Last updated: 2018-06-06

• ✔ R Markdown file: up-to-date

  Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

• ✔ Environment: empty

  Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

• ✔ Seed: set.seed(1)

  The command set.seed(1) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

• ✔ Session information: recorded

  Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

• ✔ Repository version: 0226fe3

  Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
  
  Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

  
  Ignored files:
      Ignored:    .sos/
      Ignored:    data/.sos/
      Ignored:    output/MatrixEQTLSumStats.Portable.Z.coved.K3.P3.lite.single.expanded.V1.loglik.rds
      Ignored:    workflows/.ipynb_checkpoints/
      Ignored:    workflows/.sos/
  
  Untracked files:
      Untracked:  fastqtl_to_mash_output/
      Untracked:  gtex6_workflow_output/
  
  Unstaged changes:
      Modified:   analysis/Tspecific.Rmd
      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.

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