Correlation between copy number variation genes (focal events) and selected clinical features
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.

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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