Correlations between copy number and mRNAseq expression
Stomach Adenocarcinoma (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/C18G8K5P
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 970, 1546, 2064.7, 2605, 3191.5, 3816, 4448.3, 5075, 5779, 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 441 415 413
Genes 24776 18694 15910

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
339287 MSL1 17q21.1 0.8768 0 0
84299 MIEN1 17q12 0.8603 0 0
8725 URI1 19q12 0.8566 0 0
9862 MED24 17q21.1 0.8511 0 0
6605 SMARCE1 17q21.2 0.8391 0 0
2064 ERBB2 17q12 0.8285 0 0
22794 CASC3 17q21.1 0.828 0 0
93210 PGAP3 17q12 0.827 0 0
5709 PSMD3 17q21.1 0.8266 0 0
147179 WIPF2 17q21.1 0.8165 0 0
84060 RBM48 7q21.2 0.8101 0 0
3845 KRAS 12p12.1 0.8083 0 0
6780 STAU1 20q13.13 0.807 0 0
889 KRIT1 7q21.2 0.807 0 0
29883 CNOT7 8p22 0.8019 0 0
51507 RTFDC1 20q13.31 0.8013 0 0
6874 TAF4 20q13.33 0.7984 0 0
252969 NEIL2 8p23.1 0.7969 0 0
55661 DDX27 20q13.13 0.796 0 0
55915 LANCL2 7p11.2 0.7929 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 "STAD-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.