Correlation between copy number variation genes (focal events) and selected clinical features
Kidney Chromophobe (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
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/C1RX99ZW
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 2 focal events and 11 clinical features across 66 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 2 focal events and 11 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

Clinical
Features
Time
to
Death
AGE NEOPLASM
DISEASESTAGE
PATHOLOGY
T
STAGE
PATHOLOGY
N
STAGE
PATHOLOGY
M
STAGE
GENDER KARNOFSKY
PERFORMANCE
SCORE
NUMBERPACKYEARSSMOKED RACE ETHNICITY
nCNV (%) nWild-Type logrank test Wilcoxon-test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Wilcoxon-test Wilcoxon-test Fisher's exact test Fisher's exact test
amp 8q11 23 19 (29%) 47 100
(1.00)
0.335
(1.00)
0.117
(1.00)
0.838
(1.00)
0.33
(1.00)
0.137
(1.00)
0.412
(1.00)
1
(1.00)
0.234
(1.00)
0.791
(1.00)
0.098
(1.00)
amp 15q22 31 23 (35%) 43 100
(1.00)
0.332
(1.00)
0.127
(1.00)
0.395
(1.00)
0.141
(1.00)
0.112
(1.00)
0.601
(1.00)
1
(1.00)
0.784
(1.00)
1
(1.00)
0.277
(1.00)
Methods & Data
Input
  • Copy number data file = transformed.cor.cli.txt

  • Clinical data file = KICH-TP.merged_data.txt

  • Number of patients = 66

  • Number of significantly focal cnvs = 2

  • Number of selected clinical features = 11

  • Exclude genes that fewer than K tumors have mutations, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' 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] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[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)