Lung Squamous Cell Carcinoma: Correlations between copy number and mRNA expression
Maintained by John Zhang (Dana-Farber Cancer Institute)
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 expression 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 intensity of the expressions of the corresponding feature across patients. High positive/low negative correlation coefficients indicate that genomic alterations result in differences in the expressions of mRNA the genomic regions transcribe.

Summary

The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.0231, 0.0444, 0.10392, 0.17168, 0.2449, 0.3225, 0.40244, 0.4808, 0.56878, 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 mRNA and number of samples in copy number and expression data sets and common to both

Category | Copy number | Expression | Common
--- | --- | --- | ---
Sample | 282 | 154 | 154
Genes | 29390 | 17815 | 15551

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

feature | r | p-value | q-value | chrom | start | end | geneid
--- | --- | --- | --- | --- | --- | --- | ---
C9orf82 | 0.886 | 0 | 0 | 9 | 26830683 | 26882725 | 79886
WHSC1L1 | 0.8859 | 0 | 0 | 8 | 38251717 | 38358947 | 54904
ORAOV1 | 0.8801 | 0 | 0 | 11 | 69189512 | 69199346 | 220064
PLAA | 0.8776 | 0 | 0 | 9 | 26894518 | 26937138 | 9373
PPFIA1 | 0.8728 | 0 | 0 | 11 | 69794471 | 69908150 | 8500
TOPORS | 0.8612 | 0 | 0 | 9 | 32530542 | 32542601 | 10210
PSMA1 | 0.8486 | 0 | 0 | 11 | 14482998 | 14621739 | 5682
TMEM11 | 0.8449 | 0 | 0 | 17 | 21041855 | 21058297 | 8834
ZNF639 | 0.8446 | 0 | 0 | 3 | 180524245 | 180536014 | 51193
BRF2 | 0.8429 | 0 | 0 | 8 | 37820558 | 37826569 | 55290
TM2D2 | 0.8368 | 0 | 0 | 8 | 38965484 | 38973198 | 83877
FADD | 0.8346 | 0 | 0 | 11 | 69726917 | 69731144 | 8772
TBL1XR1 | 0.8242 | 0 | 0 | 3 | 178221867 | 178397734 | 79718
ATAD1 | 0.823 | 0 | 0 | 10 | 89502855 | 89567897 | 84896
G3BP2 | 0.8222 | 0 | 0 | 4 | 76786990 | 76817629 | 9908
MAEA | 0.8218 | 0 | 0 | 4 | 1273672 | 1323925 | 10296
SCFD1 | 0.8209 | 0 | 0 | 14 | 30161272 | 30274769 | 23256
ASH2L | 0.8202 | 0 | 0 | 8 | 38082223 | 38116216 | 9070
CLTA | 0.8194 | 0 | 0 | 9 | 36180972 | 36202055 | 1211
NDUFS5 | 0.8147 | 0 | 0 | 1 | 39264593 | 39272874 | 4725

Methods & Data
Input

Gene level (TCGA Level III) expression data and copy number data of the corresponding loci derived by using the CNTools package of Bioconductor were used for the calculations. Pearson 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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

Meta

- Maintainer = John Zhang

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