Last updated: 2018-05-22

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The plot generated here summarizes eQTL sharing by magnitude between all pairs of tissues. Compare against Figure 6 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$test.b
maxz     <- out$test.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-magnitude statistics

For every pair of tissues, we count the proportion of effects significant in either tissue that are within 2-fold magnitude of one another.

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])##divide effect sizes
    shared.fold.size[i,j]=mean(quotient>0.5&quotient<2)
  }

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

all.tissue.order <- read.table("../output/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))

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 10.13.4

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     

other attached packages:
[1] lattice_0.20-35

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    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