Correlations between copy number and mRNA expression
Pan-kidney cohort (KICH+KIRC+KIRP) (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by John Zhang (MD Anderson Cancer Center)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlations between copy number and mRNA expression. Broad Institute of MIT and Harvard. doi:10.7908/C1ZK5G26
Overview
Introduction

A TCGA sample is profiled to detect the copy number variations and expressions of genes. This pipeline attempts to correlate copy number and expression data of genes across samples to determine if the copy number variations also result in differential expressions. This report contains the calculated correlation coefficients based on measurements of genomic copy number (log2) values and intensity of the expressions of the corresponding feature across patients. High positive/low negative correlation coefficients indicate that genomic alterations result in differences in the expressions of mRNA the genomic regions transcribe.

Summary

The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.1893, -0.08072, 4e-04, 0.0688, 0.1315, 0.1981, 0.2704, 0.3552, 0.46656, respectively.

Results
Correlation results

Number of genes and samples used for the calculation are shown in Table 1. Figure 1 shows the distribution of calculated correlation coefficients and quantile-quantile plot of the calculated correlation coefficients against a normal distribution. Table 2 shows the top 20 features ordered by the value of correlation coefficients.

Table 1.  Counts of mRNA and number of samples in copy number and expression data sets and common to both

Category Copy number Expression Common
Sample 882 88 36
Genes 24771 17814 15175

Figure 1.  Summary figures. Left: histogram showing the distribution of the calculated correlations across samples for all Genes. Right: QQ plot of the calculated correlations across samples. The QQ plot is used to plot the quantiles of the calculated correlation coefficients against that derived from a normal distribution. Points deviating from the blue line indicate deviation from normality.

Table 2.  Get Full Table Top 20 features (defined by the feature column) ranked by correlation coefficients

feature r p-value q-value chrom start end geneid
MED23 0.841 1.36458400135098e-10 1.08508839588311e-06 9439 6q23.2 -0.032 0.000
MAP3K7 0.8327 3.01039859706975e-10 1.08508839588311e-06 6885 6q15 -0.032 0.000
APPBP2 0.8306 3.65955266090623e-10 1.08508839588311e-06 10513 17q23.2 0.165 0.670
RUNDC1 0.8271 5.05020247842936e-10 1.12307089430846e-06 146923 17q21.31 0.165 0.670
MAP2K4 0.8179 1.12463327539558e-09 1.64995516901629e-06 6416 17p12 0.165 0.670
TEKT5 0.8171 1.20457199770385e-09 1.64995516901629e-06 146279 16p13.13 0.221 0.680
SERBP1 0.8149 1.45792111716503e-09 1.64995516901629e-06 26135 1p31.3 -0.002 -0.008
ELAC2 0.8147 1.48389700527218e-09 1.64995516901629e-06 60528 17p12 0.165 0.670
ZNF451 0.812 1.84730319929827e-09 1.8258035439227e-06 26036 6p12.1 -0.032 0.000
FBXL20 0.8071 2.74907563380111e-09 2.44537271031147e-06 84961 17q12 0.165 0.670
EFTUD2 0.8037 3.59023455409613e-09 2.54634395180797e-06 9343 17q21.31 0.165 0.670
ZKSCAN5 0.8033 3.72235686718625e-09 2.54634395180797e-06 23660 7q22.1 0.386 0.740
USP32 0.803 3.79120290716628e-09 2.54634395180797e-06 -5621 17q23.1 0.165 0.670
EPN2 0.8023 4.00762178998093e-09 2.54634395180797e-06 22905 17p11.2 0.165 0.670
CDK5RAP3 0.7987 5.28445376346554e-09 3.13377070294561e-06 80279 17q21.32 0.165 0.670
PUM2 0.7936 7.7846180612795e-09 4.32788487455359e-06 23369 2p24.1 0.011 -0.010
FASTK 0.7922 8.59833892974393e-09 4.49908306595405e-06 10922 7q36.1 0.386 0.740
LEPRE1 0.791 9.40650024539025e-09 4.64851181183123e-06 64175 1p34.2 -0.002 -0.008
PRDX1 0.7891 1.08188826786204e-08 4.94964517981967e-06 5052 1p34.1 -0.002 -0.008
CASP8AP2 0.7887 1.11287321580278e-08 4.94964517981967e-06 -2260 6q15 -0.032 0.000
Methods & Data
Input

Gene level (TCGA Level III) expression data and copy number data of the corresponding loci derived by using the CNTools package of Bioconductor were used for the calculations. Pearson correlation coefficients were calculated for each pair of genes shared by the two data sets across all the samples that were common. The input file "*.medianexp.txt" is generated in the pipeline mRNA_Preprocess_Median in the stddata run.

Correlation across sample

Pairwise correlations between the log2 copy numbers and expressions of each gene across samples were calculated using Pearson correlation.

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.