Last updated: 2018-06-05

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Having estimated patterns of sharing from the data, mash exploits these patterns to improve effect estimates at each putative eQTL. Here we give three examples can provide intuition into the way that mash achieves improved accuracy of effect estimation.

Compare the plots against Figure 4 of the paper.

Set up environment

First, we load the rmeta package, as well as some functions defined for mash analyses.


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.

out    <- readRDS("../data/MatrixEQTLSumStats.Portable.Z.rds")
b.stat <- out$test.b
z.stat <- out$test.z
out    <- readRDS(paste("../output/MatrixEQTLSumStats.Portable.Z.coved.K3.P3",
                        "lite.single.expanded.V1.posterior.rds",sep = "."))
posterior.means <- out$posterior.means
lfsr            <- out$lfsr
mar.var         <- out$marginal.var
colnames(lfsr)  <-
  colnames(mar.var) <- 
  colnames(posterior.means) <- 
standard.error  <- b.stat/z.stat
posterior.betas <- standard.error * posterior.means
pm.beta.norm    <- het.norm(posterior.betas)

Load the tissue indices:

missing.tissues <- c(7,8,19,20,24,25,31,34,37)
uk3labels       <- read.table("../data/uk3rowindices.txt")[,1]

For the plots of the eigenvectors, we load the colours that are conventionally used to represent the tissues in plots.

gtex.colors <- read.table('../data/GTExColors.txt', sep = '\t', 
                          comment.char = '')[-missing.tissues,2]
gtex.colors <- gtex.colors[uk3labels]

MCPH1 gene

In the first example, the vast majority of effect estimates are positive in each tissue, with the strongest signals in a subset of brain tissues. Based on the patterns of sharing learned in the first step, mash estimates the effects in all tissues to be positive—even those with negative observed effects. This is because the few modest negative effects at this eQTL are outweighed by the strong background information that effects are highly correlated among tissues.

To create the metaplot, we first define a function which we will use in this example, and reuse in subsequent examples.

create.metaplot <- function (j) {

  pm.beta.norm  <- pm.beta.norm[,uk3labels] # Shuffle columns.
  z.shuffle     <- z.stat[,uk3labels]
  b.shuffle     <- b.stat[,uk3labels]
  post.var      <- mar.var[,uk3labels]
  post.bshuffle <- posterior.betas[,uk3labels]
  sem.shuffle   <- standard.error[,uk3labels]
  lfsr          <- lfsr[,uk3labels]
  plot.title    <- strsplit(rownames(z.stat)[j], "[.]")[[1]][1]

  x <- as.numeric(post.bshuffle[j,])

  metaplot(as.numeric(b.shuffle[j,]),as.numeric(sem.shuffle[j,]),xlab = "",
           ylab = "",colors = meta.colors(box = as.character(gtex.colors)),
           xlim = c(-1,1))

  # Transform to posterior sd of beta.
  sd <- as.numeric(sem.shuffle[j,]) * sqrt(as.numeric(post.var[j,]))
  metaplot(x,sd,xlab = "",ylab = "",
           colors = meta.colors(box = as.character(gtex.colors)),
           xlim = c(-1,1))

Now create the MCHP1 metaplot summarizing the eQTL effect estimates. The original effect estimates are shown on the left-hand side, and the shrunken estimates from mash are shown on the right-hand side.

create.metaplot(which(rownames(z.stat) ==

Expand here to see past versions of metaplot-MCPH1-1.png:
Version Author Date
12aa3cb Peter Carbonetto 2018-06-05

FLJ13114 gene

In the second example, the effect estimates in non-brain tissues are mostly positive, but modest in size, and only one effect is, individually, nominally significant. However, combining information among tissues, mash effect estimates in non-brain tissues are all positive, and mostly “significant”.

create.metaplot(which(rownames(z.stat) ==

Expand here to see past versions of metaplot-FLJ13114-1.png:
Version Author Date
12aa3cb Peter Carbonetto 2018-06-05

RALBP1 gene

In this example, the effect estimates vary in sign, and are modest except for a very strong signal in whole blood. While whole-blood-specific effects are estimated to be rare, mash recognizes that the strong data at this eQTL outweigh this background information, and estimates a strong effect in blood with insignificant effects in other tissues.

create.metaplot(which(rownames(z.stat) ==

Expand here to see past versions of metaplot-RALBP1-1.png:
Version Author Date
12aa3cb 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] grid      stats     graphics  grDevices utils     datasets  methods  
# [8] base     
# other attached packages:
# [1] rmeta_2.16
# 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    backports_1.1.2     
#  [7] git2r_0.21.0         magrittr_1.5         evaluate_0.10.1     
# [10] stringi_1.1.7        whisker_0.3-2        R.oo_1.21.0         
# [13] R.utils_2.6.0        rmarkdown_1.9        tools_3.4.3         
# [16] stringr_1.3.0        yaml_2.1.18          compiler_3.4.3      
# [19] htmltools_0.3.6      knitr_1.20

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