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dc.creatorAiroldi, EM
dc.creatorWang, X
dc.creatorLin, X
dc.date.accessioned2021-02-03T20:02:45Z
dc.date.available2021-02-03T20:02:45Z
dc.date.issued2013-12-01
dc.identifier.issn1932-6157
dc.identifier.issn1941-7330
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5909
dc.identifier.other294KP (isidoc)
dc.identifier.other24587846 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5927
dc.description.abstractWe consider the problem of quantifying temporal coordination between multiple high-dimensional responses. We introduce a family of multi-way stochastic blockmodels suited for this problem, which avoids preprocessing steps such as binning and thresholding commonly adopted for this type of data, in biology. We develop two inference procedures based on collapsed Gibbs sampling and variational methods.We provide a thorough evaluation of the proposed methods on simulated data, in terms of membership and blockmodel estimation, predictions out-of-sample and run-time. We also quantify the effects of censoring procedures such as binning and thresholding on the estimation tasks. We use these models to carry out an empirical analysis of the functional mechanisms driving the coordination between gene expression and metabolite concentrations during carbon and nitrogen starvation, in S. cerevisiae. © Institute of Mathematical Statistics, 2013.
dc.format.extent2431-2457
dc.language.isoen
dc.relation.haspartAnnals of Applied Statistics
dc.relation.isreferencedbyInstitute of Mathematical Statistics
dc.subjectHigh dimensional data
dc.subjectvariational inference
dc.subjectmolecular biology
dc.subjectyeast
dc.titleMulti-way blockmodels for analyzing coordinated high-dimensional responses
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.1214/13-AOAS643
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidAiroldi, Edoardo|0000-0002-3512-0542
dc.date.updated2021-02-03T20:02:42Z
refterms.dateFOA2021-02-03T20:02:45Z


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