Last updated: 2018-06-22

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    File Version Author Date Message
    html 3ab329f Peter Carbonetto 2018-06-18 Build site.
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    html f01cfd4 Peter Carbonetto 2018-06-05 Re-built Uk3 and Uk4 webpages after renaming the R Markdown files.
    Rmd f94acfb Peter Carbonetto 2018-06-05 wflow_publish(c(“Uk3.Rmd”, “Uk4.Rmd”))

Here we plot the correlation matrix for the fourth covariance component, which captures some effects that are stronger in Whole Blood than other tissues.

Set up environment

First, we load a couple plotting packages used in the code chunks below.

library(lattice)
library(colorRamps)

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.

covmat <- readRDS(paste("../output/MatrixEQTLSumStats.Portable.Z.coved.K3.P3",
                        "lite.single.expanded.rds",sep = "."))
pis    <- readRDS(paste("../output/MatrixEQTLSumStats.Portable.Z.coved.K3.P3",
                        "lite.single.expanded.V1.pihat.rds",sep = "."))$pihat
z.stat <- readRDS("../data/MatrixEQTLSumStats.Portable.Z.rds")$strong.z
pi.mat <- matrix(pis[-length(pis)],ncol = 54,nrow = 22,byrow = TRUE)
names  <- colnames(z.stat)

Next, we load the tissue indices:

h <- read.table("../data/uk4rowIndices.txt")[,1]

For the plots of the eigenvectors, we load the colours that are conventionally used to represent the 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,]

Compute the correlations from the \(k=4\) covariance matrix.

k           <- 4
x           <- cov2cor(covmat[[k]])
x[x<0]      <- 0
colnames(x) <- names
rownames(x) <- names

Generate heatmap of Uk4 covariance matrix

Now we produce the heatmap showing the full covariance matrix.

clrs <- colorRampPalette(rev(c("#D73027","#FC8D59","#FEE090","#FFFFBF",
                               "#E0F3F8","#91BFDB","#4575B4")))(64)
lat=x[rev(h),rev(h)]
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 heatmapuk4final-1.png:
Version Author Date
f01cfd4 Peter Carbonetto 2018-06-05

Plot the eigenvector capturing the predominant pattern

The top eigenvector captures the predominant pattern in the Uk4 covariance matrix.

col = as.character(color.gtex[,2])
g=1
v=svd(covmat[[k]])$v[h,]
rownames(v)=colnames(v)=names[h]
par(mar = c(8,4.1,4.1,2.1))
barplot(v[,g]/v[which.max(abs(v[,g])),g],las=2,
        main=paste("Eigenvector",g,"of Uk",k),cex.names = 0.5,
        col=col[h],names=names[h])

Expand here to see past versions of plot-eigenvectors-1.png:
Version Author Date
f01cfd4 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] colorRamps_2.3  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