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Article: Bayesian Hierarchical Modeling and Selection of Differentially Expressed Genes for the EST Data

TitleBayesian Hierarchical Modeling and Selection of Differentially Expressed Genes for the EST Data
Authors
KeywordsDirichlet distribution
Gene expression
Mixture distributions
Multinomial distribution
Shrinkage estimators
Issue Date2011
PublisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOM
Citation
Biometrics, 2011, v. 67 n. 1, p. 142-150 How to Cite?
AbstractExpressed sequence tag (EST) sequencing is a one-pass sequencing reading of cloned cDNAs derived from a certain tissue. The frequency of unique tags among different unbiased cDNA libraries is used to infer the relative expression level of each tag. In this article, we propose a hierarchical multinomial model with a nonlinear Dirichlet prior for the EST data with multiple libraries and multiple types of tissues. A novel hierarchical prior is developed and the properties of the proposed prior are examined. An efficient Markov chain Monte Carlo algorithm is developed for carrying out the posterior computation. We also propose a new selection criterion for detecting which genes are differentially expressed between two tissue types. Our new method with the new gene selection criterion is demonstrated via several simulations to have low false negative and false positive rates. A real EST data set is used to motivate and illustrate the proposed method. © 2010, The International Biometric Society.
Persistent Identifierhttp://hdl.handle.net/10722/129105
ISSN
2015 Impact Factor: 1.36
2015 SCImago Journal Rankings: 1.906
ISI Accession Number ID
Funding AgencyGrant Number
NIHGM 70335
CA 74015
GM 5764-01
Funding Information:

The authors wish to thank the editor, the associate editor, and the two referees for their helpful comments and suggestions, which have led to a considerable improvement of this article. Dr Chen's research was partially supported by NIH grants GM 70335 and CA 74015 and Dr Kuo's research was partially supported by NIH grant GM 5764-01.

References

 

DC FieldValueLanguage
dc.contributor.authorYu, Fen_HK
dc.contributor.authorChen, MHen_HK
dc.contributor.authorKuo, Len_HK
dc.contributor.authorHuang, Pen_HK
dc.contributor.authorYang, Wen_HK
dc.date.accessioned2010-12-23T08:32:33Z-
dc.date.available2010-12-23T08:32:33Z-
dc.date.issued2011en_HK
dc.identifier.citationBiometrics, 2011, v. 67 n. 1, p. 142-150en_HK
dc.identifier.issn0006-341Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/129105-
dc.description.abstractExpressed sequence tag (EST) sequencing is a one-pass sequencing reading of cloned cDNAs derived from a certain tissue. The frequency of unique tags among different unbiased cDNA libraries is used to infer the relative expression level of each tag. In this article, we propose a hierarchical multinomial model with a nonlinear Dirichlet prior for the EST data with multiple libraries and multiple types of tissues. A novel hierarchical prior is developed and the properties of the proposed prior are examined. An efficient Markov chain Monte Carlo algorithm is developed for carrying out the posterior computation. We also propose a new selection criterion for detecting which genes are differentially expressed between two tissue types. Our new method with the new gene selection criterion is demonstrated via several simulations to have low false negative and false positive rates. A real EST data set is used to motivate and illustrate the proposed method. © 2010, The International Biometric Society.en_HK
dc.languageengen_US
dc.publisherBlackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/BIOMen_HK
dc.relation.ispartofBiometricsen_HK
dc.rightsThe definitive version is available at www.blackwell-synergy.com-
dc.subjectDirichlet distributionen_HK
dc.subjectGene expressionen_HK
dc.subjectMixture distributionsen_HK
dc.subjectMultinomial distributionen_HK
dc.subjectShrinkage estimatorsen_HK
dc.subject.meshAlgorithms-
dc.subject.meshBayes Theorem-
dc.subject.meshEscherichia coli Proteins - genetics-
dc.subject.meshGene Expression Profiling - methods-
dc.subject.meshModels, Statistical-
dc.titleBayesian Hierarchical Modeling and Selection of Differentially Expressed Genes for the EST Dataen_HK
dc.typeArticleen_HK
dc.identifier.emailYang, W:yangwl@hkucc.hku.hken_HK
dc.identifier.authorityYang, W=rp00524en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1541-0420.2010.01447.xen_HK
dc.identifier.pmid20560937en_HK
dc.identifier.scopuseid_2-s2.0-79952600800en_HK
dc.identifier.hkuros177455en_US
dc.identifier.hkuros196022-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952600800&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume67en_HK
dc.identifier.issue1en_HK
dc.identifier.spage142en_HK
dc.identifier.epage150en_HK
dc.identifier.isiWOS:000288386800016-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridYu, F=36060789600en_HK
dc.identifier.scopusauthoridChen, MH=34771142800en_HK
dc.identifier.scopusauthoridKuo, L=7101601988en_HK
dc.identifier.scopusauthoridHuang, P=44161192500en_HK
dc.identifier.scopusauthoridYang, W=23101349500en_HK
dc.identifier.citeulike9026427-

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