Correlation between copy number variations of arm-level result and selected clinical features

Pheochromocytoma and Paraganglioma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QV3K62

Overview

Introduction

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

Summary

Testing the association between copy number variation 10 arm-level events and 2 clinical features across 9 patients, no significant finding detected with Q value < 0.25.

No arm-level cnvs related to clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between significant copy number variation of 10 arm-level events and 2 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


		Clinical Features	AGE	GENDER
	nCNV (%)	nWild-Type	t-test	Fisher's exact test
7p gain	3 (33%)	6	0.211 (1.00)	0.226 (1.00)
1p loss	5 (56%)	4	0.589 (1.00)	1 (1.00)
1q loss	3 (33%)	6	0.759 (1.00)	0.226 (1.00)
3p loss	5 (56%)	4	0.96 (1.00)	0.524 (1.00)
3q loss	5 (56%)	4	0.447 (1.00)	0.524 (1.00)
11q loss	3 (33%)	6	0.677 (1.00)	0.226 (1.00)
17p loss	3 (33%)	6	0.583 (1.00)	1 (1.00)
21q loss	4 (44%)	5	0.398 (1.00)	0.524 (1.00)
22q loss	4 (44%)	5	0.0824 (1.00)	1 (1.00)
xq loss	5 (56%)	4	0.32 (1.00)	0.524 (1.00)

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 9
Number of significantly arm-level cnvs = 10
Number of selected clinical features = 2
Exclude regions that fewer than K tumors have mutations, K = 3

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[2] 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)

[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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