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 -0.0886, -0.0593, -0.0375, -0.0192, -0.0013, 0.017, 0.03689, 0.0603, 0.09273, 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	510	520	502

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
GAGE4\|2576	0.3616	6.31556404928624e-06	0.00218867555570412
RAX\|30062	0.3038	2.421508061623e-06	0.00148570308619631
PTCHD3\|374308	0.2981	0.000111302217288944	0.0104362864762052
CPN1\|1369	0.2964	0.000105253721326637	0.0102884046407866
WDR87\|83889	0.2582	0.000215163202629043	0.0148099975202867
C1orf126\|200197	0.2476	2.09640704795788e-08	0.0001301050076657
LTA4H\|4048	0.246	2.35184123287269e-08	0.0001301050076657
KIR2DL1\|3802	0.2431	3.91976157509966e-05	0.00610486256162555
CDCP1\|64866	0.2368	7.96948989023605e-08	0.000209233264575369
KLRC2\|3822	0.2331	2.55950215466427e-07	0.000414163600778917
KYNU\|8942	0.2321	1.44266028456741e-07	0.000294591230108665
CT45A5\|441521	0.2313	0.00201273761640564	0.0501899687041702
?\|340602	0.2301	0.0024657148843743	0.0559126054378087
GOLGA6L1\|283767	0.2299	1.29231322532064e-06	0.00131945180305237
CLEC7A\|64581	0.2297	1.95423442539422e-07	0.00035914920269895
MAGEA4\|4103	0.2283	3.44356890424891e-06	0.00158214773305716
KIR2DL4\|3805	0.2276	4.81650013428592e-07	0.000590117596452711
GLDC\|2731	0.2263	3.06303240549255e-07	0.000433018534985708
CALCA\|796	0.2235	0.00449246621456134	0.0755301676408008
SHISA5\|51246	0.2172	8.9825179294678e-07	0.000971063026516231

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.