Two-Criterion: A New Bayesian Differentially Expressed Gene Selection Algorithm

• Activity Number: 121
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Fang Yu*+ and Ming-Hui Chen and Lynn Kuo and John S. Davis
• Companies: University of Nebraska Medical Center and University of Connecticut and University of Connecticut and University of Nebraska Medical Center
• Keywords: Bayesian Method ; Microarray ; Sequencing ; Differential Expression

Abstract

Recently, the Bayesian method becomes more popular for analyzing high dimensional gene expression data as it allows us to borrow information across different genes and provides powerful estimators for evaluating gene expression levels. It is crucial to develop a simple but efficient gene selection algorithm for detecting differentially expressed(DE) genes based on the Bayesian estimators. In this paper, we extend the idea of Chen, Ibrahim, and Chi(2008) to propose a new general gene selection algorithm, namely, the two-criterion, for any Bayesian model on gene expression with microarray or sequencing data. The proposed two-criterion method first evaluates the posterior probability of a gene having different gene expressions between two competitive samples. If the obtained posterior probability is large enough, it declares the gene to be DE. The performance of the proposed method is examined and compared to those of several existing methods via several simulations. A real data set is used to further demonstrate the proposed method.