Last updated: 2018-06-05

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Here we plot the correlation matrix for the second covariance component. Its mixture weight isn’t quite as high as the dominant covariance matrix, but still one of the more predominant components.

The plots generated in the code below should be the same or very similar to the top-left panel of Supplementary Figure 2.

Set up environment

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


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/uk2rowIndices.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 \(k=2\) covariance matrix.

k           <- 2
x           <- cov2cor(covmat[[k]])
x[x < 0]    <- 0
colnames(x) <- names
rownames(x) <- names

Generate heatmap of Uk2 covariance matrix

Now we produce the heatmap showing the full covariance matrix.

clrs <- colorRampPalette(rev(c("#D73027","#FC8D59","#FEE090","#FFFFBF",
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 heatmapuk2final-1.png:
Version Author Date
03a5484 Peter Carbonetto 2018-06-05

Plot eigenvectors capturing predominant patterns

The eigenvectors capture the predominant patterns in the Uk3 covariance matrix.

col = as.character(color.gtex[,2])
for(g in 1:3){
          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
03a5484 Peter Carbonetto 2018-06-05

Expand here to see past versions of plot-eigenvectors-2.png:
Version Author Date
03a5484 Peter Carbonetto 2018-06-05

Expand here to see past versions of plot-eigenvectors-3.png:
Version Author Date
03a5484 Peter Carbonetto 2018-06-05

Session information

# 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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