Set up data

library(lattice)
library(ggplot2)
library(colorRamps)
library(mashr)

Loading required package: ashr

library(corrplot)

corrplot 0.84 loaded

data = readRDS('../data/ImmuneQTLSummary.4MASH.rds')
data$max$se = data$max$beta/data$max$z
data$null$se = data$null$beta / data$null$z

K = 10
P = 5
vhat = 1

if (vhat == 1) {
  V = cor(data$null$z[which(apply(abs(data$null$z),1, max) < 2),])
} else {
  V = diag(ncol(data$null$z))
}
mash_data = mashr::set_mash_data(Bhat = as.matrix(data$max$beta), 
                                 Shat = as.matrix(data$max$se), 
                                 V = as.matrix(V), 
                                 alpha = 1)

MASH results

# EZ
resEZ = readRDS('../output/ImmuneEZ.V1.center.mash_model.Load.K10.P5.rds')
resEZ$result = readRDS('../output/ImmuneEZ.V1.center.mash_posterior.Load.K10.P5.rds')

MASH model fit

The log-likelihood of fit is

get_loglik(resEZ)

[1] 3201420

The log likelihood is slightly higher than the original one EZ_V1_model, which is as we expected. There are more potential covariance structures added in the model.

Here is a plot of weights learned. The results are similar as before.

options(repr.plot.width=12, repr.plot.height=4)
barplot(get_estimated_pi(resEZ), las = 2, cex.names = 0.7)

Most of the mass is on the null, PCA1 and equal effects. mash placed 9.4814% of the mixture components weight on data-driven matrices, 5.4593% weight on equal effects matrix.

Pairwise sharing

x           <- get_pairwise_sharing(resEZ)
colnames(x) <- colnames(get_lfsr(resEZ))
rownames(x) <- colnames(x)
x <- x[rev(rownames(x)),rev(colnames(x))]
x[lower.tri(x)] <- NA
clrs <- colorRampPalette(rev(c("#D73027","#FC8D59","#FEE090","#FFFFBF",
                               "#E0F3F8","#91BFDB","#4575B4")))(64)
n <- nrow(x)
options(repr.plot.width=9, repr.plot.height=9)
print(levelplot(x[n:1,],col.regions = clrs,xlab = "",ylab = "",
                colorkey = TRUE, at = seq(0.7,1,length.out = 64),
                scales = list(cex = 0.5,x = list(rot = 45))))

Compare with original paper

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 10.13.2

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] corrplot_0.84   mashr_0.2-4     ashr_2.1-27     colorRamps_2.3 
[5] ggplot2_2.2.1   lattice_0.20-35

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.14      compiler_3.4.3    git2r_0.20.0     
 [4] plyr_1.8.4        iterators_1.0.9   tools_3.4.3      
 [7] digest_0.6.13     evaluate_0.10.1   tibble_1.3.4     
[10] gtable_0.2.0      rlang_0.1.6       Matrix_1.2-12    
[13] foreach_1.4.4     yaml_2.1.16       parallel_3.4.3   
[16] mvtnorm_1.0-6     stringr_1.2.0     knitr_1.17       
[19] rprojroot_1.2     grid_3.4.3        rmarkdown_1.8    
[22] rmeta_2.16        magrittr_1.5      backports_1.1.2  
[25] scales_0.5.0      codetools_0.2-15  htmltools_0.3.6  
[28] MASS_7.3-47       assertthat_0.2.0  colorspace_1.3-2 
[31] labeling_0.3      stringi_1.1.6     lazyeval_0.2.1   
[34] munsell_0.4.3     doParallel_1.0.11 pscl_1.5.2       
[37] truncnorm_1.0-7   SQUAREM_2017.10-1