Correlations between copy number and mRNAseq expression
Cervical Squamous Cell Carcinoma and Endocervical 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/C1NC60J6
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 933.7, 1481.4, 2008, 2548, 3175, 3841, 4516, 5207.6, 5939.3, 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 295 304 292
Genes 24776 18187 15568

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
329 BIRC2 11q22.2 0.8742 0 0
10413 YAP1 11q22.1 0.8591 0 0
84259 DCUN1D5 11q22.3 0.8412 0 0
25904 CNOT10 3p22.3 0.8234 0 0
114908 TMEM123 11q22.2 0.8178 0 0
10425 ARIH2 3p21.31 0.8138 0 0
10948 STARD3 17q12 0.812 0 0
28976 ACAD9 3q21.3 0.8111 0 0
387 RHOA 3p21.31 0.8093 0 0
29102 DROSHA 5p13.3 0.8063 0 0
5810 RAD1 5p13.2 0.8025 0 0
7917 BAG6 6p21.33 0.7966 0 0
9538 EI24 11q24.2 0.7952 0 0
23039 XPO7 8p21.3 0.794 0 0
7520 XRCC5 2q35 0.7934 0 0
112936 VPS26B 11q25 0.7931 0 0
8498 RANBP3 19p13.3 0.7924 0 0
6829 SUPT5H 19q13.2 0.7918 0 0
93973 ACTR8 3p21.1 0.7917 0 0
79648 MCPH1 8p23.1 0.784 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 "CESC-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.