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
doi:10.7908/C13F4NCS
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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
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