Bayesian identification of differential gene expression induced by metals in human bronchial epithelial cells

Abstract

The study of genetics continues to advance dramatically with the development of microarray technology. In light of the advancements, interesting statistical challenges have arisen. Given that only one observation can be made from each gene on a single array, statisticians are faced with three issues: analysis with more genes than arrays, separating true differential expression from noise, and multiple hypothesis testing for regulation. Within this study, we model the expression of 1185 genes simultaneously in response to five chemical constituents of particulate matter; arsenic, iron, nickel, vanadium, and zinc. Taking advantage of a hierarchical Bayesian mixture model with latent variables, we compare multiple treatments to a control and estimate noise across arrays without assuming equal treatment means for housekeeping genes. To account for model uncertainty and hyperparameter specification, model averaging, MCMC, and Rao-Blackwell estimation are utilized.