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

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.

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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