This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 20 arm-level results and 2 clinical features across 14 patients, no significant finding detected with Q value < 0.25.
-
No arm-level cnvs related to clinical features.
Table 1. Get Full Table Overview of the association between significant copy number variation of 20 arm-level results 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 | ||
| nCNV (%) | nWild-Type | logrank test | t-test | |
| 1q gain | 6 (43%) | 8 |
0.0455 (1.00) |
0.0583 (1.00) |
| 3p gain | 3 (21%) | 11 |
1 (1.00) |
0.566 (1.00) |
| 3q gain | 8 (57%) | 6 |
0.414 (1.00) |
0.939 (1.00) |
| 5p gain | 4 (29%) | 10 |
0.617 (1.00) |
0.151 (1.00) |
| 6p gain | 3 (21%) | 11 |
1 (1.00) |
0.905 (1.00) |
| 8q gain | 5 (36%) | 9 |
0.617 (1.00) |
0.973 (1.00) |
| 20q gain | 3 (21%) | 11 |
1 (1.00) |
0.42 (1.00) |
| 3p loss | 6 (43%) | 8 |
0.617 (1.00) |
0.0724 (1.00) |
| 4p loss | 6 (43%) | 8 |
0.414 (1.00) |
0.671 (1.00) |
| 5p loss | 3 (21%) | 11 |
0.414 (1.00) |
0.378 (1.00) |
| 5q loss | 7 (50%) | 7 |
0.414 (1.00) |
0.526 (1.00) |
| 8p loss | 4 (29%) | 10 |
0.221 (1.00) |
0.236 (1.00) |
| 10p loss | 4 (29%) | 10 |
0.617 (1.00) |
0.296 (1.00) |
| 10q loss | 5 (36%) | 9 |
0.221 (1.00) |
0.231 (1.00) |
| 11p loss | 6 (43%) | 8 |
0.414 (1.00) |
0.157 (1.00) |
| 11q loss | 5 (36%) | 9 |
0.414 (1.00) |
0.393 (1.00) |
| 12p loss | 4 (29%) | 10 |
0.0455 (1.00) |
0.0823 (1.00) |
| 13q loss | 7 (50%) | 7 |
0.221 (1.00) |
0.152 (1.00) |
| 17p loss | 5 (36%) | 9 |
0.414 (1.00) |
0.938 (1.00) |
| 19p loss | 3 (21%) | 11 |
0.414 (1.00) |
0.307 (1.00) |
-
Mutation data file = broad_values_by_arm.mutsig.cluster.txt
-
Clinical data file = CESC.clin.merged.picked.txt
-
Number of patients = 14
-
Number of significantly arm-level cnvs = 20
-
Number of selected clinical features = 2
-
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.