This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 3 genes and 3 clinical features across 316 patients, no significant finding detected with Q value < 0.25.
-
No gene mutations related to clinical features.
Table 1. Get Full Table Overview of the association between mutation status of 3 genes and 3 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 |
KARNOFSKY PERFORMANCE SCORE |
||
| nMutated (%) | nWild-Type | logrank test | t-test | t-test | |
| TP53 | 276 (87%) | 40 |
0.389 (1.00) |
0.649 (1.00) |
0.585 (1.00) |
| TBP | 4 (1%) | 312 |
0.323 (1.00) |
0.191 (1.00) |
|
| SRC | 4 (1%) | 312 |
0.197 (1.00) |
0.621 (1.00) |
-
Mutation data file = transformed.cor.cli.txt
-
Clinical data file = OV-TP.merged_data.txt
-
Number of patients = 316
-
Number of significantly mutated genes = 3
-
Number of selected clinical features = 3
-
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.