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 -0.0901, -0.0555, -0.0299, -0.0076, 0.0129, 0.0343, 0.0565, 0.0819, 0.1179, 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	178	501	178

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	0.5772	3.16434416447464e-08	0.000585846678610835
C11orf86\|254439	0.4769	2.61463648874027e-05	0.0691533999321963
SLC10A1\|6554	0.4138	2.28025533215437e-05	0.0691533999321963
FAM75C1\|441452	0.4086	0.000885177113341795	0.372458388100227
ATP4B\|496	0.4056	0.000802346794357867	0.372378037004076
ALPP\|250	0.3523	7.41261393644166e-06	0.0410068770173548
OLFM4\|10562	0.3471	4.32222384172398e-06	0.0400108261028389
C11orf85\|283129	0.3425	0.00643617278344699	0.57937921309843
CYP4F8\|11283	0.3382	0.0152177543398606	0.656740102210208
CGA\|1081	0.3333	0.00236277267216645	0.492232359473852
C20orf141\|128653	0.3273	0.00344135298672987	0.544557343558264
INHA\|3623	0.3271	8.85964718966292e-06	0.0410068770173548
AMAC1\|146861	0.3263	0.00849798649148492	0.591472638734405
TRIM17\|51127	0.3156	1.77367862410627e-05	0.0656757720934069
C8G\|733	0.313	3.8124254579941e-05	0.0882290561616283
LCE5A\|254910	0.3036	0.0100476233776328	0.614590738754272
OR1F1\|4992	0.3022	0.00291919812566244	0.510456062793209
CNTD2\|79935	0.2988	6.85069099315427e-05	0.126833693047258
THRSP\|7069	0.2988	0.00788721539538573	0.591472638734405
GPR78\|27201	0.2968	0.0236579483031365	0.704713159984946

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.