Last updated: 2018-05-07

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File	Version	Author	Date	Message
Rmd	6ebb3f9	Peter Carbonetto	2018-05-07	wflow_publish(“index.Rmd”)
Rmd	c6c8450	Peter Carbonetto	2018-05-07	Setting up workflowr settings.
html	c6c8450	Peter Carbonetto	2018-05-07	Setting up workflowr settings.
Rmd	33f72d6	Peter Carbonetto	2018-05-07	Added workflowr 1.0.1.9000 files.
html	b4e38c2	Gao Wang	2017-09-20	Fix one more broken link
html	80f285f	Gao Wang	2017-09-20	Update figures
html	afc401f	Peter Carbonetto	2017-09-20	Moved doc to docs.

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In this directory, we provide all the instructions for reproducing the GTEx results from: “Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions” (Urbut et al 2017).

Figure 3:Summary of primary patterns identified by mash in GTEx data

Figure 4:Examples illustrating of how mash uses patterns of sharing to inform effect estimates in the GTEx data.

Figure 5:Histogram of Sharing

Figure 6:Pairwise sharing by magnitude of eQTL among tissues

Supplementary Figure 1:Sample sizes and effective sample sizes from mash analysis across tissues

Supplementary Figure 2:There are 4 figures here:

Summary of covariance matrices Uk with largest estimated weight (> 1%) in GTEx data:Uk2

Summary of covariance matrices Uk with largest estimated weight (> 1%) in GTEx data:Uk4

Summary of covariance matrices Uk with largest estimated weight (> 1%) in GTEx data:Uk5

Summary of covariance matrices Uk with largest estimated weight (> 1%) in GTEx data:Uk8

Supplementary Figure 3: Illustration of how Linkage Disequilibrium can impact effect estimate table and figure

Supplementary Figure 4:Pairwise Sharing By Sign

Supplementary Figure 5:Number of “tissue-specific eQTLs” in each tissues.

Supplementary Figure 6:Expression levels in genes with “tissue-specific eQTLs” are similar to those in other genes

Table 1: Heterogeneity Analysis Simulation and Data.

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