Correlations between copy number and mRNAseq expression
Kidney Chromophobe (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): Correlations between copy number and mRNAseq expression. Broad Institute of MIT and Harvard. doi:10.7908/C15M64G1
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 Rnaseq 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 RNAseq expression of the corresponding feature across patients. High positive/low negative correlation coefficients indicate that genomic alterations result in differences in the expressions of mRNAseq the genomic regions transcribe.

Summary

The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are 866, 2114.4, 2794, 3401, 4016, 4605, 5237.9, 5908.6, 6683, 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 mRNAseq and number of samples in copy number and expression data sets and common to both

Category Copy number Expression Common
Sample 66 66 66
Genes 23778 17843 17758

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

Locus ID Gene Symbol Cytoband cor p-value q-value
23185 LARP4B 10p15.3 0.8536 0 0
84991 RBM17 10p15.1 0.8413 0 0
6945 MLX 17q21.2 0.8394 0 0
83607 AMMECR1L 2q14.3 0.8269 0 0
7327 UBE2G2 21q22.3 0.8201 0 0
22993 HMGXB3 5q32 0.8123 0 0
54619 CCNJ 10q24.1 0.808 2.22044604925031e-16 3.94173582662916e-12
55571 C2orf29 2q11.2 0.8057 4.44089209850063e-16 7.88302756404846e-12
51021 MRPS16 10q22.2 0.7938 1.77635683940025e-15 3.15303338993544e-11
56888 KCMF1 2p11.2 0.7929 2.22044604925031e-15 3.94106969281438e-11
84947 SERAC1 6q25.3 0.7876 4.44089209850063e-15 7.88169529641891e-11
57466 SCAF4 21q22.11 0.7839 7.105427357601e-15 1.26100019315345e-10
8575 PRKRA 2q31.2 0.7719 3.28626015289046e-14 5.83179726731942e-10
670 BPHL 6p25.2 0.7706 3.81916720471054e-14 6.77711220475885e-10
5211 PFKL 21q22.3 0.7652 7.41628980449605e-14 1.31594646290978e-09
22944 KIN 10p14 0.7643 8.26005930321116e-14 1.46558232216876e-09
10206 TRIM13 13q14.2 0.7634 9.14823772291129e-14 1.62308033679892e-09
81037 CLPTM1L 5p15.33 0.7631 9.45910016980633e-14 1.67813896112534e-09
57700 FAM160B1 10q25.3 0.7624 1.0325074129014e-13 1.83166815048708e-09
11193 WBP4 13q14.11 0.7621 1.07025499573865e-13 1.89852533694079e-09
Methods & Data
Input

Gene level (TCGA Level III) mRNAseq expression data and copy number data of corresponding gene derived by GISTIC pipelinePearson 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

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.