Gene Ontology-Based Over-Representation Analysis Using a Bayesian Approach

• Activity Number: 418
• Type: Contributed
• Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Jing Cao*+
• Companies: Southern Methodist University
• Keywords: over-representation analysis ; gene ontology ; high-throughput experiment ; Bayesian model

Abstract

In high-throughput experiments a common strategy to interpret the results is to detect a list of differentially expressed genes and then to use the knowledge of the functional characteristics of the genes as a means to gain insights into the biological mechanisms underlying the gene list. Specifically, over-representation analysis (ORA) is conducted to investigate whether gene sets associated with particular biological functions, for example as represented by Gene Ontology (GO) annotations, are over-represented in the gene list. However, the standard ORA analyzes each GO term in isolation and does not take into account the dependence structure of the GO term hierarchy. We have developed a Bayesian approach to measure over-representation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms. The Bayesian framework borrows information across related GO terms to strengthen the detection of over-representation signals. As a result, this method tends to identify biological pathways associated with subtrees of interacting GO terms rather than individual isolated GO terms.