Correlation between copy number variation genes (focal events) and molecular subtypes
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
16 April 2014 | analyses__2014_04_16
Maintainer Information
Citation Information
doi:10.7908/C1PG1QBN
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 variation genes (focal events) and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1PG1QBN
Overview
Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and molecular subtypes.

Summary

Testing the association between copy number variation 24 focal events and 7 molecular subtypes across 28 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

- No focal cnvs related to molecuar subtypes.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 24 focal events and 7 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.

|  | Clinical Features | METHLYATION CNMF | MRNASEQ CNMF | MRNASEQ CHIERARCHICAL | MIRSEQ CNMF | MIRSEQ CHIERARCHICAL | MIRSEQ MATURE CNMF | MIRSEQ MATURE CHIERARCHICAL
| nCNV (%) | nWild-Type | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Chi-square test | Fisher's exact test | Fisher's exact test
amp 1q31 1 | 9 (32%) | 19 | 0.192 (1.00) | 0.228 (1.00) | 0.0346 (1.00) | 0.0461 (1.00) | 0.536 (1.00) | 0.874 (1.00) | 0.406 (1.00)
amp 2p15 | 8 (29%) | 20 | 0.00641 (1.00) | 0.686 (1.00) | 0.669 (1.00) | 1 (1.00) | 0.00518 (0.87) | 0.436 (1.00) | 1 (1.00)
amp 3q27 3 | 7 (25%) | 21 | 0.616 (1.00) | 0.396 (1.00) | 1 (1.00) | 0.385 (1.00) | 0.147 (1.00) | 0.418 (1.00) | 0.0646 (1.00)
amp 8q24 12 | 6 (21%) | 22 | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.349 (1.00) | 0.291 (1.00) | 1 (1.00)
amp 12q13 12 | 5 (18%) | 23 | 0.312 (1.00) | 1 (1.00) | 0.128 (1.00) | 0.326 (1.00) | 0.575 (1.00) | 1 (1.00) | 1 (1.00)
amp 16p11 2 | 6 (21%) | 22 | 0.11 (1.00) | 0.173 (1.00) | 0.375 (1.00) | 0.648 (1.00) | 0.185 (1.00) | 0.842 (1.00) | 0.638 (1.00)
amp xq27 3 | 6 (21%) | 22 | 0.11 (1.00) | 0.655 (1.00) | 0.375 (1.00) | 0.648 (1.00) | 0.717 (1.00) | 0.842 (1.00) | 0.638 (1.00)
del 1p22 1 | 4 (14%) | 24 | 0.478 (1.00) | 1 (1.00) | 0.601 (1.00) | 0.326 (1.00) | 0.846 (1.00) | 1 (1.00) | 1 (1.00)
del 1p13 1 | 7 (25%) | 21 | 0.285 (1.00) | 0.67 (1.00) | 0.207 (1.00) | 0.0329 (1.00) | 0.67 (1.00) | 0.418 (1.00) | 0.204 (1.00)
del 1q43 | 7 (25%) | 21 | 0.285 (1.00) | 1 (1.00) | 0.674 (1.00) | 1 (1.00) | 0.977 (1.00) | 1 (1.00) | 1 (1.00)
del 2q23 1 | 6 (21%) | 22 | 1 (1.00) | 1 (1.00) | 0.634 (1.00) | 0.648 (1.00) | 0.838 (1.00) | 1 (1.00) | 0.621 (1.00)
del 6q14 1 | 9 (32%) | 19 | 0.467 (1.00) | 0.435 (1.00) | 0.417 (1.00) | 0.103 (1.00) | 0.163 (1.00) | 0.874 (1.00) | 0.219 (1.00)
del 6q23 3 | 7 (25%) | 21 | 0.285 (1.00) | 0.0836 (1.00) | 0.0302 (1.00) | 0.385 (1.00) | 0.222 (1.00) | 1 (1.00) | 1 (1.00)
del 8p23 1 | 4 (14%) | 24 | 1 (1.00) | 0.311 (1.00) | 0.116 (1.00) | 0.326 (1.00) | 0.551 (1.00) | 1 (1.00) | 1 (1.00)
del 8q12 1 | 4 (14%) | 24 | 1 (1.00) | 0.311 (1.00) | 0.116 (1.00) | 0.326 (1.00) | 0.846 (1.00) | 1 (1.00) | 1 (1.00)
del 9p21 3 | 9 (32%) | 19 | 0.0481 (1.00) | 0.114 (1.00) | 0.417 (1.00) | 0.42 (1.00) | 0.0149 (1.00) | 0.281 (1.00) | 0.219 (1.00)
del 10q23 31 | 5 (18%) | 23 | 0.823 (1.00) | 0.639 (1.00) | 0.315 (1.00) | 0.648 (1.00) | 0.687 (1.00) | 1 (1.00) | 1 (1.00)
del 13q14 2 | 3 (11%) | 25 | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.15 (1.00) | 0.751 (1.00) | 1 (1.00)
del 13q33 3 | 4 (14%) | 24 | 0.787 (1.00) | 0.102 (1.00) | 0.265 (1.00) | 0.222 (1.00) | 0.0188 (1.00) | 0.751 (1.00) | 0.535 (1.00)
del 15q15 1 | 8 (29%) | 20 | 0.413 (1.00) | 1 (1.00) | 0.4 (1.00) | 0.678 (1.00) | 0.486 (1.00) | 1 (1.00) | 0.678 (1.00)
del 15q21 1 | 9 (32%) | 19 | 0.656 (1.00) | 1 (1.00) | 0.677 (1.00) | 1 (1.00) | 0.633 (1.00) | 1 (1.00) | 1 (1.00)
del 16p13 13 | 4 (14%) | 24 | 0.478 (1.00) | 0.311 (1.00) | 0.601 (1.00) | 0.326 (1.00) | 0.0852 (1.00) | 0.28 (1.00) | 0.264 (1.00)
del 16q23 1 | 5 (18%) | 23 | 0.823 (1.00) | 0.639 (1.00) | 0.315 (1.00) | 0.648 (1.00) | 0.609 (1.00) | 1 (1.00) | 0.621 (1.00)
del 17q24 1 | 4 (14%) | 24 | 0.0918 (1.00) | 1 (1.00) | 0.601 (1.00) | 1 (1.00) | 0.276 (1.00) | 0.502 (1.00) | 0.613 (1.00)

Methods & Data
Input

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

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

- Number of patients = 28

- Number of significantly focal cnvs = 24

- Number of molecular subtypes = 7

- Exclude genes that fewer than K tumors have alterations, 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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[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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