Last updated: 2018-06-06

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Here we develop another summary of sharing of eQTLs across tissues by showing the estimated distribution of sharing; specifically, the distribution of the number of tissues shared by sign and by magnitude. This complements this summary and this one.

Compare the plots below to the plots in Figure 5 of the paper.

source("../code/normfuncs.R")

thresh <- 0.05

out     <- readRDS("../data/MatrixEQTLSumStats.Portable.Z.rds")
maxbeta <- out$test.b
maxz    <- out$test.z
out <- readRDS(paste("../output/MatrixEQTLSumStats.Portable.Z.coved.K3.P3",
                     "lite.single.expanded.V1.posterior.rds",sep = "."))
lfsr           <- out$lfsr
pm.mash        <- out$posterior.means
standard.error <- maxbeta/maxz
pm.mash.beta   <- pm.mash * standard.error

sigmat <- (lfsr <= thresh)
nsig   <- rowSums(sigmat)
par(mar = c(4,4,2,1))
par(oma = c(8,4,0,0) + 0.1)
par(mfrow = c(1,3))
hist((het.func(het.norm(effectsize=pm.mash.beta[nsig>0,]),threshold=0.5)),
     main="",xlab="",breaks=0.5:44.5,col="grey",freq=FALSE,ylim=c(0,0.9),
     xaxt="n")
axis(1,at = seq(1, 44, by=1),labels = c(1:44))
mtext("All Tissues")

sigmat <- (lfsr[,-c(7:16)] <= thresh)
nsig   <- rowSums(sigmat)
hist((het.func(het.norm(effectsize=pm.mash.beta[nsig>0,-c(7:16)]),
     threshold=0.5)),main="",xlab="",breaks=0.5:34.5,col="grey",
     freq=FALSE,ylim=c(0,0.9),xaxt="n")
axis(1,at = seq(1, 34, by=1),labels = c(1:34))
mtext("Non-Brain Tissues")

sigmat     <- (lfsr[,c(7:16)]<=thresh)
nsig       <-  rowSums(sigmat)
brain.norm <- het.norm(effectsize=pm.mash.beta[nsig>0,c(7:16)])
hist(het.func(brain.norm,threshold=0.5),main="",xlab="",breaks=0.5:10.5,
     col="grey",freq=FALSE,xaxt="n",ylim=c(0,0.9))
axis(1, at=seq(1, 10, by=1), labels=c(1:10))
mtext("Brain Tissues")

sign.func <- function (normeffectsize)
  apply(normeffectsize,1,function(x)(sum(x>0)))
sigmat <- (lfsr<=thresh)
nsig   <- rowSums(sigmat)
par(mar = c(4,4.5,2,1))
par(oma = c(8,4,0,0) + 0.1)
par(mfrow = c(1,3))
hist(sign.func(het.norm(effectsize=pm.mash.beta[nsig>0,])),main="",xlab="",
     breaks=0.5:44.5,col="grey",freq=FALSE,xaxt="n",ylim=c(0,0.9))
axis(1, at=seq(1, 44, by=1), labels=c(1:44))
mtext("All Tissues")

sigmat <- (lfsr[,-c(7:16)] <= thresh)
nsig   <- rowSums(sigmat)
hist(sign.func(het.norm(effectsize = pm.mash.beta[nsig>0,-c(7:16)])),
     main="",xlab="",breaks=0.5:34.5,col="grey",freq=FALSE,ylim=c(0,0.9),
     xaxt="n")
axis(1, at=seq(1, 34, by=1), labels=c(1:34))
mtext("Non-Brain Tissues")

sigmat     <- (lfsr[,c(7:16)]<=thresh)
nsig       <-  rowSums(sigmat)
brain.norm <- het.norm(effectsize=pm.mash.beta[nsig>0,c(7:16)])
hist(sign.func(brain.norm),main="",xlab="",breaks=0.5:10.5,col="grey",
     freq=FALSE,xaxt="n",ylim=c(0,0.9))
axis(1, at=seq(1, 10, by=1), labels=c(1:10))
mtext("Brain Tissues")

sessionInfo()
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.4
# 
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# loaded via a namespace (and not attached):
#  [1] workflowr_1.0.1.9000 Rcpp_0.12.16         digest_0.6.15       
#  [4] rprojroot_1.3-2      R.methodsS3_1.7.1    backports_1.1.2     
#  [7] git2r_0.21.0         magrittr_1.5         evaluate_0.10.1     
# [10] stringi_1.1.7        whisker_0.3-2        R.oo_1.21.0         
# [13] R.utils_2.6.0        rmarkdown_1.9        tools_3.4.3         
# [16] stringr_1.3.0        yaml_2.1.18          compiler_3.4.3      
# [19] htmltools_0.3.6      knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.0.1.9000