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 -0.14214, -0.0984, -0.06712, -0.0422, -0.0178, 0.0059, 0.031, 0.061, 0.101, 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	194	304	193

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
MYL7\|58498	0.4852	5.84671357772848e-06	0.0118149088709053
PRSS37\|136242	0.4703	0.000149425389998292	0.0438322510951442
MAGEA4\|4103	0.3741	0.000357731367427894	0.0625846835070673
ADCYAP1R1\|117	0.363	0.00253461389236964	0.14133976848833
PRTN3\|5657	0.3625	0.0032438936092527	0.152677746344702
MAGEA8\|4107	0.3612	5.04354682537844e-05	0.0269785253273993
C3orf49\|132200	0.3567	0.00554816038790729	0.181668944913246
GJB7\|375519	0.3537	1.69315037301665e-05	0.0153448213690089
SSX4\|6759	0.3275	0.006013199160531	0.187033783856913
PGPEP1L\|145814	0.32	9.23039321381047e-05	0.0349735752874106
TAS2R13\|50838	0.3081	0.0176144985265181	0.273302964548592
PDP1\|54704	0.3064	1.46900584154785e-05	0.0153448213690089
KIR3DL3\|115653	0.3034	0.00214751446585359	0.135928921714178
RFPL4A\|342931	0.3009	0.0096820305522165	0.222383736120283
TRIM17\|51127	0.2968	2.77203142649007e-05	0.0195251528621723
LOC100271832\|100271832	0.2881	0.0148331201626304	0.256191791450863
GBP5\|115362	0.2831	6.62581840529253e-05	0.0314674224439581
COX8C\|341947	0.2816	0.0103872067642137	0.226986527155563
P2RY6\|5031	0.2816	7.28361894841711e-05	0.0327766360846321
C1orf49\|84066	0.2805	0.0178281335883055	0.273357709320204

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.