Last updated: 2018-06-22

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Expand here to see past versions:
    File Version Author Date Message
    html 3ab329f Peter Carbonetto 2018-06-18 Build site.
    Rmd 6314ce0 Gao Wang 2018-06-16 Relabel ‘test’ to ‘strong’ in data and code
    html 1de4a7b Peter Carbonetto 2018-06-05 Rebuilt SharingSign page after renaming and other improvements.
    Rmd 38cbda8 Peter Carbonetto 2018-06-05 wflow_publish(“SharingSign.Rmd”)
    html 38cbda8 Peter Carbonetto 2018-06-05 wflow_publish(“SharingSign.Rmd”)

The plot generated here summarizes eQTL sharing by sign between all pairs of tissues. Compare against Supplementary Figure 4 of the paper.

Set up environment

First, we load the lattice package used for generating the plot below.

library(lattice)

Load data and mash results

In the next code chunk, we load some GTEx summary statistics, as well as some of the results generated from the mash analysis of the GTEx data.

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

Compute sharing-by-sign statistics

For each pair of tissues, compute the estimated proportion of eQTLs that have effect sizes that are the same sign.

thresh=0.05
pm.mash.beta=pm.mash.beta[rowSums(lfsr.all<0.05)>0,]
lfsr.mash=lfsr.all[rowSums(lfsr.all<0.05)>0,]
shared.fold.size=matrix(NA,nrow = ncol(lfsr.mash),ncol=ncol(lfsr.mash))
colnames(shared.fold.size)=rownames(shared.fold.size)=colnames(maxz)
for(i in 1:ncol(lfsr.mash)){
  for(j in 1:ncol(lfsr.mash)){
    sig.row=which(lfsr.mash[,i]<thresh)
    sig.col=which(lfsr.mash[,j]<thresh)
    a=(union(sig.row,sig.col))
    quotient=(pm.mash.beta[a,i]/pm.mash.beta[a,j])
    shared.fold.size[i,j]=mean(quotient > 0)
  }
}

Plot heatmap of sharing by sign

Generate the heatmap using the “levelplot” function from the lattice package.

all.tissue.order = read.table("../data/alltissueorder.txt")[,1]
clrs <- colorRampPalette(rev(c("#D73027","#FC8D59","#FEE090","#FFFFBF",
                               "#E0F3F8","#91BFDB","#4575B4")))(64)
lat=shared.fold.size[rev(all.tissue.order),rev(all.tissue.order)]
lat[lower.tri(lat)] <- NA
n=nrow(lat)
print(levelplot(lat[n:1,],col.regions = clrs,xlab = "",ylab = "",
      colorkey = TRUE))

Expand here to see past versions of heatmap-sharing-sign-1.png:
Version Author Date
38cbda8 Peter Carbonetto 2018-06-05

Session information

sessionInfo()
# R version 3.4.3 (2017-11-30)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS High Sierra 10.13.5
# 
# 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     
# 
# other attached packages:
# [1] lattice_0.20-35
# 
# loaded via a namespace (and not attached):
#  [1] workflowr_1.0.1.9000 Rcpp_0.12.17         digest_0.6.15       
#  [4] rprojroot_1.3-2      R.methodsS3_1.7.1    grid_3.4.3          
#  [7] backports_1.1.2      git2r_0.21.0         magrittr_1.5        
# [10] evaluate_0.10.1      stringi_1.1.7        whisker_0.3-2       
# [13] R.oo_1.21.0          R.utils_2.6.0        rmarkdown_1.9       
# [16] tools_3.4.3          stringr_1.3.0        yaml_2.1.18         
# [19] compiler_3.4.3       htmltools_0.3.6      knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.0.1.9000