Correlation between copy number variations of arm-level result and molecular subtypes
Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (Primary solid tumor)
15 July 2014 | analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C12J69KT
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/C12J69KT
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 | Fisher's exact test | Fisher's exact test | Fisher's exact test
1q gain | 4 (14%) | 24 | 1 (1.00) | 1 (1.00) | 0.638 (1.00) | 0.326 (1.00) | 0.804 (1.00) | 0.279 (1.00) | 0.69 (1.00)
3p gain | 5 (18%) | 23 | 1 (1.00) | 1 (1.00) | 0.673 (1.00) | 1 (1.00) | 0.439 (1.00) | 1 (1.00) | 0.0887 (1.00)
3q gain | 6 (21%) | 22 | 1 (1.00) | 1 (1.00) | 0.515 (1.00) | 1 (1.00) | 0.541 (1.00) | 1 (1.00) | 0.196 (1.00)
6p gain | 3 (11%) | 25 | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.596 (1.00) | 0.522 (1.00) | 0.751 (1.00) | 0.181 (1.00)
7p gain | 8 (29%) | 20 | 0.686 (1.00) | 1 (1.00) | 0.459 (1.00) | 1 (1.00) | 0.56 (1.00) | 1 (1.00) | 0.0167 (1.00)
7q gain | 7 (25%) | 21 | 1 (1.00) | 0.67 (1.00) | 0.123 (1.00) | 0.678 (1.00) | 0.507 (1.00) | 1 (1.00) | 0.0165 (1.00)
10p gain | 3 (11%) | 25 | 1 (1.00) | 0.583 (1.00) | 0.763 (1.00) | 0.596 (1.00) | 0.889 (1.00) | 1 (1.00) | 0.181 (1.00)
11p gain | 4 (14%) | 24 | 0.311 (1.00) | 0.6 (1.00) | 0.146 (1.00) | 0.0978 (1.00) | 0.547 (1.00) | 1 (1.00) | 1 (1.00)
11q gain | 8 (29%) | 20 | 0.41 (1.00) | 0.686 (1.00) | 0.0873 (1.00) | 0.385 (1.00) | 0.12 (1.00) | 0.631 (1.00) | 0.876 (1.00)
12p gain | 3 (11%) | 25 | 0.583 (1.00) | 0.583 (1.00) | 0.108 (1.00) | 1 (1.00) | 0.767 (1.00) | 1 (1.00) | 0.831 (1.00)
12q gain | 4 (14%) | 24 | 0.311 (1.00) | 1 (1.00) | 0.145 (1.00) | 0.596 (1.00) | 0.549 (1.00) | 1 (1.00) | 0.967 (1.00)
16p gain | 4 (14%) | 24 | 0.311 (1.00) | 0.6 (1.00) | 0.21 (1.00) | 0.596 (1.00) | 0.804 (1.00) | 0.276 (1.00) | 0.892 (1.00)
16q gain | 4 (14%) | 24 | 0.0349 (1.00) | 0.6 (1.00) | 1 (1.00) | 1 (1.00) | 0.802 (1.00) | 1 (1.00) | 1 (1.00)
18p gain | 5 (18%) | 23 | 1 (1.00) | 1 (1.00) | 0.676 (1.00) | 1 (1.00) | 0.857 (1.00) | 1 (1.00) | 0.0892 (1.00)
18q gain | 5 (18%) | 23 | 1 (1.00) | 1 (1.00) | 0.676 (1.00) | 1 (1.00) | 0.857 (1.00) | 1 (1.00) | 0.0892 (1.00)
21q gain | 6 (21%) | 22 | 1 (1.00) | 1 (1.00) | 0.425 (1.00) | 0.165 (1.00) | 0.122 (1.00) | 0.315 (1.00) | 0.193 (1.00)
xq gain | 3 (11%) | 25 | 0.0873 (1.00) | 1 (1.00) | 0.385 (1.00) | 1 (1.00) | 0.764 (1.00) | 0.75 (1.00) | 0.978 (1.00)
1p loss | 3 (11%) | 25 | 1 (1.00) | 1 (1.00) | 1 (1.00) | 0.596 (1.00) | 0.889 (1.00) | 1 (1.00) | 0.612 (1.00)
6q loss | 3 (11%) | 25 | 0.583 (1.00) | 1 (1.00) | 0.387 (1.00) | 0.222 (1.00) | 0.488 (1.00) | 1 (1.00) | 0.828 (1.00)
8p loss | 4 (14%) | 24 | 1 (1.00) | 0.311 (1.00) | 0.355 (1.00) | 0.326 (1.00) | 0.513 (1.00) | 1 (1.00) | 0.0661 (1.00)
15q loss | 5 (18%) | 23 | 0.333 (1.00) | 0.639 (1.00) | 0.449 (1.00) | 0.648 (1.00) | 0.438 (1.00) | 1 (1.00) | 0.024 (1.00)
16q loss | 4 (14%) | 24 | 1 (1.00) | 1 (1.00) | 0.641 (1.00) | 1 (1.00) | 0.863 (1.00) | 1 (1.00) | 0.435 (1.00)
17p loss | 3 (11%) | 25 | 1 (1.00) | 1 (1.00) | 0.766 (1.00) | 1 (1.00) | 0.887 (1.00) | 0.75 (1.00) | 0.231 (1.00)
xq loss | 3 (11%) | 25 | 0.0873 (1.00) | 0.583 (1.00) | 0.564 (1.00) | 0.596 (1.00) | 0.487 (1.00) | 0.0871 (1.00) | 0.341 (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

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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