Correlations between APOBEC_MutLoad_MinEstimate and mRNAseq expression

Overview

Introduction

This pipeline attempts to calculate the pearson correlation between APOBEC_MutLoad_MinEstimate and mRnaseq data of each gene across samples to determine if the APOBEC_MutLoad_MinEstimate also result in differential expressions.

Summary

The correlation coefficients in 10, 20, 30, 40, 50, 60, 70, 80, 90 percentiles are -0.0564, -0.0395, -0.0259, -0.0141, -0.0035, 0.0069, 0.018, 0.0314, 0.0498, respectively.

Results

Correlation results

Number of 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 genes ordered by the value of correlation coefficients.

Table 1. Counts of mRNAseq and number of samples in APOBEC_MutLoad_MinEstimate and expression data sets and common to both

Category	APOBEC_MutLoad_MinEstimate	Expression	Common
Sample	978	1093	974

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 genes ranked by correlation coefficients

geneID	cor	p-value	q-value
HFE2\|148738	0.3074	2.43114595122051e-09	2.22401231617653e-05
CACNA1S\|779	0.268	2.91497706417232e-06	0.00280696949295246
ABRA\|137735	0.2343	1.00563434024536e-07	0.000229988573614115
TBX10\|347853	0.1925	0.000309245891950916	0.0463767445830652
PROKR1\|10887	0.1918	2.37452006577321e-05	0.00868884382467734
OR1Q1\|158131	0.1863	0.000574190064553726	0.0659842181582006
CHRNA4\|1137	0.182	0.000189695270141232	0.0324675201750846
SUSD2\|56241	0.179	1.86831059512116e-08	8.54565266208418e-05
CDH10\|1008	0.1753	0.000315819193703604	0.0465986126451704
ASB5\|140458	0.1752	0.00130308763832487	0.0996150070466432
SNX11\|29916	0.1721	6.5118462622138e-08	0.000170201056019234
OVCH1\|341350	0.1719	0.00155976586092477	0.108096500725301
FBXO40\|51725	0.1678	0.000383190373016262	0.0535179470588209
LOC400891\|400891	0.1649	0.00162179682096375	0.110306299763393
MYL2\|4633	0.1638	0.0012057557025793	0.0955548580695832
KCNT1\|57582	0.1614	6.24746180877267e-06	0.00394150211218292
TEX11\|56159	0.1576	3.53917629292155e-06	0.0030834652121568
SLC10A1\|6554	0.1574	0.00140275816386604	0.103151994109108
TMC1\|117531	0.1573	3.55047654312379e-05	0.0111999170401712
SEPX1\|51734	0.156	1.00074233921887e-06	0.00143152409696613

Methods & Data

Input

Gene level (TCGA Level III) mRNAseq expression data and APOBEC_MutLoad_MinEstimate derived by Mutation_APOBEC pipeline were used to do this analysis. Pearson correlation coefficients were calculated for APOBEC_MutLoad_MinEstimate and each gene across all the samples that were common.

Correlation across sample

Pearson correlation with pairwise.complete.obs was used to do this analysis.