February 2007
Annals of Applied Statistics, 1(1), 152-178.
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 focussed 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.Keywords: animal-human extrapolation, atherosclerosis risk factors, gene-environment interactions, gene expression signatures, multivariate anova, latent factor models, sparse statistical modelling.
The manuscript is available electronically at the Annals of Applied Statistics site and in the printed journal issue.
Supplementary Material is available at the journal web repository, and also here as follows: