Fisher's exact test | NULL | Fisher's exact test | |
CTNNB1 | 4 (40%) | 6 |
0.0808 (0.323) |
0.286 (0.573) |
0.127 (0.381) |
0.524 (0.573) |
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Mutation data file = transformed.cor.cli.txt
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Clinical data file = ACC-TP.merged_data.txt
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Number of patients = 10
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Number of significantly mutated genes = 1: 'CTNNB1 MUTATION ANALYSIS'
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Number of selected clinical features = 5
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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.