The MATLAB interface has been tested extensively in MATLAB version 8.6.0 (2015b).
If you find that this software is useful for your research project, please cite our paper:
Carbonetto, P. and Stephens, M. (2012). Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Analysis 7, 73-108.
Copyright (c) 2012-2017, Peter Carbonetto.
The varbvs source code repository by Peter Carbonetto is free software: you can redistribute it under the terms of the GNU General Public License. All the files in this project are part of varbvs. This project is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. See file LICENSE for the full text of the license.
Begin by downloading the github repository for this project. The simplest way to do this is to download the repository as a ZIP archive. Once you have extracted the files from the compressed archive, you will see that the main directory contains two subdirectories, one containing the MATLAB code, and the other containing the files for the R package.
Next, you will need to compile the C code into MATLAB executable (“MEX”) files. To build the necessary MEX files, run the install.m script in MATLAB. For the MEX files to be built successfully, you will need to have a C compiler supported by MATLAB, and you will need to configure MATLAB to build MEX files. See this webpage for more details.
When you take this step, it is important that you configure MATLAB so that it uses the version of the C compiler that is compatible with your version of MATLAB. Otherwise, you will encounter errors when building the MEX files, or MATLAB may crash when attempting to run the examples. If you run into these problems, you may have to run the mex command with the -v flag to check what compiler is being used, and you may have to edit the MEX configuration file manually.
Also note that the beginning of this script sets some compiler and linker flags. These flags tell the GCC compiler to use the ISO C99 standard, and to optimize the code as much as possible. However, these flags may not be relevant to your setup, especially if you are not using gcc. Do not remove flag -DMATLAB_MEX_FILE; this is important for correctly compiling the C code for MATLAB.
The main function of this package is the varbvs function,
which fits the variable selection model to data. In the
demo_qtl.m script, for example, the varbvs function call
is simply
fit = varbvs(X,Z,y,labels,[],struct('logodds',(-3:0.1:-1)));
In this example, we have used varbvs to map a quantitative trait (i.e., a continuously valued outcome) in a small, simulated data set. Additionally, script demo_cc.m demonstrates mapping of a binary valued outcome in a simulated data set.
We have provided a few other MATLAB scripts to demonstrate the application of varbvs to very large data sets: demo_cd.m, demo_celiac.m and demo_cytokine.m. Although we cannot share the data needed to run these scripts due to data privacy restrictions, we have included these scripts anyhow since it is helpful to be able to follow the steps given in these MATLAB scripts. These scripts reproduce some of the results and figures presented in Carbonetto et al (2016).
The varbvs software package was developed by:
Peter Carbonetto
Dept. of Human Genetics, University of Chicago
2012-2017
Xiang Zhou, Xiang Zhu, Matthew Stephens and others have also contributed to the development of this software.