Correlation between copy number variations of arm-level result and molecular subtypes
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1TB1563
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 molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1TB1563
Overview
Introduction

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

Summary

Testing the association between copy number variation 7 arm-level results and molecular subtype 'METHLYATION_CNMF' across 18 patients, no significant finding detected with Q value < 0.25.

- No arm-level cnvs related to molecular subtypes.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 7 arm-level results and 1 molecular subtypes. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

|  | Molecular subtypes | METHLYATION CNMF
| nCNV (%) | nWild-Type | Fisher's exact test
3q gain | 0 (0%) | 15 | 1 (1.00)
7p gain | 0 (0%) | 14 | 1 (1.00)
7q gain | 0 (0%) | 15 | 1 (1.00)
11q gain | 0 (0%) | 14 | 1 (1.00)
18p gain | 0 (0%) | 15 | 1 (1.00)
18q gain | 0 (0%) | 15 | 1 (1.00)
21q gain | 0 (0%) | 13 | 1 (1.00)

Methods & Data
Input

- Mutation data file = broad_values_by_arm.mutsig.cluster.txt

- Molecular subtypes file = DLBC-TP.transferedmergedcluster.txt

- Number of patients = 18

- Number of significantly arm-level cnvs = 7

- Number of molecular subtypes = 1: 'METHLYATION_CNMF'

- Exclude genes that fewer than K tumors have mutations, K = 3

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] 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)
[2] 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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