Statistics Papers
Document Type
Journal Article
Date of this Version
10-2014
Publication Source
The Annals of Applied Statistics
Volume
8
Issue
3
Start Page
1750
Last Page
1781
DOI
10.1214/14-AOAS755
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. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat. 4 (2010) 266–298] 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. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. 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. The methods developed in this work are readily available in the R package bart Machine.
Copyright/Permission Statement
The original and published work is available at: https://projecteuclid.org/euclid.aoas/1414091233#abstract
Keywords
variable selection, nonparametric regression, Bayesian learning, machine learning, permutation testing, decision trees, gene regulatory network
Recommended Citation
Bleich, J., Kapelner, A., George, E. I., & Jensen, S. T. (2014). Variable Selection for BART: An Application to Gene Regulation. The Annals of Applied Statistics, 8 (3), 1750-1781. http://dx.doi.org/10.1214/14-AOAS755
Included in
Life Sciences Commons, Medicine and Health Sciences Commons, Physical Sciences and Mathematics Commons
Date Posted: 27 November 2017
This document has been peer reviewed.