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 -0.0882, -0.059, -0.0376, -0.0193, -0.0015, 0.0169, 0.0366, 0.0601, 0.0934, 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	511	520	503

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	0.3557	6.90672372849477e-06	0.00275938627570167
CPN1\|1369	0.3265	1.56144580212292e-05	0.00407112590048528
RAX\|30062	0.2944	4.60858122020014e-06	0.0021353449676404
KIR2DL1\|3802	0.2593	1.07143369818541e-05	0.00312552515956372
TCL1B\|9623	0.257	0.000371209246404325	0.0206729803952081
LTA4H\|4048	0.2569	5.04571251447317e-09	9.27301045909878e-05
MAGEA1\|4100	0.2528	6.26344467222761e-05	0.00844581122176194
?\|340602	0.2508	0.000761353931289399	0.0305669812278026
PTCHD3\|374308	0.2496	0.00131523262413058	0.038731032034741
GOLGA6L1\|283767	0.2458	2.21354630847159e-07	0.000581150772244156
CTAG2\|30848	0.2375	0.000939778129957602	0.0338651813183545
CT45A5\|441521	0.2366	0.00147750845544659	0.0420761123405153
GLDC\|2731	0.2334	1.23103093274679e-07	0.000581150772244156
C1orf126\|200197	0.2326	1.43737367563901e-07	0.000581150772244156
TRIM15\|89870	0.2324	1.24547276270093e-06	0.00104042265604171
KLRC2\|3822	0.2305	3.49768792595739e-07	0.0007142278744805
CDCP1\|64866	0.2299	1.86286452930062e-07	0.000581150772244156
FAM75C1\|441452	0.2297	0.0026603650557937	0.0549968380150468
MAGEA4\|4103	0.2288	3.27932536414721e-06	0.00177257181006757
DPPA2\|151871	0.228	0.00341774010805995	0.0623930335252282

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.