apaQTL effect size by eQTL status

Last updated: 2020-02-06

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Knit directory: apaQTL/analysis/

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	76c8064	brimittleman	2020-02-06	add effect size plot for eQTL

library(workflowr)

This is workflowr version 1.5.0
Run ?workflowr for help getting started

library(tidyverse)

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library(ggpubr)

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nomnames=c("peakID", 'snp','dist', 'pval', 'slope')

nuclearapaUnexplained=read.table("../data/overlapeQTL_try2/apaNuclear_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval)) %>% mutate(Set="Unexplained")

nuclearapaexplained=read.table("../data/overlapeQTL_try2/apaNuclear_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))%>% mutate(Set="Explained")


NuclearBoth= nuclearapaUnexplained %>% bind_rows(nuclearapaexplained)



ggplot(NuclearBoth, aes(y=abs(slope), x=Set,fill=Set)) + geom_boxplot() + stat_compare_means() + scale_fill_brewer(palette = "Dark2") + theme(legend.position = "none") + labs(x="eQTL Set", y="abs(apaQTL Effect size)", title="Unexplained eQTLs have stronger effect sizes in apaQTLs")

totalapaUnexplained=read.table("../data/overlapeQTL_try2/apaTotal_unexplainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp)  %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>% dplyr::slice(which.min(adjPval)) %>% mutate(Set="Unexplained")

totlaapaexplained=read.table("../data/overlapeQTL_try2/apaTotal_explainedQTLs.txt", stringsAsFactors = F, col.names = nomnames) %>% separate(peakID, into=c("chr","start","end","geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc", "strand", "PASnum"), sep="_") %>% group_by(gene, snp) %>%  mutate(nPeaks=n(), adjPval=pval* nPeaks) %>%  dplyr::slice(which.min(adjPval))%>% mutate(Set="Explained")


TotalBoth= totalapaUnexplained %>% bind_rows(totlaapaexplained)



ggplot(TotalBoth, aes(y=abs(slope), x=Set,fill=Set)) + geom_boxplot() + stat_compare_means() + scale_fill_brewer(palette = "Dark2") + theme(legend.position = "none") + labs(x="eQTL Set", y="abs(Total mRNA apaQTL Effect size)", title="Unexplained eQTLs have stronger effect sizes in Total apaQTLs")

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

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attached base packages:
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other attached packages:
 [1] ggpubr_0.2      magrittr_1.5    forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1     tidyr_0.8.3    
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[37] cli_1.1.0          tools_3.5.1        lazyeval_0.2.1    
[40] crayon_1.3.4       whisker_0.3-2      pkgconfig_2.0.2   
[43] xml2_1.2.0         lubridate_1.7.4    assertthat_0.2.0  
[46] rmarkdown_1.10     httr_1.3.1         rstudioapi_0.10   
[49] R6_2.3.0           nlme_3.1-137       git2r_0.26.1      
[52] compiler_3.5.1

apaQTL effect size by eQTL status

Briana Mittleman

2/6/2020