marble

Robust Marginal Bayesian Variable Selection

Recently, multiple marginal variable selection methods have been developed and shown to be effective in Gene-Environment interactions studies. We propose a novel marginal Bayesian variable selection method for Gene-Environment interactions studies. In particular, our marginal Bayesian method is robust to data contamination and outliers in the outcome variables. With the incorporation of spike-and-slab priors, we have implemented the Gibbs sampler based on Markov Chain Monte Carlo (MCMC). The core algorithms of the package have been developed in C++.

How to install

• To install from github, run these two lines of code in R

install.packages("devtools")
devtools::install_github("xilustat/marble")

Examples

Example.1 (default method: robust sparse marginal selection)

library(marble)
data(dat)

max.steps=10000
fit=marble(X, Y, E, clin, max.steps=max.steps)
## coefficients of parameters
fit$coefficient
## Estimated values of main G effects 
fit$coefficient$G
## Estimated values of interactions effects 
fit$coefficient$GE
## Rank list of main G effects and interactions 
fit$ranklist
selected=GxESelection(fit,sparse=TRUE)
selected

Example.2 (alternative: non-robust sparse group selection)

fit=marble(X, Y, E, clin, max.steps=max.steps, sparse=FALSE)
selected=GxESelection(fit,sparse=FALSE)
selected

Methods

This package provides implementation for methods proposed in

• Lu, X., Fan, K., Ren, J., and Wu, C. (2021). Identifying Gene–Environment Interactions With Robust Marginal Bayesian Variable Selection. Frontiers in Genetics. 12:667074.