Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies |
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| Authors: | Bo Chen Minhua Chen John Paisley Aimee Zaas Christopher Woods Geoffrey S Ginsburg III" target="_blank">Alfred HeroIII Joseph Lucas David Dunson Lawrence Carin |
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| Institution: | 1.Electrical and Computer Engineering Department,Duke University,Durham,USA;2.Institute for Genome Sciences & Policy,Department of Medicine Duke University,Durham,USA;3.Electrical & Computer Engineering Department,University of Michigan,Ann Arbor,USA;4.Statistics Department,Duke University,Durham,USA |
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| Abstract: | Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one
to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis,
with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the
Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of
factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian
(VB) analysis. |
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