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.

By downloading these Materials, I agree to the following:

1. I will not use the Materials to
   1. obtain information that could directly or indirectly identify subjects.
   2. produce links among the Distributor's datasets or among the Distributor's data and other datasets that could identify individuals or organizations.
   3. obtain information about, or further contact with, subjects known to me except where the use and/or release of such identifying information has no potential for constituting an unwarranted invasion of privacy and/or breach of confidentiality.
2. I agree not to download any Materials where prohibited by applicable law.
3. I agree not to use the Materials in any way prohibited by applicable law.
4. I agree that any books, articles, conference papers, theses, dissertations, reports, or other publications that I create which employ data reference the bibliographic citation accompanying this data. These citations include the data authors, data identifier, and other information accord with the Recommended Standard (http://thedata.org/citation/standard) for social science data.
5. THE DISTRIBUTOR MAKES NO WARRANTIES, EXPRESS OR IMPLIED, BY OPERATION OF LAW OR OTHERWISE, REGARDING OR RELATING TO THE DATASET

BY CLICKING THE "I AGREE" CHECKBOX BELOW, I CONFIRM THAT I HAVE READ AND UNDERSTOOD EACH AND EVERY TERM SET FORTH IN THE TERMS AND CONDITIONS FOR THE USE OF DATA FOUND ABOVE, AND I AGREE TO BE BOUND BY ALL OF SUCH TERMS AND CONDITIONS.

IF I DO NOT UNDERSTAND OR AGREE TO ALL OF THE TERMS AND CONDITIONS, I MUST NOT DOWNLOAD THE MATERIALS.