Campus Units
Statistics
Document Type
Article
Publication Version
Published Version
Publication Date
2012
Journal or Book Title
The Annals of Applied Statistics
Volume
6
Issue
3
First Page
1118
Last Page
1133
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.
Copyright Owner
Institute of Mathematical Statistics
Copyright Date
2012
Language
en
File Format
application/pdf
Recommended Citation
Lund, Steven P. and Nettleton, Dan, "The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data" (2012). Statistics Publications. 224.
https://lib.dr.iastate.edu/stat_las_pubs/224
Included in
Applied Statistics Commons, Genetics Commons, Microarrays Commons, Statistical Methodology Commons, Statistical Models Commons
Comments
This article is published as Lund, Steven P., and Dan Nettleton. "The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data." The Annals of Applied Statistics 6, no. 3 (2012): 1118-1133. doi: 10.1214/12-AOAS535. Posted with permission.