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
doi:10.7908/C1BV7DJK
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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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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