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 -0.0943, -0.0645, -0.0421, -0.0243, -0.0073, 0.0109, 0.0307, 0.0538, 0.0841, 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	0.3795	3.68524226703748e-05	0.059671257949302
PRB2\|653247	0.3525	0.000492695085792194	0.162073286859791
AMY1A\|276	0.336	3.75046233180854e-07	0.00338798014743924
PRB1\|5542	0.3226	0.00112581029720316	0.221087115647494
PARP6\|56965	0.3182	3.03399114720548e-08	0.000548151180565615
LOC153328\|153328	0.3074	0.000976554946509722	0.20960639126307
ITLN2\|142683	0.2657	0.000142987177085052	0.107639555349818
GAGE12J\|729396	0.2611	0.00382301169452592	0.362847821188325
PRB3\|5544	0.26	0.00684226922243347	0.437665105841921
ITGB1BP1\|9270	0.2531	1.28569006339063e-05	0.0332355740204831
HAO2\|51179	0.2507	0.000713792654708723	0.186234049358677
APOA2\|336	0.2477	0.0149574590362018	0.502671930617265
OR7E5P\|219445	0.2476	0.0173368703329628	0.524679719028918
LOC285033\|285033	0.2469	2.10931759365707e-05	0.042343378849558
FGF5\|2250	0.2439	0.000967981775940707	0.20960639126307
GPR26\|2849	0.243	0.0164543755875841	0.514015639904517
RPL23AP82\|284942	0.2389	3.96333146284178e-05	0.059671257949302
NBPF4\|148545	0.2387	0.000267218760003907	0.133162920418091
VIL1\|7429	0.2352	0.0162568094671274	0.511789797132692
CALHM3\|119395	0.2345	0.0160418543810497	0.509364117930447

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.