Open Access
June 2007 Of mice and men: Sparse statistical modeling in cardiovascular genomics
David M. Seo, Pascal J. Goldschmidt-Clermont, Mike West
Ann. Appl. Stat. 1(1): 152-178 (June 2007). DOI: 10.1214/07-AOAS110


In high-throughput genomics, large-scale designed experiments are becoming common, and analysis approaches based on highly multivariate regression and anova concepts are key tools. Shrinkage models of one form or another can provide comprehensive approaches to the problems of simultaneous inference that involve implicit multiple comparisons over the many, many parameters representing effects of design factors and covariates. We use such approaches here in a study of cardiovascular genomics. The primary experimental context concerns a carefully designed, and rich, gene expression study focused on gene-environment interactions, with the goals of identifying genes implicated in connection with disease states and known risk factors, and in generating expression signatures as proxies for such risk factors. A coupled exploratory analysis investigates cross-species extrapolation of gene expression signatures—how these mouse-model signatures translate to humans. The latter involves exploration of sparse latent factor analysis of human observational data and of how it relates to projected risk signatures derived in the animal models. The study also highlights a range of applied statistical and genomic data analysis issues, including model specification, computational questions and model-based correction of experimental artifacts in DNA microarray data.


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David M. Seo. Pascal J. Goldschmidt-Clermont. Mike West. "Of mice and men: Sparse statistical modeling in cardiovascular genomics." Ann. Appl. Stat. 1 (1) 152 - 178, June 2007.


Published: June 2007
First available in Project Euclid: 29 June 2007

zbMATH: 1129.62104
MathSciNet: MR2393845
Digital Object Identifier: 10.1214/07-AOAS110

Keywords: Animal–human extrapolation , atherosclerosis risk factors , gene expression signatures , gene-environment interactions , latent factor models , multivariate anova , sparse statistical modeling

Rights: Copyright © 2007 Institute of Mathematical Statistics

Vol.1 • No. 1 • June 2007
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