Last updated: 2018-06-22

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This repository contains code and data resources to accompany our research paper:

Sarah M. Urbut, Gao Wang and Matthew Stephens (2017). Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. bioRxiv doi:10.1101/096552.

We provide four sets of resources:

  1. If you are primarily interested in applying the multivariate adaptive shrinkage (mash) methods to your own data, please see the mashr R package.

  2. If you would like to obtain the exact GTEx association statistics used in the multivariate adaptive shrinkage (mash) analysis described in our research paper (e.g., so you can apply your method of choice to these data, and compare against our results), please see here.

  3. If you would like follow the exact steps we took to produce the results of the GTEx analysis presented in the manuscript, follow the instructions here. Note: The mashr package was not used in this analysis because mashr was only developed afterward.

  4. If you are working with association statistics from the GTEx study, or similar genetic association data, please see here for our code to convert association statistics in the FastQTL format, or a similar format, to a format that is more suitable for analysis with mash.

Citing this work

If you find any of the source code in this repository useful for your work, please cite our manuscript, Urbut et al (2017). The full citation is given above. Please also cite the Zenodo archive:

Sarah M. Urbut, Gao Wang, Peter Carbonetto and Matthew Stephens (2018), Code and data resources accompanying Urbut et al 2017, version 1.0, Zenodo, doi:10.5281/zenodo.1296399.


Copyright (c) 2016-2018, Sarah Urbut, Gao Wang, Peter Carbonetto and Matthew Stephens.

All source code and software in this repository are made available under the terms of the MIT license.

What’s included in the git repository

See here for the source repository. This is what you will find in the repository:

├── analysis
├── code
├── data
├── docs
├── output
└── workflows


This project was developed by Sarah Urbut, Gao Wang, Peter Carbonetto and Matthew Stephens at the University of Chicago.

This reproducible R Markdown analysis was created with workflowr