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, -0.0981, -0.0672, -0.042, -0.0175, 0.006, 0.031, 0.0613, 0.10194, 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	0.4827	6.64923105020421e-06	0.0126330189235998
PRSS37\|136242	0.4726	0.000137425189183471	0.0423618968759286
MAGEA4\|4103	0.3787	0.00029862119846058	0.0622802215913949
MAGEA8\|4107	0.3623	4.78680021336331e-05	0.0262088296248366
PRTN3\|5657	0.3622	0.00327062059122873	0.151741777277237
ADCYAP1R1\|117	0.3582	0.00292085363789241	0.147559903089859
GJB7\|375519	0.3546	1.59714488345841e-05	0.0156125478889854
C3orf49\|132200	0.3518	0.00628123699036576	0.18666153128069
SSX4\|6759	0.326	0.00627088193478142	0.186658804824664
PGPEP1L\|145814	0.3202	9.11345316541023e-05	0.0352651856849608
TAS2R13\|50838	0.3051	0.0187970016258188	0.274399823538777
PDP1\|54704	0.3041	1.70771108289891e-05	0.0156125478889854
KIR3DL3\|115653	0.304	0.00210728919112269	0.132829111132642
TRIM17\|51127	0.2992	2.37569987013853e-05	0.0180028556409206
C1orf49\|84066	0.2983	0.0115214676572508	0.235404592024457
RFPL4A\|342931	0.2972	0.0106795126898511	0.226947185829069
COX8C\|341947	0.2887	0.00851813236342114	0.208505078456986
GBP5\|115362	0.2879	4.89965473824405e-05	0.0262088296248366
LOC100271832\|100271832	0.2876	0.0150117184467025	0.254487010767607
P2RY6\|5031	0.2831	6.63859744596262e-05	0.0301840429374306

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.