Replication data for: Of Mice and Men: Sparse Statistical Modeling in Cardiovascular Genomics (doi:10.7910/DVN/OSQPDV)

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Replication data for: Of Mice and Men: Sparse Statistical Modeling in Cardiovascular Genomics

doi:10.7910/DVN/OSQPDV

Harvard Dataverse

2007-11-28

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David M. Seo; Pascal J. Goldschmidt-Clermont; and Mike West, 2007, "Replication data for: Of Mice and Men: Sparse Statistical Modeling in Cardiovascular Genomics", https://doi.org/10.7910/DVN/OSQPDV, Harvard Dataverse, V1

Replication data for: Of Mice and Men: Sparse Statistical Modeling in Cardiovascular Genomics

doi:10.7910/DVN/OSQPDV

David M. Seo (Duke University)

Pascal J. Goldschmidt-Clermont (University of Miami)

and Mike West (Duke University)

2007

Harvard Dataverse

Institute for Mathematical Statistics

2007-10-01

2007

https://doi.org/10.7910/DVN/OSQPDV

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

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.

Subject: STANDARD DEPOSIT TERMS 1.0 Type: DATAPASS:TERMS:STANDARD:1.0 Notes: This study was deposited under the of the Data-PASS standard deposit terms. A copy of the usage agreement is included in the file section of this study.;

<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>

David M. Seo, Pascal J. Goldschmidt-Clermont and Mike West. 2007. "Of Mice and Men: Sparse Statistical Modeling in Cardiovascular Genomics." Ann. Appl. Statist. Volume 1, Number 1 (2007), 152-178. <a href="http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?view=body&amp;id=pdfview_1&amp;handle=euclid.aoas/1183143733" target= "_new">article available here</a>

David M. Seo, Pascal J. Goldschmidt-Clermont and Mike West. 2007. "Of Mice and Men: Sparse Statistical Modeling in Cardiovascular Genomics." Ann. Appl. Statist. Volume 1, Number 1 (2007), 152-178. <a href="http://projecteuclid.org/DPubS/Repository/1.0/Disseminate?view=body&amp;id=pdfview_1&amp;handle=euclid.aoas/1183143733" target= "_new">article available here</a>

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Human.factorlanalysis.info.zip

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Zip file containing raw data and model parameters files for the human factor analyses

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mice-anovaregn-info.zip

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Zip file containing raw data and model parameters files for the mice factor analyses

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supplement3.pdf

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SPARSE STATISTICAL MODELLING IN CARDIOVASCULAR GENOMICS — SUPPLEMENTARY FIGURES AND TABLES

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