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 -0.091, -0.0558, -0.03001, -0.0077, 0.0131, 0.03478, 0.0571, 0.08304, 0.1193, 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	177	501	177

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
TSPYL6\|388951	0.5781	2.98381297447747e-08	0.000552423134094759
C11orf86\|254439	0.4779	2.50474980545423e-05	0.0662470541402566
SLC10A1\|6554	0.4159	2.05020617163321e-05	0.0632625284360286
FAM75C1\|441452	0.4083	0.000894303331782531	0.367936264102706
ATP4B\|496	0.4056	0.000801333902122892	0.362553969104832
ALPP\|250	0.3571	5.86888532749796e-06	0.0362188476510991
OLFM4\|10562	0.3512	3.48935851168797e-06	0.0323009917426955
C11orf85\|283129	0.3425	0.00688754967740124	0.579950713666998
CYP4F8\|11283	0.3394	0.0148429297342312	0.645632246523302
CGA\|1081	0.3345	0.00242376193145444	0.476423030323529
AMAC1\|146861	0.3274	0.00827759667052819	0.593874537637112
C20orf141\|128653	0.3272	0.00345293134324942	0.532729757407665
INHA\|3623	0.3265	9.76297271670923e-06	0.0451879192192887
TRIM17\|51127	0.316	1.83494550967112e-05	0.0632625284360286
C8G\|733	0.313	4.02855914163336e-05	0.09323092993525
LCE5A\|254910	0.3046	0.00979684226331745	0.596182849930428
OR1F1\|4992	0.3028	0.00285761779867921	0.497923409941477
THRSP\|7069	0.2994	0.00774556144293648	0.585994538325235
GPR78\|27201	0.2975	0.0233062458127198	0.683193044987412
TMED1\|11018	0.2969	5.99622199286554e-05	0.123348948862125

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.