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 -0.0945, -0.0647, -0.04202, -0.0244, -0.0071, 0.0107, 0.0306, 0.05348, 0.08384, 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	290	367	290

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
AHSG\|197	0.3797	3.64947199917864e-05	0.0549458421743005
PRB2\|653247	0.351	0.000523283548035547	0.163213817218766
AMY1A\|276	0.3382	3.11037913647283e-07	0.00280976099293273
PRB1\|5542	0.3228	0.00111732722117397	0.225294741216969
PARP6\|56965	0.3195	2.62622421587366e-08	0.000474479929081895
LOC153328\|153328	0.3076	0.000969013082315229	0.213501943392552
ITLN2\|142683	0.2689	0.000118288649798126	0.0926222638907202
GAGE12J\|729396	0.2618	0.00372294721519539	0.344428288745074
PRB3\|5544	0.2617	0.00647732198136652	0.423185745506196
ITGB1BP1\|9270	0.2557	1.03413674445996e-05	0.0311395809369301
HAO2\|51179	0.2515	0.00068438386661196	0.180004570286463
APOA2\|336	0.2509	0.0136709041178493	0.491038220073924
LOC285033\|285033	0.2475	2.01296175987853e-05	0.0398077669095581
OR7E5P\|219445	0.2434	0.0193642893335331	0.540735931249808
FGF5\|2250	0.2424	0.00104218865985661	0.213968437700334
RPL23AP82\|284942	0.2412	3.29491461945608e-05	0.0541174749361027
CALHM3\|119395	0.236	0.0153427263015629	0.507454986682769
NBPF4\|148545	0.2358	0.000318022438845489	0.145724059006324
VIL1\|7429	0.2331	0.0172582734665108	0.515380540032149
ADK\|132	0.2329	6.23935892352101e-05	0.0751509984475027

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.