Liver Hepatocellular Carcinoma: Correlation between copy number variations of arm-level result and selected clinical features

Overview

Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 59 arm-level results and 2 clinical features across 48 patients, no significant finding detected with Q value < 0.25.

No arm-level 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 59 arm-level results and 2 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


		Clinical Features	VITALSTATUS	GENDER
	nCNV (%)	nWild-Type	Fisher's exact test	Fisher's exact test
1p gain	5 (10%)	43	0.348 (1.00)	1 (1.00)
1q gain	26 (54%)	22	0.772 (1.00)	0.557 (1.00)
2p gain	10 (21%)	38	1 (1.00)	1 (1.00)
2q gain	6 (12%)	42	1 (1.00)	0.197 (1.00)
3q gain	3 (6%)	45	1 (1.00)	1 (1.00)
4p gain	4 (8%)	44	0.609 (1.00)	0.286 (1.00)
5p gain	14 (29%)	34	0.111 (1.00)	1 (1.00)
5q gain	14 (29%)	34	0.111 (1.00)	1 (1.00)
6p gain	13 (27%)	35	1 (1.00)	1 (1.00)
6q gain	5 (10%)	43	1 (1.00)	0.635 (1.00)
7p gain	13 (27%)	35	1 (1.00)	0.0188 (1.00)
7q gain	13 (27%)	35	0.517 (1.00)	0.0959 (1.00)
8p gain	8 (17%)	40	0.245 (1.00)	0.451 (1.00)
8q gain	23 (48%)	25	0.248 (1.00)	0.566 (1.00)
9p gain	3 (6%)	45	0.234 (1.00)	0.554 (1.00)
9q gain	3 (6%)	45	0.234 (1.00)	0.554 (1.00)
10p gain	6 (12%)	42	0.666 (1.00)	0.669 (1.00)
12p gain	3 (6%)	45	0.234 (1.00)	0.554 (1.00)
12q gain	3 (6%)	45	1 (1.00)	1 (1.00)
15q gain	5 (10%)	43	0.348 (1.00)	0.372 (1.00)
17q gain	18 (38%)	30	0.766 (1.00)	1 (1.00)
19p gain	4 (8%)	44	0.609 (1.00)	0.286 (1.00)
19q gain	5 (10%)	43	1 (1.00)	0.0724 (1.00)
20p gain	8 (17%)	40	1 (1.00)	0.695 (1.00)
20q gain	10 (21%)	38	1 (1.00)	1 (1.00)
21q gain	3 (6%)	45	0.234 (1.00)	0.056 (1.00)
22q gain	6 (12%)	42	0.188 (1.00)	0.197 (1.00)
1p loss	8 (17%)	40	1 (1.00)	0.695 (1.00)
3p loss	7 (15%)	41	1 (1.00)	0.0967 (1.00)
3q loss	3 (6%)	45	1 (1.00)	0.554 (1.00)
4p loss	8 (17%)	40	0.701 (1.00)	0.451 (1.00)
4q loss	14 (29%)	34	1 (1.00)	0.354 (1.00)
5q loss	4 (8%)	44	0.609 (1.00)	1 (1.00)
6q loss	8 (17%)	40	0.245 (1.00)	0.236 (1.00)
7p loss	3 (6%)	45	0.234 (1.00)	0.554 (1.00)
7q loss	5 (10%)	43	1 (1.00)	0.372 (1.00)
8p loss	24 (50%)	24	1 (1.00)	1 (1.00)
8q loss	5 (10%)	43	0.348 (1.00)	1 (1.00)
9p loss	13 (27%)	35	1 (1.00)	0.522 (1.00)
9q loss	11 (23%)	37	1 (1.00)	0.488 (1.00)
10p loss	4 (8%)	44	1 (1.00)	1 (1.00)
10q loss	13 (27%)	35	1 (1.00)	0.321 (1.00)
11p loss	5 (10%)	43	1 (1.00)	0.372 (1.00)
11q loss	9 (19%)	39	0.461 (1.00)	1 (1.00)
12p loss	5 (10%)	43	0.348 (1.00)	1 (1.00)
12q loss	3 (6%)	45	0.234 (1.00)	0.554 (1.00)
13q loss	19 (40%)	29	0.556 (1.00)	1 (1.00)
14q loss	16 (33%)	32	0.359 (1.00)	1 (1.00)
15q loss	8 (17%)	40	0.701 (1.00)	0.695 (1.00)
16p loss	10 (21%)	38	0.724 (1.00)	0.719 (1.00)
16q loss	18 (38%)	30	1 (1.00)	0.762 (1.00)
17p loss	23 (48%)	25	1 (1.00)	0.25 (1.00)
17q loss	4 (8%)	44	0.609 (1.00)	1 (1.00)
18p loss	5 (10%)	43	0.348 (1.00)	1 (1.00)
18q loss	7 (15%)	41	1 (1.00)	1 (1.00)
19p loss	6 (12%)	42	1 (1.00)	0.381 (1.00)
19q loss	5 (10%)	43	1 (1.00)	0.635 (1.00)
21q loss	8 (17%)	40	0.701 (1.00)	0.0154 (1.00)
22q loss	9 (19%)	39	0.137 (1.00)	0.127 (1.00)

Methods & Data

Input

Mutation data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = LIHC.clin.merged.picked.txt
Number of patients = 48
Number of significantly arm-level cnvs = 59
Number of selected clinical features = 2
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

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] 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)