Correlation between copy number variation genes (focal events) and molecular subtypes
Kidney Chromophobe (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
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 (2016): Correlation between copy number variation genes (focal events) and molecular subtypes. Broad Institute of MIT and Harvard. doi:10.7908/C1R78DMC
Overview
Introduction

This pipeline computes the correlation between significant copy number variation (cnv focal) genes and molecular subtypes.

Summary

Testing the association between copy number variation 2 focal events and 10 molecular subtypes across 66 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

  • No focal cnvs related to molecuar subtypes.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 2 focal events and 10 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.

Clinical
Features
CN
CNMF
METHLYATION
CNMF
RPPA
CNMF
RPPA
CHIERARCHICAL
MRNASEQ
CNMF
MRNASEQ
CHIERARCHICAL
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRSEQ
MATURE
CNMF
MIRSEQ
MATURE
CHIERARCHICAL
nCNV (%) nWild-Type Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
amp 8q23 3 19 (29%) 47 0.232
(0.421)
0.292
(0.421)
0.189
(0.421)
0.0442
(0.295)
0.0322
(0.295)
0.0247
(0.295)
0.642
(0.677)
0.228
(0.421)
0.1
(0.402)
0.1
(0.402)
amp 15q22 31 23 (35%) 43 0.28
(0.421)
0.643
(0.677)
0.204
(0.421)
0.372
(0.465)
0.293
(0.421)
0.18
(0.421)
0.905
(0.905)
0.295
(0.421)
0.446
(0.524)
0.318
(0.424)
Methods & Data
Input
  • Copy number data file = all_lesions.txt from GISTIC pipeline

  • Processed Copy number data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_Correlate_Genomic_Events_Preprocess/KICH-TP/22507963/transformed.cor.cli.txt

  • Molecular subtype file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KICH-TP/22539844/KICH-TP.transferedmergedcluster.txt

  • Number of patients = 66

  • Number of significantly focal cnvs = 2

  • Number of molecular subtypes = 10

  • Exclude genes that fewer than K tumors have alterations, 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)