Journal Article

GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach

Song Zhang, Jing Cao, Y. Megan Kong and Richard H. Scheuermann

in Bioinformatics

Volume 26, issue 7, pages 905-911
Published in print April 2010 | ISSN: 1367-4803
Published online February 2010 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btq059
GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach

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Motivation: 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.

Contact: song.zhang@utsouthwestern.edu; richard.scheuermann@utsouthwestern.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5826 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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