Correlation between copy number variations of arm-level result and selected clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
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/C16M35NV
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 43 arm-level events and 3 clinical features across 57 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 43 arm-level events and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

Clinical
Features
AGE GENDER RACE
nCNV (%) nWild-Type Wilcoxon-test Fisher's exact test Fisher's exact test
1p gain 3 (5%) 54 0.217
(1.00)
0.279
(1.00)
1q gain 6 (11%) 51 0.907
(1.00)
1
(1.00)
1
(1.00)
5p gain 3 (5%) 54 0.334
(1.00)
0.545
(1.00)
1
(1.00)
5q gain 3 (5%) 54 1
(1.00)
0.545
(1.00)
1
(1.00)
7p gain 11 (19%) 46 0.856
(1.00)
0.168
(1.00)
0.556
(1.00)
7q gain 9 (16%) 48 0.991
(1.00)
0.0536
(1.00)
0.614
(1.00)
8p gain 5 (9%) 52 0.778
(1.00)
0.647
(1.00)
0.647
(1.00)
8q gain 6 (11%) 51 0.658
(1.00)
1
(1.00)
1
(1.00)
10p gain 5 (9%) 52 0.0515
(1.00)
1
(1.00)
1
(1.00)
10q gain 5 (9%) 52 0.0515
(1.00)
1
(1.00)
1
(1.00)
12p gain 4 (7%) 53 0.888
(1.00)
1
(1.00)
1
(1.00)
12q gain 5 (9%) 52 0.544
(1.00)
0.647
(1.00)
1
(1.00)
13q gain 4 (7%) 53 0.731
(1.00)
0.607
(1.00)
1
(1.00)
18p gain 3 (5%) 54 0.0829
(1.00)
1
(1.00)
1
(1.00)
18q gain 5 (9%) 52 0.767
(1.00)
0.647
(1.00)
1
(1.00)
19p gain 7 (12%) 50 0.609
(1.00)
1
(1.00)
0.718
(1.00)
19q gain 5 (9%) 52 0.429
(1.00)
1
(1.00)
1
(1.00)
20p gain 4 (7%) 53 0.9
(1.00)
0.607
(1.00)
1
(1.00)
20q gain 3 (5%) 54 0.453
(1.00)
0.279
(1.00)
1
(1.00)
1p loss 33 (58%) 24 0.378
(1.00)
0.411
(1.00)
0.836
(1.00)
1q loss 5 (9%) 52 0.438
(1.00)
1
(1.00)
0.645
(1.00)
2p loss 3 (5%) 54 0.391
(1.00)
1
(1.00)
0.461
(1.00)
2q loss 3 (5%) 54 0.734
(1.00)
0.279
(1.00)
0.46
(1.00)
3p loss 28 (49%) 29 0.774
(1.00)
0.783
(1.00)
0.19
(1.00)
3q loss 39 (68%) 18 0.973
(1.00)
0.14
(1.00)
1
(1.00)
4p loss 5 (9%) 52 0.413
(1.00)
0.332
(1.00)
1
(1.00)
4q loss 6 (11%) 51 0.376
(1.00)
0.654
(1.00)
0.705
(1.00)
5p loss 3 (5%) 54 0.217
(1.00)
0.279
(1.00)
1
(1.00)
5q loss 4 (7%) 53 0.118
(1.00)
0.607
(1.00)
0.565
(1.00)
6q loss 7 (12%) 50 0.111
(1.00)
0.0447
(1.00)
1
(1.00)
8p loss 5 (9%) 52 0.374
(1.00)
0.647
(1.00)
0.347
(1.00)
8q loss 3 (5%) 54 0.0928
(1.00)
1
(1.00)
1
(1.00)
9p loss 4 (7%) 53 0.223
(1.00)
0.286
(1.00)
0.562
(1.00)
9q loss 5 (9%) 52 0.652
(1.00)
0.151
(1.00)
0.348
(1.00)
11p loss 16 (28%) 41 0.763
(1.00)
0.216
(1.00)
0.219
(1.00)
11q loss 16 (28%) 41 0.894
(1.00)
0.216
(1.00)
0.693
(1.00)
14q loss 10 (18%) 47 0.564
(1.00)
0.298
(1.00)
1
(1.00)
16p loss 3 (5%) 54 0.858
(1.00)
0.279
(1.00)
1
(1.00)
17p loss 21 (37%) 36 0.551
(1.00)
0.778
(1.00)
0.255
(1.00)
17q loss 9 (16%) 48 0.801
(1.00)
0.705
(1.00)
1
(1.00)
21q loss 15 (26%) 42 0.793
(1.00)
0.537
(1.00)
0.348
(1.00)
22q loss 23 (40%) 34 0.474
(1.00)
0.585
(1.00)
0.458
(1.00)
xq loss 20 (35%) 37 0.88
(1.00)
0.383
(1.00)
0.0283
(1.00)
Methods & Data
Input
  • Copy number data file = transformed.cor.cli.txt

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

  • Number of patients = 57

  • Number of significantly arm-level cnvs = 43

  • Number of selected clinical features = 3

  • 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)