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Article: Bayesian mixture models for complex high dimensional count data in phage display experiments

TitleBayesian mixture models for complex high dimensional count data in phage display experiments
Authors
KeywordsBayesian inference
Gibbs sampler
Markov chain Monte Carlo simulation
Metropolis-hastings algorithm
Peptide
Issue Date2007
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSC
Citation
Journal Of The Royal Statistical Society. Series C: Applied Statistics, 2007, v. 56 n. 2, p. 139-152 How to Cite?
AbstractPhage display is a biological process that is used to screen random peptide libraries for ligands that bind to a target of interest with high affinity. On the basis of a count data set from an innovative multistage phage display experiment, we propose a class of Bayesian mixture models to cluster peptide counts into three groups that exhibit different display patterns across stages. Among the three groups, the investigators are particularly interested in that with an ascending display pattern in the counts, which implies that the peptides are likely to bind to the target with strong affinity. We apply a Bayesian false discovery rate approach to identify the peptides with the strongest affinity within the group. A list of peptides is obtained, among which important ones with meaningful functions are further validated by biologists. To examine the performance of the Bayesian model, we conduct a simulation study and obtain desirable results. © 2007 Royal Statistical Society.
Persistent Identifierhttp://hdl.handle.net/10722/146581
ISSN
2015 Impact Factor: 1.354
2015 SCImago Journal Rankings: 1.092
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorJi, Yen_HK
dc.contributor.authorYin, Gen_HK
dc.contributor.authorTsui, KWen_HK
dc.contributor.authorKolonin, MGen_HK
dc.contributor.authorSun, Jen_HK
dc.contributor.authorArap, Wen_HK
dc.contributor.authorPasqualini, Ren_HK
dc.contributor.authorDo, KAen_HK
dc.date.accessioned2012-05-02T08:37:10Z-
dc.date.available2012-05-02T08:37:10Z-
dc.date.issued2007en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series C: Applied Statistics, 2007, v. 56 n. 2, p. 139-152en_HK
dc.identifier.issn0035-9254en_HK
dc.identifier.urihttp://hdl.handle.net/10722/146581-
dc.description.abstractPhage display is a biological process that is used to screen random peptide libraries for ligands that bind to a target of interest with high affinity. On the basis of a count data set from an innovative multistage phage display experiment, we propose a class of Bayesian mixture models to cluster peptide counts into three groups that exhibit different display patterns across stages. Among the three groups, the investigators are particularly interested in that with an ascending display pattern in the counts, which implies that the peptides are likely to bind to the target with strong affinity. We apply a Bayesian false discovery rate approach to identify the peptides with the strongest affinity within the group. A list of peptides is obtained, among which important ones with meaningful functions are further validated by biologists. To examine the performance of the Bayesian model, we conduct a simulation study and obtain desirable results. © 2007 Royal Statistical Society.en_HK
dc.languageengen_US
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSCen_HK
dc.relation.ispartofJournal of the Royal Statistical Society. Series C: Applied Statisticsen_HK
dc.subjectBayesian inferenceen_HK
dc.subjectGibbs sampleren_HK
dc.subjectMarkov chain Monte Carlo simulationen_HK
dc.subjectMetropolis-hastings algorithmen_HK
dc.subjectPeptideen_HK
dc.titleBayesian mixture models for complex high dimensional count data in phage display experimentsen_HK
dc.typeArticleen_HK
dc.identifier.emailYin, G: gyin@hku.hken_HK
dc.identifier.authorityYin, G=rp00831en_HK
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1111/j.1467-9876.2007.00570.xen_HK
dc.identifier.scopuseid_2-s2.0-33947690149en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33947690149&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume56en_HK
dc.identifier.issue2en_HK
dc.identifier.spage139en_HK
dc.identifier.epage152en_HK
dc.identifier.isiWOS:000245159600002-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridJi, Y=36570526400en_HK
dc.identifier.scopusauthoridYin, G=8725807500en_HK
dc.identifier.scopusauthoridTsui, KW=7101671569en_HK
dc.identifier.scopusauthoridKolonin, MG=6505914933en_HK
dc.identifier.scopusauthoridSun, J=9236913900en_HK
dc.identifier.scopusauthoridArap, W=7003789819en_HK
dc.identifier.scopusauthoridPasqualini, R=7004755757en_HK
dc.identifier.scopusauthoridDo, KA=7103366651en_HK
dc.identifier.citeulike1187631-

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