Correlation between copy number variation genes (focal events) 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 variation genes (focal events) and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1M32TF6

Overview

Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.

Summary

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

No focal 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 focal 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
amp 14q24 3	4 (44%)	5	0.836 (1.00)	1 (1.00)
del 1p36 32	6 (67%)	3	0.507 (1.00)	1 (1.00)
del 3p24 1	4 (44%)	5	0.549 (1.00)	1 (1.00)
del 3q26 1	6 (67%)	3	0.0516 (0.981)	0.226 (1.00)
del 11q22 1	3 (33%)	6	0.677 (1.00)	0.226 (1.00)
del 17p13 2	4 (44%)	5	0.866 (1.00)	1 (1.00)
del 17q11 2	3 (33%)	6	0.612 (1.00)	1 (1.00)
del 22q12 3	5 (56%)	4	0.0296 (0.591)	0.524 (1.00)
del 22q13 31	5 (56%)	4	0.582 (1.00)	1 (1.00)
del xp21 1	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 focal cnvs = 10
Number of selected clinical features = 2
Exclude genes 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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