Correlation between copy number variations of arm-level result and selected clinical features
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C16M355K
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16M355K
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 9 arm-level events and 3 clinical features across 16 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 9 arm-level events 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 | GENDER
| nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test
1Q GAIN MUTATION ANALYSIS | 3 (19%) | 13 | 0.48 (1.00) | 0.711 (1.00) | 0.55 (1.00)
3Q GAIN MUTATION ANALYSIS | 3 (19%) | 13 | 0.424 (1.00) | 0.964 (1.00) | 1 (1.00)
7P GAIN MUTATION ANALYSIS | 4 (25%) | 12 | 0.333 (1.00) | 0.649 (1.00) | 1 (1.00)
7Q GAIN MUTATION ANALYSIS | 3 (19%) | 13 | 0.578 (1.00) | 0.996 (1.00) | 1 (1.00)
11Q GAIN MUTATION ANALYSIS | 4 (25%) | 12 | 0.302 (1.00) | 0.417 (1.00) | 0.585 (1.00)
18P GAIN MUTATION ANALYSIS | 3 (19%) | 13 | 0.388 (1.00) | 0.0557 (1.00) | 1 (1.00)
18Q GAIN MUTATION ANALYSIS | 3 (19%) | 13 | 0.388 (1.00) | 0.0557 (1.00) | 1 (1.00)
21Q GAIN MUTATION ANALYSIS | 5 (31%) | 11 | 0.784 (1.00) | 0.649 (1.00) | 1 (1.00)
XQ LOSS MUTATION ANALYSIS | 3 (19%) | 13 | 0.0143 (0.386) | 0.944 (1.00) | 0.213 (1.00)

Methods & Data
Input

- Copy number data file = transformed.cor.cli.txt

- Clinical data file = DLBC-TP.clin.merged.picked.txt

- Number of patients = 16

- Number of significantly arm-level cnvs = 9

- Number of selected clinical features = 3

- 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

Student's t-test analysis

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] 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)
[4] 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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