Last updated: 2018-05-18

workflowr checks: (Click a bullet for more information)

  • ✔ 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: 37ef62c

    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:    workflows/.ipynb_checkpoints/
    Ignored:    workflows/.sos/

Untracked files:
    Untracked:  gtex6_workflow_output/
    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.
Expand here to see past versions:
    File Version Author Date Message
    Rmd 80f285f Gao Wang 2017-09-20 Update figures
    html 80f285f Gao Wang 2017-09-20 Update figures

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 = "."))$pihat
z.stat <- readRDS("../data/MatrixEQTLSumStats.Portable.Z.rds")$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("../output/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))

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])

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

This reproducible R Markdown analysis was created with workflowr 1.0.1.9000