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Article: Bayesian Semiparametric Model for Pathway-Based Analysis with Zero-Inflated Clinical Outcomes

TitleBayesian Semiparametric Model for Pathway-Based Analysis with Zero-Inflated Clinical Outcomes
Authors
KeywordsGaussian process
Marginal likelihood
Mixed model
Unknown link
Pathway based analysis
Issue Date2016
PublisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jabes
Citation
Journal of Agricultural, Biological, and Environmental Statistics, 2016, v. 21, p. 641-662 How to Cite?
AbstractIn this paper, we propose a semiparametric regression approach for identifying pathways related to zero-inflated clinical outcomes, where a pathway is a gene set derived from prior biological knowledge. Our approach is developed by using a Bayesian hierarchical framework. We model the pathway effect nonparametrically into a zero-inflated Poisson hierarchical regression model with an unknown link function. Nonparametric pathway effect was estimated via a kernel machine, and the unknown link function was estimated by transforming a mixture of the beta cumulative density function. Our approach provides flexible nonparametric settings to describe the complicated association between gene expressions and zero-inflated clinical outcomes. The Metropolis-within-Gibbs sampling algorithm and Bayes factor were adopted to make statistical inferences. Our simulation results support that our semiparametric approach is more accurate and flexible than zero-inflated Poisson regression with the canonical link function, which is especially true when the number of genes is large. The usefulness of our approach is demonstrated through its applications to the Canine data set from Enerson et al. (Toxicol Pathol 34:27–32, 2006). Our approach can also be applied to other settings where a large number of highly correlated predictors are present.
Persistent Identifierhttp://hdl.handle.net/10722/281773
ISSN
2023 Impact Factor: 1.4
2023 SCImago Journal Rankings: 0.526
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorCheng, L-
dc.contributor.authorKim, I-
dc.contributor.authorPang, H-
dc.date.accessioned2020-03-27T04:22:22Z-
dc.date.available2020-03-27T04:22:22Z-
dc.date.issued2016-
dc.identifier.citationJournal of Agricultural, Biological, and Environmental Statistics, 2016, v. 21, p. 641-662-
dc.identifier.issn1085-7117-
dc.identifier.urihttp://hdl.handle.net/10722/281773-
dc.description.abstractIn this paper, we propose a semiparametric regression approach for identifying pathways related to zero-inflated clinical outcomes, where a pathway is a gene set derived from prior biological knowledge. Our approach is developed by using a Bayesian hierarchical framework. We model the pathway effect nonparametrically into a zero-inflated Poisson hierarchical regression model with an unknown link function. Nonparametric pathway effect was estimated via a kernel machine, and the unknown link function was estimated by transforming a mixture of the beta cumulative density function. Our approach provides flexible nonparametric settings to describe the complicated association between gene expressions and zero-inflated clinical outcomes. The Metropolis-within-Gibbs sampling algorithm and Bayes factor were adopted to make statistical inferences. Our simulation results support that our semiparametric approach is more accurate and flexible than zero-inflated Poisson regression with the canonical link function, which is especially true when the number of genes is large. The usefulness of our approach is demonstrated through its applications to the Canine data set from Enerson et al. (Toxicol Pathol 34:27–32, 2006). Our approach can also be applied to other settings where a large number of highly correlated predictors are present.-
dc.languageeng-
dc.publisherAmerican Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jabes-
dc.relation.ispartofJournal of Agricultural, Biological, and Environmental Statistics-
dc.subjectGaussian process-
dc.subjectMarginal likelihood-
dc.subjectMixed model-
dc.subjectUnknown link-
dc.subjectPathway based analysis-
dc.titleBayesian Semiparametric Model for Pathway-Based Analysis with Zero-Inflated Clinical Outcomes-
dc.typeArticle-
dc.identifier.emailPang, H: herbpang@hku.hk-
dc.identifier.authorityPang, H=rp01857-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s13253-016-0264-3-
dc.identifier.scopuseid_2-s2.0-84986253502-
dc.identifier.hkuros309589-
dc.identifier.volume21-
dc.identifier.spage641-
dc.identifier.epage662-
dc.identifier.isiWOS:000389464800003-
dc.publisher.placeUnited States-
dc.identifier.issnl1085-7117-

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