Description

Gene regulatory networks play a crucial role in controlling an organism’s biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. We propose a novel efficient Bayesian method (BFCS) for discovering local causal relationships among triplets of (normally distributed) variables. In our approach, we score covariance structures for each triplet in one go and incorporate available background knowledge in the form of priors to derive posterior probabilities over local causal structures. Our method is flexible in the sense that it allows for different types of causal structures and assumptions. The proposed algorithm produces stable and conservative posterior probability estimates over local causal structures that can be used to derive an honest ranking of the most meaningful regulatory relationships.

Content

The data set contains source code implementing the BFCS algorithm, which is described in the article titled "A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks" by Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen and Tom Heskes, as well as in the follow-up extension titled "Large-scale Local Causal Inference of Gene Regulatory Relationships. The data set also contains simulated data necessary for reproducing the figures in the article as well as routines necessary for recreating it. This research is presented in Chapter 3 of the PhD thesis titled "Being Bayesian about Causal Inference" by Ioan Gabriel Bucur. The code is written in the R and C++ programming languages.

Structure

The code is structured on the skeleton of an R package package as follows:

• The folder data contains pre-saved simulated data, which we use to recreate the figures from the article. The simulated data can also be reproduced using the scripts/reproduce-data.R R script. The simulated data sets are described in R/data.R.

• The folder R contains the R files that implement the BFCS algorithm and the routines necessary for reproducing the figures from the article. The main method is implemented in R/compute_BFCS_vectorized.R.

• The folder man contains the documentation for the implemented functions.

• The folder src contains an efficient Rcpp implementation for computing the BGe score described in Geiger and Heckerman (2002).

• The folder scripts contains the script pgm2018-article-figures.R, which can be run from R to produce the figures in the PGM 2018 article and the script ijar-article-figures.R, which can produce the figures in the IJAR extension. Finally reproduce-data.R is a script for reproducing the simulated data and the pre-computed posterior probabilities.

• The folder tests contains a few basic unit tests for the R package.

• The top folder also contains the following files:

  • DESCRIPTION is the file describing the R package
  • NAMESPACE is the file specifying the functions provided by the R package
  • LICENSE.md is the file containing the GPL-3 license

Prerequisites

In order to install the software, R must be downloaded and installed. For reproducing the tables from the articles, the TeX system including XeTeX is required. A portable distribution satisfying this requirement is TeX Live.

Installation Instructions

Download the software from GitHub with the following command: git clone https://github.com/igbucur/BFCS.git. For installing and running the BFCS R package, several R package are required. These are specified in the package DESCRIPTION file.

To install the package, open an R instance and run (from the BFCS folder):

install.packages('devtools') # required package
devtools::install_deps(".", dependencies = TRUE) # install BFCS package dependencies
install.packages(".", repos = NULL, type = 'source') # install BFCS

library(BFCS) # load the package
help(package = "BFCS") # see available functions

Licensing

BFCS algorithm - Bayes Factors of Covariance Structures

Copyright (C) 2020 Ioan Gabriel Bucur ioan.gabriel.bucur@gmail.com

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program 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 the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.