title: GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.
abstract: BACKGROUND
  A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy.
  
  
  RESULTS
  We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation 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 (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.
authors.affiliation: Department of Clinical Sciences, U.T. Southwestern Medical Center, 5323 Harry Hines Boulevard Dallas, TX 75390-9072, USA. song.zhang@utsouthwestern.edu
authors.forename: Song
authors.initials: S
authors.lastname: Zhang
authors.forename: Jing
authors.initials: J
authors.lastname: Cao
authors.forename: Y Megan
authors.initials: YM
authors.lastname: Kong
authors.forename: Richard H
authors.initials: RH
authors.lastname: Scheuermann
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis