The Annals of Applied Statistics

Of mice and men: Sparse statistical modeling in cardiovascular genomics

David M. Seo, Pascal J. Goldschmidt-Clermont, and Mike West

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Appl. Stat. Volume 1, Number 1 (2007), 152-178.

Dates
First available in Project Euclid: 29 June 2007

Permanent link to this document
http://projecteuclid.org/euclid.aoas/1183143733

Digital Object Identifier
doi:10.1214/07-AOAS110

Mathematical Reviews number (MathSciNet)
MR2393845

Zentralblatt MATH identifier
1129.62104

Citation

Seo, David M.; Goldschmidt-Clermont, Pascal J.; West, Mike. Of mice and men: Sparse statistical modeling in cardiovascular genomics. The Annals of Applied Statistics 1 (2007), no. 1, 152--178. doi:10.1214/07-AOAS110. http://projecteuclid.org/euclid.aoas/1183143733.


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