Last updated: 2018-06-05

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    Ignored:    .sos/
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Unstaged changes:
    Modified:   analysis/gtex.Rmd
    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.
Expand here to see past versions:
    File Version Author Date Message
    Rmd 35816c4 Peter Carbonetto 2018-06-05 wflow_publish(“SharingMag.Rmd”)
    Rmd 747f788 Peter Carbonetto 2018-06-05 Rebuilt SharingSign page after renaming and other improvements.
    html 747f788 Peter Carbonetto 2018-06-05 Rebuilt SharingSign page after renaming and other improvements.

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

Plot heatmap of sharing by magnitude

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-magnitude-1.png:
Version Author Date
747f788 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 10.13.4
# 
# 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