Correlation between copy number variation genes (focal events) 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/C12V2DFV
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 variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C12V2DFV
Overview
Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.

Summary

Testing the association between copy number variation 9 focal events and 3 clinical features across 16 patients, no significant finding detected with Q value < 0.25.

- No focal 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 focal 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
AMP PEAK 1(2P16 1) MUTATION ANALYSIS | 6 (38%) | 10 | 0.0806 (1.00) | 0.801 (1.00) | 0.633 (1.00)
DEL PEAK 2(1Q43) MUTATION ANALYSIS | 3 (19%) | 13 | 0.683 (1.00) | 0.944 (1.00) | 0.213 (1.00)
DEL PEAK 3(2Q22 3) MUTATION ANALYSIS | 4 (25%) | 12 | 0.698 (1.00) | 0.686 (1.00) | 0.0885 (1.00)
DEL PEAK 4(6Q14 1) MUTATION ANALYSIS | 3 (19%) | 13 | 0.333 (1.00) | 0.163 (1.00) | 0.55 (1.00)
DEL PEAK 5(6Q23 3) MUTATION ANALYSIS | 3 (19%) | 13 | 0.912 (1.00) | 0.0369 (0.995) | 0.0625 (1.00)
DEL PEAK 7(9P21 3) MUTATION ANALYSIS | 4 (25%) | 12 | 0.333 (1.00) | 0.851 (1.00) | 1 (1.00)
DEL PEAK 9(13Q33 3) MUTATION ANALYSIS | 3 (19%) | 13 | 0.388 (1.00) | 0.224 (1.00) | 0.213 (1.00)
DEL PEAK 10(15Q15 1) MUTATION ANALYSIS | 4 (25%) | 12 | 0.778 (1.00) | 0.953 (1.00) | 0.585 (1.00)
DEL PEAK 11(16P13 13) MUTATION ANALYSIS | 3 (19%) | 13 | 0.683 (1.00) | 0.901 (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 focal cnvs = 9

- Number of selected clinical features = 3

- Exclude genes 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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