Last updated: 2018-06-05

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    Rmd e26bc83 Sarah Margaret Urbut 2016-06-15 updated uk5.Rmd

Here we plot the correlation matrix for the fifth covariance component, which, similarly to the fourth covariance component, 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 = "."))$pihat
z.stat <- readRDS("../data/MatrixEQTLSumStats.Portable.Z.rds")$test.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/uk5rowIndices.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 \(k=5\) covariance matrix.

k <- 5
x <- cov2cor(covmat[[k]])
x[x<0]      <- 0
colnames(x) <- names
rownames(x) <- names

Generate heatmap of Uk5 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))

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])
k=5
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])

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] colorRamps_2.3  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

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