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
doi:10.7908/C1RX99ZW
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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
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