Last updated: 2018-06-18

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Expand here to see past versions:
    File Version Author Date Message
    Rmd 6314ce0 Gao Wang 2018-06-16 Relabel ‘test’ to ‘strong’ in data and code
    html 6eee6a9 Peter Carbonetto 2018-06-06 Updated the webpages for a bunch of R Markdown files after minor revisions.
    Rmd 3dc0ba5 Peter Carbonetto 2018-06-06 wflow_publish(c(“ExpressionAnalysis.Rmd”, “fastqtl2mash.Rmd”,
    Rmd 2b0db9b Peter Carbonetto 2018-06-06 Misc. revisions to Rmd files.
    html 678f73e Peter Carbonetto 2018-06-06 Added text to Tspecific analysis file.
    Rmd 0fa57df Peter Carbonetto 2018-06-06 wflow_publish(“Tspecific.Rmd”)
    html df200f8 Peter Carbonetto 2018-06-06 Added text to Tspecific analysis page.
    Rmd 2f3cc45 Peter Carbonetto 2018-06-06 wflow_publish(“Tspecific.Rmd”)
    Rmd 52ecb42 Peter Carbonetto 2018-06-06 I have a complete revision of Tspecific.Rmd without the accompanying text.
    html 52ecb42 Peter Carbonetto 2018-06-06 I have a complete revision of Tspecific.Rmd without the accompanying text.
    Rmd 7fec23c Peter Carbonetto 2018-06-06 Renamed Tspecific analysis.
    html aecc7f1 Peter Carbonetto 2017-09-20 Moved doc to docs.
    Rmd 7072cdb Peter Carbonetto 2017-09-20 Reorganized many of the files.

Despite high average levels of sharing of eQTLs among tissues, mash also identifies eQTLs that are relatively “tissue-specific”. Here we count the number of “tissue-specific” eQTLs in each tissue.

Compare the bar chart at the bottom of this page against Supplementary Figure 5 in the manuscript.

Set up environment

First, we load some functions defined for mash analyses.

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

This is the threshold used to determine which genes have at least one significant effect across tissues.

thresh <- 0.05

Load data and mash results

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

To create the bar chart below, we use the colours that are conventionally used to represent the GTEx tissues in plots.

missing.tissues <- c(7,8,19,20,24,25,31,34,37)
color.gtex      <- read.table("../data/GTExColors.txt",sep = '\t',
                              comment.char = '')[-missing.tissues,]
col = as.character(color.gtex[,2])

Compute number of “tissue-specific” effects for each tissue.

We define “tissue-specific” to mean that the effect is at least 2-fold larger in one tissue than in any other tissue.

nsig                <- rowSums(lfsr.mash < thresh)
pm.mash.beta.norm   <- het.norm(effectsize = pm.mash.beta)
pm.mash.beta.norm   <- pm.mash.beta.norm[nsig > 0,]
lfsr.mash           <- as.matrix(lfsr.mash[nsig > 0,])
colnames(lfsr.mash) <- colnames(maxz)
a         <- which(rowSums(pm.mash.beta.norm > 0.5) == 1)
lfsr.fold <- as.matrix(lfsr.mash[a,])
pm        <- as.matrix(pm.mash.beta.norm[a,])
tspec     <- NULL
for(i in 1:ncol(pm))
  tspec[i] <- sum(pm[,i] > 0.5)
tspec           <- as.matrix(tspec)
rownames(tspec) <- colnames(maxz)

Plot number of “tissue-specific” effects for each tissue

par(mfrow = c(2,1))
barplot(as.numeric(t(tspec)),las = 2,cex.names = 0.75,col = col,
        names = colnames(lfsr.fold))

Expand here to see past versions of plot-counts-1.png:
Version Author Date
df200f8 Peter Carbonetto 2018-06-06

Testis stands out as the tissue with the most tissue-specific effects. Other tissues showing stronger-than-average tissue specificity include skeletal muscle, thyroid and transformed cell lines (fibroblasts and LCLs).

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     
# 
# 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    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