Correlation between gene mutation status and molecular subtypes

Ovarian Serous Cystadenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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 (2013): Correlation between gene mutation status and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1X06534

Overview

Introduction

This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.

Summary

Testing the association between mutation status of 3 genes and 14 molecular subtypes across 316 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

No gene mutations related to molecuar subtypes.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between mutation status of 3 genes and 14 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	MRNA CNMF	MRNA CHIERARCHICAL	MIR CNMF	MIR CHIERARCHICAL	CN CNMF	METHLYATION CNMF	RPPA CNMF	RPPA CHIERARCHICAL	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRSEQ MATURE CNMF	MIRSEQ MATURE CHIERARCHICAL
	nMutated (%)	nWild-Type	Fisher's exact test	Fisher's exact test	Fisher's exact test	Chi-square 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	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
TP53	276 (87%)	40	0.827 (1.00)	0.414 (1.00)	0.502 (1.00)	0.367 (1.00)	0.716 (1.00)	0.771 (1.00)	0.411 (1.00)	0.313 (1.00)	0.779 (1.00)	0.898 (1.00)	0.326 (1.00)	0.186 (1.00)	0.0412 (1.00)	0.396 (1.00)
TBP	4 (1%)	312	1 (1.00)	0.837 (1.00)	0.692 (1.00)	0.596 (1.00)	0.0357 (1.00)	0.018 (0.684)	0.131 (1.00)	0.872 (1.00)	1 (1.00)	1 (1.00)	0.689 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
SRC	4 (1%)	312	0.838 (1.00)	0.837 (1.00)	0.566 (1.00)	0.16 (1.00)	0.127 (1.00)	0.464 (1.00)					0.483 (1.00)	0.325 (1.00)	0.591 (1.00)	0.325 (1.00)

Methods & Data

Input

Mutation data file = OV-TP.mutsig.cluster.txt
Molecular subtypes file = OV-TP.transferedmergedcluster.txt
Number of patients = 316
Number of significantly mutated genes = 3
Number of Molecular subtypes = 14
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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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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