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dc.contributor.authorTelesca, Donatello
dc.contributor.authorMüller, Peter
dc.contributor.authorParmigiani, Giovanni
dc.contributor.authorFreedman, Ralph
dc.date.accessioned2019-10-15T18:22:22Z
dc.date.issued2012
dc.identifier.citationTelesca, Donatello, Peter Müller, Giovanni Parmigiani, and Ralph S. Freedman. 2012. “Modeling Dependent Gene Expression.” The Annals of Applied Statistics 6 (2): 542–60. https://doi.org/10.1214/11-aoas525.
dc.identifier.issn1932-6157
dc.identifier.issn1941-7330
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41552044*
dc.description.abstractIn this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We evaluate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.
dc.language.isoen_US
dc.publisherInstitute of Mathematical Statistics
dash.licenseLAA
dc.titleModeling Dependent Gene Expression
dc.typeJournal Article
dc.description.versionVersion of Record
dc.relation.journalThe Annals of Applied Statistics
dash.depositing.authorParmigiani, Giovanni::f3245dcdd7b6792a98c81408983a7a6b::600
dc.date.available2019-10-15T18:22:22Z
dash.workflow.comments1Science Serial ID 102287
dc.identifier.doi10.1214/11-AOAS525
dash.source.volume6;2
dash.source.page542


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