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September 2012 The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data
Steven P. Lund, Dan Nettleton
Ann. Appl. Stat. 6(3): 1118-1133 (September 2012). DOI: 10.1214/12-AOAS535

Abstract

When analyzing microarray data, hierarchical models are often used to share information across genes when estimating means and variances or identifying differential expression. Many methods utilize some form of the two-level hierarchical model structure suggested by Kendziorski et al. [Stat. Med. (2003) 22 3899–3914] in which the first level describes the distribution of latent mean expression levels among genes and among differentially expressed treatments within a gene. The second level describes the conditional distribution, given a latent mean, of repeated observations for a single gene and treatment. Many of these models, including those used in Kendziorski’s et al. [Stat. Med. (2003) 22 3899–3914] EBarrays package, assume that expression level changes due to treatment effects have the same distribution as expression level changes from gene to gene. We present empirical evidence that this assumption is often inadequate and propose three-level hierarchical models as extensions to the two-level log-normal based EBarrays models to address this inadequacy. We demonstrate that use of our three-level models dramatically changes analysis results for a variety of microarray data sets and verify the validity and improved performance of our suggested method in a series of simulation studies. We also illustrate the importance of accounting for the uncertainty of gene-specific error variance estimates when using hierarchical models to identify differentially expressed genes.

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Steven P. Lund. Dan Nettleton. "The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data." Ann. Appl. Stat. 6 (3) 1118 - 1133, September 2012. https://doi.org/10.1214/12-AOAS535

Information

Published: September 2012
First available in Project Euclid: 31 August 2012

zbMATH: 06096524
MathSciNet: MR3012523
Digital Object Identifier: 10.1214/12-AOAS535

Keywords: differential expression , EM algorithm , hierarchical models , random effects , uncertainty

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.6 • No. 3 • September 2012
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