Correlations between copy number and mRNA expression

Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

Maintained by John Zhang (MD Anderson Cancer Center)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Kidney Renal Papillary Cell Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlations between copy number and mRNA expression. Broad Institute of MIT and Harvard. doi:10.7908/C10P0WZ8

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 -0.52769, -0.30528, -0.1208, 0.0345, 0.1843, 0.3315, 0.48097, 0.62018, 0.76409, 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	127	16	7
Genes	24174	17815	15702

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
IFNW1	0.9889	0.00140270189034508	0.142232198154113	3467	9p21.3	-0.426	-1.216
SLC27A4	0.9838	6.35161122035655e-05	0.102549737810707	10999	9q34.11	-0.428	-0.590
ZNF282	0.9836	6.58544907676806e-05	0.102549737810707	8427	7q36.1	0.013	0.022
ZNF124	0.9827	7.53521964051984e-05	0.102549737810707	7678	1q44	-0.422	0.428
ARVCF	0.9816	8.6916881397947e-05	0.102549737810707	421	22q11.21	-0.437	-0.029
CCDC23	0.9804	0.000101598768285971	0.102549737810707	374969	1p34.2	-0.421	-0.001
ZNF701	0.9793	0.000116905007926604	0.102549737810707	55762	19q13.41	-0.411	-0.059
SEMA4G	0.9791	0.000120157485863359	0.102549737810707	57715	10q24.31	0.006	0.009
ATR	0.979	0.000120775757059377	0.102549737810707	545	3q23	0.015	0.121
HTATSF1	0.9784	0.000130320038393972	0.102549737810707	27336	Xq26.3	-0.124	-0.163
C14orf43	0.9777	0.000141523648667041	0.102549737810707	91748	14q24.3	-0.423	-0.438
OPA1	0.9773	0.000147212228341465	0.102549737810707	4976	3q29	0.015	0.121
EIF4A3	0.9772	0.000149040371129949	0.102549737810707	9775	17q25.3	0.402	-0.007
MYO1C	0.9766	0.000158492664076082	0.102549737810707	4641	17p13.3	0.394	-0.095
C10orf58	0.9758	0.000172921910555957	0.102549737810707	84293	10q23.1	0.006	0.009
COX4NB	0.9753	0.00018223089195879	0.102549737810707	10328	16q24.1	0.379	-0.572
ACTN3	0.9749	0.00093435539012754	0.124747960280115	89	11q13.2	0.004	-0.017
RBP2	0.9749	0.000188639234890431	0.102549737810707	5948	3q23	0.015	0.121
NAGA	0.9742	0.000202860528162985	0.102549737810707	4668	22q13.2	-0.437	-0.029
ZNF706	0.9725	0.000237350055114716	0.102549737810707	51123	8q22.3	0.007	0.023

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.

Correlation across sample

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

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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