Correlation between copy number variation genes (focal events) and selected clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
15 July 2014 | analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C12Z149D
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/C12Z149D
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 28 focal events and 3 clinical features across 57 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 28 focal 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
amp 1q21 3 | 7 (12%) | 50 | 0.568 (1.00) | 1 (1.00) | 0.704 (1.00)
amp 4q25 | 3 (5%) | 54 | 0.775 (1.00) | 1 (1.00) | 0.459 (1.00)
amp 4q31 1 | 4 (7%) | 53 | 0.925 (1.00) | 0.607 (1.00) | 1 (1.00)
amp 11p15 2 | 5 (9%) | 52 | 0.117 (1.00) | 0.647 (1.00) | 0.348 (1.00)
amp 12q13 3 | 8 (14%) | 49 | 0.434 (1.00) | 0.699 (1.00) | 0.42 (1.00)
amp 14q24 3 | 6 (11%) | 51 | 0.886 (1.00) | 1 (1.00) | 0.0624 (1.00)
amp 17q21 31 | 4 (7%) | 53 | 0.31 (1.00) | 0.119 (1.00) | 1 (1.00)
del 1p12 | 40 (70%) | 17 | 0.944 (1.00) | 0.078 (1.00) | 1 (1.00)
del 1q44 | 5 (9%) | 52 | 0.866 (1.00) | 1 (1.00) | 1 (1.00)
del 2q34 | 3 (5%) | 54 | 0.734 (1.00) | 0.279 (1.00) | 0.46 (1.00)
del 3p24 1 | 25 (44%) | 32 | 0.263 (1.00) | 0.782 (1.00) | 0.607 (1.00)
del 3q26 1 | 41 (72%) | 16 | 0.965 (1.00) | 0.0622 (1.00) | 0.612 (1.00)
del 4q22 1 | 8 (14%) | 49 | 0.414 (1.00) | 1 (1.00) | 1 (1.00)
del 4q35 2 | 7 (12%) | 50 | 0.789 (1.00) | 0.687 (1.00) | 1 (1.00)
del 5q15 | 6 (11%) | 51 | 0.55 (1.00) | 0.654 (1.00) | 1 (1.00)
del 6p12 3 | 5 (9%) | 52 | 0.374 (1.00) | 0.151 (1.00) | 0.563 (1.00)
del 6q16 1 | 9 (16%) | 48 | 0.278 (1.00) | 0.0201 (1.00) | 0.564 (1.00)
del 8p22 | 8 (14%) | 49 | 0.8 (1.00) | 0.699 (1.00) | 0.423 (1.00)
del 9p24 2 | 4 (7%) | 53 | 0.223 (1.00) | 0.286 (1.00) | 0.563 (1.00)
del 9q21 12 | 6 (11%) | 51 | 0.948 (1.00) | 0.0809 (1.00) | 0.423 (1.00)
del 11p15 4 | 19 (33%) | 38 | 0.267 (1.00) | 0.24 (1.00) | 0.639 (1.00)
del 11q22 1 | 16 (28%) | 41 | 0.894 (1.00) | 0.216 (1.00) | 0.693 (1.00)
del 12q21 33 | 4 (7%) | 53 | 0.381 (1.00) | 1 (1.00) | 1 (1.00)
del 16q21 | 3 (5%) | 54 | 0.592 (1.00) | 1 (1.00) | 0.457 (1.00)
del 17p13 2 | 23 (40%) | 34 | 0.672 (1.00) | 1 (1.00) | 0.439 (1.00)
del 17q11 2 | 17 (30%) | 40 | 0.747 (1.00) | 0.763 (1.00) | 1 (1.00)
del 22q13 31 | 26 (46%) | 31 | 0.706 (1.00) | 1 (1.00) | 0.77 (1.00)
del xp21 1 | 20 (35%) | 37 | 1 (1.00) | 0.383 (1.00) | 0.027 (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 focal cnvs = 28

- Number of selected clinical features = 3

- 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

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] 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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