Correlation between copy number variations of arm-level result and selected clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C13F4NCS
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 20 arm-level events and 4 clinical features across 25 patients, no significant finding detected with Q value < 0.25.

  • No arm-level cnvs related to clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 20 arm-level events and 4 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 GENDER RACE
nCNV (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test
1q gain 4 (16%) 21 0.405
(1.00)
0.458
(1.00)
1
(1.00)
0.257
(1.00)
3p gain 4 (16%) 21 0.418
(1.00)
0.882
(1.00)
0.604
(1.00)
0.386
(1.00)
3q gain 5 (20%) 20 0.418
(1.00)
0.634
(1.00)
0.341
(1.00)
0.153
(1.00)
6p gain 3 (12%) 22 0.617
(1.00)
0.503
(1.00)
1
(1.00)
0.602
(1.00)
7p gain 8 (32%) 17 0.317
(1.00)
0.62
(1.00)
1
(1.00)
0.778
(1.00)
7q gain 7 (28%) 18 0.522
(1.00)
0.379
(1.00)
0.407
(1.00)
0.563
(1.00)
10p gain 3 (12%) 22 0.724
(1.00)
0.357
(1.00)
0.23
(1.00)
0.607
(1.00)
11p gain 3 (12%) 22 0.569
(1.00)
0.558
(1.00)
0.23
(1.00)
1
(1.00)
11q gain 7 (28%) 18 0.249
(1.00)
0.102
(1.00)
0.407
(1.00)
1
(1.00)
12p gain 3 (12%) 22 0.724
(1.00)
0.0264
(1.00)
1
(1.00)
0.604
(1.00)
12q gain 3 (12%) 22 0.724
(1.00)
0.0264
(1.00)
1
(1.00)
0.606
(1.00)
16p gain 3 (12%) 22 0.808
(1.00)
1
(1.00)
1
(1.00)
1
(1.00)
16q gain 3 (12%) 22 0.316
(1.00)
0.615
(1.00)
0.565
(1.00)
1
(1.00)
18p gain 5 (20%) 20 0.389
(1.00)
0.434
(1.00)
0.341
(1.00)
0.152
(1.00)
18q gain 5 (20%) 20 0.389
(1.00)
0.434
(1.00)
0.341
(1.00)
0.153
(1.00)
21q gain 6 (24%) 19 0.892
(1.00)
0.799
(1.00)
1
(1.00)
1
(1.00)
8p loss 3 (12%) 22 0.808
(1.00)
0.315
(1.00)
0.23
(1.00)
0.111
(1.00)
15q loss 5 (20%) 20 0.522
(1.00)
0.759
(1.00)
0.341
(1.00)
0.69
(1.00)
16q loss 4 (16%) 21 0.724
(1.00)
1
(1.00)
0.105
(1.00)
0.389
(1.00)
xq loss 3 (12%) 22 0.00468
(0.374)
0.933
(1.00)
0.23
(1.00)
0.604
(1.00)
Methods & Data
Input
  • Copy number data file = transformed.cor.cli.txt

  • Clinical data file = DLBC-TP.merged_data.txt

  • Number of patients = 25

  • Number of significantly arm-level cnvs = 20

  • Number of selected clinical features = 4

  • Exclude regions that fewer than K tumors have mutations, K = 3

Survival analysis

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

Fisher's exact test

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

Q value calculation

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.

Download Results

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.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[3] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)