Bayesian Analysis of Gene Expression Levels: Statistical Quantification of Relative mRNA Level across Multiple Strains or Treatments

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Bayesian Analysis of Gene Expression Levels: Statistical Quantification of Relative mRNA Level across Multiple Strains or Treatments

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dc.contributor.author Townsend, Jeffrey P
dc.contributor.author Hartl, Daniel L.
dc.date.accessioned 2010-10-05T14:42:24Z
dc.date.issued 2002
dc.identifier.citation Townsend, Jeffrey P. and Daniel L. Hartl. 2002. Bayesian analysis of gene expression levels: statistical quantification of relative mRNA level across multiple strains or treatments. Genome Biology 3(12): research0071.1-research0071.16. en_US
dc.identifier.issn 1465-6906 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4457588
dc.description.abstract Background: Methods of microarray analysis that suit experimentalists using the technology are vital. Many methodologies discard the quantitative results inherent in cDNA microarray comparisons or cannot be flexibly applied to multifactorial experimental design. Here we present a flexible, quantitative Bayesian framework. This framework can be used to analyze normalized microarray data acquired by any replicated experimental design in which any number of treatments, genotypes, or developmental states are studied using a continuous chain of comparisons. Results: We apply this method to Saccharomyces cerevisiae microarray datasets on the transcriptional response to ethanol shock, to SNF2 and SWI1 deletion in rich and minimal media, and to wild-type and zap1 expression in media with high, medium, and low levels of zinc. The method is highly robust to missing data, and yields estimates of the magnitude of expression differences and experimental error variances on a per-gene basis. It reveals genes of interest that are differentially expressed at below the twofold level, genes with high 'fold-change' that are not statistically significantly different, and genes differentially regulated in quantitatively unanticipated ways. Conclusions: Anyone with replicated normalized cDNA microarray ratio datasets can use the freely available MacOS and Windows software, which yields increased biological insight by taking advantage of replication to discern important changes in expression level both above and below a twofold threshold. Not only does the method have utility at the moment, but also, within the Bayesian framework, there will be considerable opportunity for future development. en_US
dc.description.sponsorship Organismic and Evolutionary Biology en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof http://genomebiology.com/2002/3/12/research/0071 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC151173/pdf/ en_US
dash.license LAA
dc.title Bayesian Analysis of Gene Expression Levels: Statistical Quantification of Relative mRNA Level across Multiple Strains or Treatments en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal Genome Biology en_US
dash.depositing.author Hartl, Daniel L.
dc.date.available 2010-10-05T14:42:24Z

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  • FAS Scholarly Articles [7078]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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