Two-Layer Bayesian Variable Selection with Group and Network Information

• Activity Number: 673
• Type: Contributed
• Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Francesco Claudio Stingo*+
• Companies: Rice University
• Keywords: Bayesian variable selection ; gene expression ; Markov chain Monte Carlo ; Markov random field prior ; pathway selection

Abstract

The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. These information are used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model, taking into account a set of constraints for identifiability and biological interpretation.We illustrate the method with an application to gene expression data with censored survival outcomes.In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.