Variable Selection for BART: An Application to Gene Regulation

• Activity Number: 229
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistical Computing
• Author(s): Justin Bleich and Edward I. George and Shane T. Jensen* and Adam Kapelner
• Companies: University of Pennsylvania and The Wharton School and University of Pennsylvania and Queens College
• Keywords: nonparametric regression ; permutation inference ; tree models

Abstract

We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Bayesian Additive Regression Trees (BART, Chipman, George, and McCulloch, 2010) provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (Saccharomyces cerevisiae). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods.