Abstract
Annals of Applied Statistics 2007, Vol. 1, No. 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 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.
