Correlation between copy number variations of arm-level result 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/C1T72G2C
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 molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1T72G2C
Overview
Introduction

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

Summary

Testing the association between copy number variation 24 arm-level events and 7 molecular subtypes across 28 patients, no significant finding detected with P value < 0.05 and 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 24 arm-level 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
1q gain | 4 (14%) | 24 | 1 (1.00) | 1 (1.00) | 0.601 (1.00) | 0.326 (1.00) | 0.359 (1.00) | 0.28 (1.00) | 1 (1.00)
3p gain | 5 (18%) | 23 | 0.823 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.529 (1.00) | 1 (1.00) | 0.326 (1.00)
3q gain | 6 (21%) | 22 | 0.692 (1.00) | 1 (1.00) | 0.634 (1.00) | 1 (1.00) | 0.673 (1.00) | 1 (1.00) | 0.638 (1.00)
6p gain | 3 (11%) | 25 | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.596 (1.00) | 0.326 (1.00) | 0.751 (1.00) | 1 (1.00)
7p gain | 8 (29%) | 20 | 0.287 (1.00) | 1 (1.00) | 0.4 (1.00) | 1 (1.00) | 0.499 (1.00) | 1 (1.00) | 0.415 (1.00)
7q gain | 7 (25%) | 21 | 0.616 (1.00) | 0.67 (1.00) | 0.207 (1.00) | 0.678 (1.00) | 0.365 (1.00) | 1 (1.00) | 1 (1.00)
10p gain | 3 (11%) | 25 | 1 (1.00) | 0.583 (1.00) | 1 (1.00) | 0.596 (1.00) | 0.566 (1.00) | 1 (1.00) | 1 (1.00)
11p gain | 4 (14%) | 24 | 0.478 (1.00) | 0.6 (1.00) | 0.265 (1.00) | 0.0978 (1.00) | 0.709 (1.00) | 1 (1.00) | 0.613 (1.00)
11q gain | 8 (29%) | 20 | 0.547 (1.00) | 0.686 (1.00) | 0.194 (1.00) | 0.385 (1.00) | 0.222 (1.00) | 0.632 (1.00) | 1 (1.00)
12p gain | 3 (11%) | 25 | 1 (1.00) | 0.583 (1.00) | 0.533 (1.00) | 1 (1.00) | 0.906 (1.00) | 1 (1.00) | 1 (1.00)
12q gain | 4 (14%) | 24 | 0.478 (1.00) | 1 (1.00) | 0.265 (1.00) | 0.596 (1.00) | 0.709 (1.00) | 1 (1.00) | 0.613 (1.00)
16p gain | 4 (14%) | 24 | 0.478 (1.00) | 0.6 (1.00) | 1 (1.00) | 0.596 (1.00) | 0.435 (1.00) | 0.28 (1.00) | 0.128 (1.00)
16q gain | 4 (14%) | 24 | 0.0918 (1.00) | 0.6 (1.00) | 1 (1.00) | 1 (1.00) | 0.276 (1.00) | 1 (1.00) | 0.613 (1.00)
18p gain | 5 (18%) | 23 | 0.823 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.838 (1.00) | 1 (1.00) | 1 (1.00)
18q gain | 5 (18%) | 23 | 0.823 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.838 (1.00) | 1 (1.00) | 1 (1.00)
21q gain | 6 (21%) | 22 | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.165 (1.00) | 0.0148 (1.00) | 0.314 (1.00) | 0.124 (1.00)
xq gain | 3 (11%) | 25 | 0.179 (1.00) | 1 (1.00) | 0.533 (1.00) | 1 (1.00) | 0.393 (1.00) | 0.751 (1.00) | 0.535 (1.00)
1p loss | 3 (11%) | 25 | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.596 (1.00) | 0.607 (1.00) | 1 (1.00) | 1 (1.00)
6q loss | 3 (11%) | 25 | 1 (1.00) | 1 (1.00) | 0.533 (1.00) | 0.222 (1.00) | 0.607 (1.00) | 1 (1.00) | 1 (1.00)
8p loss | 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)
15q loss | 5 (18%) | 23 | 0.312 (1.00) | 0.639 (1.00) | 0.315 (1.00) | 0.648 (1.00) | 0.433 (1.00) | 1 (1.00) | 0.621 (1.00)
16q loss | 4 (14%) | 24 | 1 (1.00) | 1 (1.00) | 0.601 (1.00) | 1 (1.00) | 0.575 (1.00) | 1 (1.00) | 1 (1.00)
17p loss | 3 (11%) | 25 | 1 (1.00) | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.607 (1.00) | 0.751 (1.00) | 0.535 (1.00)
xq loss | 3 (11%) | 25 | 0.179 (1.00) | 0.583 (1.00) | 0.284 (1.00) | 0.596 (1.00) | 0.15 (1.00) | 0.0862 (1.00) | 0.274 (1.00)

Methods & Data
Input

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

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

- Number of patients = 28

- Number of significantly arm-level cnvs = 24

- Number of molecular subtypes = 7

- 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

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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