Correlation between gene mutation status and molecular subtypes

Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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): Kidney Renal Papillary Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between gene mutation status and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1BV7DJK

Overview

Introduction

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

Summary

Testing the association between mutation status of 9 genes and 8 molecular subtypes across 111 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 9 genes and 8 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	CN CNMF	METHLYATION CNMF	MRNASEQ CNMF	MRNASEQ CHIERARCHICAL	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
	nMutated (%)	nWild-Type	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
MET	9 (8%)	102	1 (1.00)		0.346 (1.00)	0.502 (1.00)	0.773 (1.00)	0.752 (1.00)	0.0974 (1.00)	0.0934 (1.00)
IL32	4 (4%)	107	0.2 (1.00)		0.0522 (1.00)		0.649 (1.00)	0.603 (1.00)	0.129 (1.00)	0.803 (1.00)
CDC27	4 (4%)	107			0.785 (1.00)	0.571 (1.00)	0.84 (1.00)	1 (1.00)	0.334 (1.00)	0.381 (1.00)
NF2	7 (6%)	104			0.0754 (1.00)	0.0298 (1.00)	0.23 (1.00)	0.104 (1.00)	0.789 (1.00)	1 (1.00)
SFRS2IP	5 (5%)	106			0.659 (1.00)	0.507 (1.00)	0.116 (1.00)	0.681 (1.00)	0.389 (1.00)	0.923 (1.00)
PPARGC1B	3 (3%)	108	0.2 (1.00)		0.194 (1.00)				0.189 (1.00)	0.0601 (1.00)
LGI4	4 (4%)	107			0.441 (1.00)	0.688 (1.00)	0.686 (1.00)	1 (1.00)	0.0436 (1.00)	0.24 (1.00)
RPTN	3 (3%)	108	0.2 (1.00)		0.121 (1.00)				0.522 (1.00)	0.0436 (1.00)
BHMT	4 (4%)	107			0.457 (1.00)	0.571 (1.00)	0.769 (1.00)	0.323 (1.00)	0.69 (1.00)	0.803 (1.00)

Methods & Data

Input

Mutation data file = KIRP-TP.mutsig.cluster.txt
Molecular subtypes file = KIRP-TP.transferedmergedcluster.txt
Number of patients = 111
Number of significantly mutated genes = 9
Number of Molecular subtypes = 8
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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