Bayesian Inference of the Number of Factors in Gene-Expression Analysis: Application to Human Virus Challenge Studies

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. Results: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between seve...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Bo Chen, Minhua Chen, John William Paisley, Aimee Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Alfred Hero, Joseph Lucas, David B. Dunson, Lawrence Carin
• Keywords: BMCBI 2010, Business, Challenge Studies, Factor Analysis, Gene-expression Data