Correlations between copy number and mRNAseq expression
Glioma (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/C1WD3ZZJ
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 806.5, 1587, 2060, 2496, 2990, 3525, 4140, 4799, 5672, 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 1090 669 660
Genes 24776 18325 15616

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
23480 SEC61G 7p11.2 0.9007 0 0
10102 TSFM 12q14.1 0.8778 0 0
4193 MDM2 12q15 0.8733 0 0
84893 FBXO18 10p15.1 0.8656 0 0
92979 MARCH9 12q14.1 0.8634 0 0
6302 TSPAN31 12q14.1 0.8597 0 0
23185 LARP4B 10p15.3 0.8574 0 0
4898 NRD1 1p32.3 0.8569 0 0
60313 GPBP1L1 1p34.1 0.8546 0 0
908 CCT6A 7p11.2 0.8522 0 0
26128 KIAA1279 10q22.1 0.8492 0 0
219771 CCNY 10p11.21 0.8475 0 0
54788 DNAJB12 10q22.1 0.8466 0 0
51231 VRK3 19q13.33 0.8446 0 0
10489 LRRC41 1p34.1 0.8421 0 0
51322 WAC 10p12.1 0.8398 0 0
54455 FBXO42 1p36.13 0.8392 0 0
7268 TTC4 1p32.3 0.8384 0 0
23560 GTPBP4 10p15.3 0.8357 0 0
2631 GBAS 7p11.2 0.8356 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 "GBMLGG-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.