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March 2006 Bayesian identification of differential gene expression induced by metals in human bronchial epithelial cells
Merlise A. Clyde, Leanna L. House, Yuh-Chin T. Huang
Bayesian Anal. 1(1): 105-120 (March 2006). DOI: 10.1214/06-BA103

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.

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Merlise A. Clyde. Leanna L. House. Yuh-Chin T. Huang. "Bayesian identification of differential gene expression induced by metals in human bronchial epithelial cells." Bayesian Anal. 1 (1) 105 - 120, March 2006. https://doi.org/10.1214/06-BA103

Information

Published: March 2006
First available in Project Euclid: 22 June 2012

zbMATH: 1331.62423
MathSciNet: MR2227366
Digital Object Identifier: 10.1214/06-BA103

Keywords: Bayesian , differential expression , hierarchical model , latent variables , macroarray , MCMC , microarray , Model selection , toxicology

Rights: Copyright © 2006 International Society for Bayesian Analysis

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