Correlation between copy number variations of arm-level result and selected clinical features

Testicular Germ Cell Tumors (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 variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1G73CN1

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 55 arm-level events and 5 clinical features across 31 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 55 arm-level events and 5 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.


		Clinical Features	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	ETHNICITY
	nCNV (%)	nWild-Type	Wilcoxon-test	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
1p gain	10 (32%)	21	0.916 (1.00)	0.513 (1.00)	0.252 (1.00)	0.538 (1.00)	1 (1.00)
1q gain	11 (35%)	20	0.82 (1.00)	0.561 (1.00)	0.458 (1.00)	0.584 (1.00)	1 (1.00)
2p gain	11 (35%)	20	0.341 (1.00)	0.308 (1.00)	0.273 (1.00)	1 (1.00)	0.535 (1.00)
2q gain	12 (39%)	19	0.0487 (1.00)	0.396 (1.00)	0.149 (1.00)	1 (1.00)	0.265 (1.00)
3p gain	6 (19%)	25	0.96 (1.00)	0.426 (1.00)	0.654 (1.00)	1 (1.00)	0.488 (1.00)
3q gain	9 (29%)	22	0.81 (1.00)	0.0805 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
6p gain	6 (19%)	25	0.153 (1.00)	0.0549 (1.00)	0.654 (1.00)	0.121 (1.00)	1 (1.00)
6q gain	6 (19%)	25	0.27 (1.00)	0.0558 (1.00)	0.654 (1.00)	0.121 (1.00)	1 (1.00)
7p gain	28 (90%)	3	0.16 (1.00)	1 (1.00)	0.6 (1.00)	1 (1.00)	0.0189 (1.00)
7q gain	27 (87%)	4	0.301 (1.00)	0.668 (1.00)	1 (1.00)	0.426 (1.00)	0.0369 (1.00)
8p gain	25 (81%)	6	0.88 (1.00)	0.184 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
8q gain	24 (77%)	7	0.266 (1.00)	0.361 (1.00)	0.22 (1.00)	1 (1.00)	0.55 (1.00)
9q gain	3 (10%)	28	0.284 (1.00)	0.138 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
12q gain	24 (77%)	7	0.0614 (1.00)	0.96 (1.00)	1 (1.00)	0.541 (1.00)	0.55 (1.00)
14q gain	11 (35%)	20	0.0859 (1.00)	0.511 (1.00)	1 (1.00)	0.519 (1.00)	1 (1.00)
15q gain	8 (26%)	23	0.0266 (1.00)	0.93 (1.00)	0.433 (1.00)	1 (1.00)	0.55 (1.00)
16p gain	4 (13%)	27	0.262 (1.00)	0.0874 (1.00)	0.101 (1.00)	0.121 (1.00)	1 (1.00)
16q gain	5 (16%)	26	0.706 (1.00)	0.167 (1.00)	0.333 (1.00)	0.219 (1.00)	1 (1.00)
17p gain	9 (29%)	22	0.827 (1.00)	0.374 (1.00)	0.113 (1.00)	0.25 (1.00)	0.537 (1.00)
17q gain	12 (39%)	19	0.807 (1.00)	0.243 (1.00)	0.273 (1.00)	0.603 (1.00)	0.265 (1.00)
19p gain	5 (16%)	26	0.0179 (1.00)	0.568 (1.00)	0.654 (1.00)	1 (1.00)	1 (1.00)
20p gain	9 (29%)	22	0.183 (1.00)	0.516 (1.00)	0.433 (1.00)	1 (1.00)	0.537 (1.00)
20q gain	10 (32%)	21	0.15 (1.00)	0.346 (1.00)	0.252 (1.00)	1 (1.00)	0.533 (1.00)
21q gain	27 (87%)	4	0.443 (1.00)	0.77 (1.00)	0.6 (1.00)	1 (1.00)	0.349 (1.00)
22q gain	6 (19%)	25	0.0752 (1.00)	0.493 (1.00)	0.0829 (1.00)	1 (1.00)	1 (1.00)
xq gain	6 (19%)	25	0.0141 (1.00)	0.939 (1.00)	1 (1.00)	0.519 (1.00)	1 (1.00)
3p loss	12 (39%)	19	0.684 (1.00)	0.334 (1.00)	0.716 (1.00)	1 (1.00)	1 (1.00)
3q loss	9 (29%)	22	0.93 (1.00)	0.774 (1.00)	0.704 (1.00)	0.603 (1.00)	0.537 (1.00)
4p loss	23 (74%)	8	0.175 (1.00)	0.482 (1.00)	0.433 (1.00)	0.519 (1.00)	0.55 (1.00)
4q loss	25 (81%)	6	0.12 (1.00)	0.492 (1.00)	0.394 (1.00)	1 (1.00)	1 (1.00)
5p loss	21 (68%)	10	0.138 (1.00)	0.842 (1.00)	0.0538 (1.00)	1 (1.00)	1 (1.00)
5q loss	19 (61%)	12	0.529 (1.00)	0.146 (1.00)	0.00915 (1.00)	0.576 (1.00)	1 (1.00)
6p loss	5 (16%)	26	0.726 (1.00)	0.975 (1.00)	0.654 (1.00)	0.519 (1.00)	0.422 (1.00)
6q loss	5 (16%)	26	0.726 (1.00)	0.975 (1.00)	0.654 (1.00)	0.519 (1.00)	0.422 (1.00)
9p loss	18 (58%)	13	0.779 (1.00)	0.625 (1.00)	0.722 (1.00)	0.519 (1.00)	0.558 (1.00)
9q loss	16 (52%)	15	0.797 (1.00)	0.714 (1.00)	0.724 (1.00)	0.237 (1.00)	0.101 (1.00)
10p loss	20 (65%)	11	0.057 (1.00)	0.439 (1.00)	1 (1.00)	0.261 (1.00)	0.281 (1.00)
10q loss	20 (65%)	11	0.238 (1.00)	0.704 (1.00)	0.458 (1.00)	0.261 (1.00)	0.0367 (1.00)
11p loss	20 (65%)	11	0.291 (1.00)	0.704 (1.00)	0.716 (1.00)	0.603 (1.00)	1 (1.00)
11q loss	24 (77%)	7	0.193 (1.00)	0.666 (1.00)	0.685 (1.00)	0.519 (1.00)	0.55 (1.00)
13q loss	24 (77%)	7	0.287 (1.00)	1 (1.00)	0.685 (1.00)	0.519 (1.00)	1 (1.00)
14q loss	5 (16%)	26	0.154 (1.00)	0.201 (1.00)	1 (1.00)	0.219 (1.00)	1 (1.00)
15q loss	8 (26%)	23	0.667 (1.00)	0.154 (1.00)	0.685 (1.00)	0.584 (1.00)	1 (1.00)
16p loss	10 (32%)	21	0.112 (1.00)	0.983 (1.00)	0.704 (1.00)	0.261 (1.00)	0.0267 (1.00)
16q loss	10 (32%)	21	0.112 (1.00)	0.981 (1.00)	0.704 (1.00)	0.261 (1.00)	0.0267 (1.00)
17p loss	10 (32%)	21	0.29 (1.00)	1 (1.00)	0.458 (1.00)	1 (1.00)	1 (1.00)
17q loss	5 (16%)	26	0.484 (1.00)	0.402 (1.00)	1 (1.00)	0.426 (1.00)	1 (1.00)
18p loss	25 (81%)	6	0.176 (1.00)	0.106 (1.00)	1 (1.00)	1 (1.00)	1 (1.00)
18q loss	26 (84%)	5	0.132 (1.00)	0.133 (1.00)	1 (1.00)	0.426 (1.00)	1 (1.00)
19p loss	14 (45%)	17	0.21 (1.00)	0.0206 (1.00)	0.285 (1.00)	0.576 (1.00)	0.576 (1.00)
19q loss	17 (55%)	14	0.233 (1.00)	0.0025 (0.685)	0.479 (1.00)	0.603 (1.00)	1 (1.00)
20p loss	8 (26%)	23	0.365 (1.00)	0.155 (1.00)	0.685 (1.00)	0.219 (1.00)	1 (1.00)
20q loss	3 (10%)	28	0.0411 (1.00)	0.85 (1.00)	0.226 (1.00)		0.271 (1.00)
22q loss	15 (48%)	16	0.373 (1.00)	0.71 (1.00)	0.479 (1.00)	1 (1.00)	0.6 (1.00)
xq loss	7 (23%)	24	0.162 (1.00)	0.816 (1.00)	0.0373 (1.00)	0.541 (1.00)	0.55 (1.00)

Methods & Data

Input

Copy number data file = transformed.cor.cli.txt
Clinical data file = TGCT-TP.merged_data.txt
Number of patients = 31
Number of significantly arm-level cnvs = 55
Number of selected clinical features = 5
Exclude regions 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

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] 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)

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