Bayesian Hierarchical Latent Model for Gene Set Analysis

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dc.contributor.authorChao, Yien
dc.contributor.committeechairKim, Inyoungen
dc.contributor.committeememberLeman, Scotland C.en
dc.contributor.committeememberBirch, Jeffrey B.en
dc.contributor.departmentStatisticsen
dc.date.accessioned2014-03-14T20:34:39Zen
dc.date.adate2009-05-13en
dc.date.available2014-03-14T20:34:39Zen
dc.date.issued2009-04-29en
dc.date.rdate2012-06-22en
dc.date.sdate2009-04-30en
dc.description.abstractPathway is a set of genes which are predefined and serve a particular celluar or physiological function. Ranking pathways relevant to a particular phenotype can help researchers focus on a few sets of genes in pathways. In this thesis, a Bayesian hierarchical latent model was proposed using generalized linear random effects model. The advantage of the approach was that it can easily incorporate prior knowledges when the sample size was small and the number of genes was large. For the covariance matrix of a set of random variables, two Gaussian random processes were considered to construct the dependencies among genes in a pathway. One was based on the polynomial kernel and the other was based on the Gaussian kernel. Then these two kernels were compared with constant covariance matrix of the random effect by using the ratio, which was based on the joint posterior distribution with respect to each model. For mixture models, log-likelihood values were computed at different values of the mixture proportion, compared among mixtures of selected kernels and point-mass density (or constant covariance matrix). The approach was applied to a data set (Mootha et al., 2003) containing the expression profiles of type II diabetes where the motivation was to identify pathways that can discriminate between normal patients and patients with type II diabetes.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-04302009-134407en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04302009-134407/en
dc.identifier.urihttp://hdl.handle.net/10919/32060en
dc.publisherVirginia Techen
dc.relation.haspartYiChaoThesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectPathway based analysisen
dc.subjectPoint-mass densityen
dc.subjectProbit regression modelen
dc.subjectBayesian hierarchical modelen
dc.subjectLatent variableen
dc.subjectGeneralized linear mixed modelen
dc.titleBayesian Hierarchical Latent Model for Gene Set Analysisen
dc.typeThesisen
thesis.degree.disciplineStatisticsen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen
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