Correlations between copy number and mRNAseq expression
Sarcoma (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): Correlations between copy number and mRNAseq expression. Broad Institute of MIT and Harvard. doi:10.7908/C10G3JMQ
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 991.5, 1595, 2152.5, 2702, 3292, 3936, 4604, 5253, 5977, 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 257 259 255
Genes 24776 18187 15576

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
1019 CDK4 12q14.1 0.9434 0 0
4193 MDM2 12q15 0.9405 0 0
25895 METTL21B 12q14.1 0.9245 0 0
6302 TSPAN31 12q14.1 0.9224 0 0
10576 CCT2 12q15 0.9058 0 0
10102 TSFM 12q14.1 0.9027 0 0
10106 CTDSP2 12q14.1 0.8925 0 0
4234 METTL1 12q14.1 0.8913 0 0
8089 YEATS4 12q15 0.869 0 0
60528 ELAC2 17p12 0.8676 0 0
10956 OS9 12q13.3 0.8618 0 0
10818 FRS2 12q15 0.8589 0 0
10294 DNAJA2 16q11.2 0.8564 0 0
2782 GNB1 1p36.33 0.854 0 0
6341 SCO1 17p13.1 0.8533 0 0
92979 MARCH9 12q14.1 0.8505 0 0
10540 DCTN2 12q13.3 0.8396 0 0
25978 CHMP2B 3p11.2 0.8388 0 0
91419 XRCC6BP1 12q14.1 0.837 0 0
8451 CUL4A 13q34 0.8342 0 0
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. The input file "SARC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt " is generated in the pipeline mRNAseq_Preprocess 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.