Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis

dc.contributor.author Ji, Tieming
dc.contributor.author Liu, Peng
dc.contributor.author Nettleton, Dan
dc.contributor.author Nettleton, Dan
dc.contributor.department Statistics
dc.date 2019-09-01T09:40:28.000
dc.date.accessioned 2020-07-02T06:57:22Z
dc.date.available 2020-07-02T06:57:22Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2012
dc.date.issued 2012-01-01
dc.description.abstract <p>Statistical inference for microarray experiments usually involves the estimation of error variance for each gene. Because the sample size available for each gene is often low, the usual unbiased estimator of the error variance can be unreliable. Shrinkage methods, including empirical Bayes approaches that borrow information across genes to produce more stable estimates, have been developed in recent years. Because the same microarray platform is often used for at least several experiments to study similar biological systems, there is an opportunity to improve variance estimation further by borrowing information not only across genes but also across experiments. We propose a lognormal model for error variances that involves random gene effects and random experiment effects. Based on the model, we develop an empirical Bayes estimator of the error variance for each combination of gene and experiment and call this estimator BAGE because information is Borrowed Across Genes and Experiments. A permutation strategy is used to make inference about the differential expression status of each gene. Simulation studies with data generated from different probability models and real microarray data show that our method outperforms existing approaches.</p>
dc.description.comments <p>This article is published as Ji, Tieming; Liu, Peng; and Nettleton, Dan (2012) "Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis," Statistical Applications in Genetics and Molecular Biology: Vol. 11: Iss. 3, Article 12. doi: <a href="https://doi.org/10.1515/1544-6115.1806">10.1515/1544-6115.1806</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/237/
dc.identifier.articleid 1228
dc.identifier.contextkey 14898024
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/237
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90551
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/237/0-Repository_Policy_for_De_Gruyter.pdf|||Fri Jan 14 22:49:27 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/237/2012_Nettleton_BorrowingInformation.pdf|||Fri Jan 14 22:49:29 UTC 2022
dc.source.uri 10.1515/1544-6115.1806
dc.subject.disciplines Genetics
dc.subject.disciplines Microarrays
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords BAGE variance estimator
dc.subject.keywords empirical Bayes
dc.subject.keywords false discovery rate
dc.subject.keywords permutation test
dc.subject.keywords shrinkage estimator
dc.title Borrowing Information Across Genes and Experiments for Improved Error Variance Estimation in Microarray Data Analysis
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 7d86677d-f28f-4ab1-8cf7-70378992f75b
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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