Bayesian Models for Pooling Microarray Studies with Multiple Sources of Replications

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Bayesian Models for Pooling Microarray Studies with Multiple Sources of Replications

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dc.contributor.author Conlon, Erin M
dc.contributor.author Song, Joon J
dc.contributor.author Liu, Jun
dc.date.accessioned 2010-09-30T13:48:27Z
dc.date.issued 2006
dc.identifier.citation Conlon, Erin M., Joon J. Song, and Jun S. Liu. 2006. Bayesian models for pooling microarray studies with multiple sources of replications. BMC Bioinformatics 7:247. en_US
dc.identifier.issn 1471-2105 en_US
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:4454166
dc.description.abstract Background: Biologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently. Results: We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the gene-specific posterior probability of differential expression, which provides a direct method for ranking genes, and provides Bayesian estimates of false discovery rates (FDR). In simulations combining two and five independent studies, with fixed FDR levels, we observed large increases in the number of discovered genes in pooled versus individual analyses. When the number of output genes is fixed (e.g., top 100), the pooled model found appreciably more truly differentially expressed genes than the individual studies. We were also able to identify more differentially expressed genes from pooling two independent studies in Bacillus subtilis than from each individual data set. Finally, we observed that in our simulation studies our Bayesian FDR estimates tracked the true FDRs very well. Conclusion: Our method provides a cohesive framework for combining multiple but not identical microarray studies with several sources of replication, with data produced from the same platform. We assume that each study contains only two conditions: an experimental and a control sample. We demonstrated our model's suitability for a small number of studies that have been either pre-scaled or have no outliers. en_US
dc.description.sponsorship Statistics en_US
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.isversionof doi:10.1186/1471-2105-7-247 en_US
dc.relation.hasversion http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1534062/pdf/ en_US
dash.license LAA
dc.title Bayesian Models for Pooling Microarray Studies with Multiple Sources of Replications en_US
dc.type Journal Article en_US
dc.description.version Version of Record en_US
dc.relation.journal BMC Bioinformatics en_US
dash.depositing.author Liu, Jun
dc.date.available 2010-09-30T13:48:27Z

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  • FAS Scholarly Articles [7103]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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