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Nucleic Acids Research Advance Access originally published online on August 1, 2008
Nucleic Acids Research 2008 36(17):e109; doi:10.1093/nar/gkn434
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Nucleic Acids Research, 2008, Vol. 36, No. 17 e109
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Methods Online

A probabilistic generative model for GO enrichment analysis

Yong Lu1, Roni Rosenfeld2, Itamar Simon3, Gerard J. Nau4 and Ziv Bar-Joseph1,2,*

1Computer Science Department, 2Machine Learning Department, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213 USA, 3Department of Molecular Biology, Hebrew University Medical School, Jerusalem, Israel 91120 and 4Department of Molecular Genetics and Biochemistry, University of Pittsburgh Medical School, Pittsburgh, PA 15213, USA

*To whom correspondence should be addressed. Tel: +1 412 268 8595; Fax: +1 412 268 3431; Email: zivbj{at}cs.cmu.edu

Received April 23, 2008. Revised June 23, 2008. Accepted June 23, 2008.

The Gene Ontology (GO) is extensively used to analyze all types of high-throughput experiments. However, researchers still face several challenges when using GO and other functional annotation databases. One problem is the large number of multiple hypotheses that are being tested for each study. In addition, categories often overlap with both direct parents/descendents and other distant categories in the hierarchical structure. This makes it hard to determine if the identified significant categories represent different functional outcomes or rather a redundant view of the same biological processes. To overcome these problems we developed a generative probabilistic model which identifies a (small) subset of categories that, together, explain the selected gene set. Our model accommodates noise and errors in the selected gene set and GO. Using controlled GO data our method correctly recovered most of the selected categories, leading to dramatic improvements over current methods for GO analysis. When used with microarray expression data and ChIP-chip data from yeast and human our method was able to correctly identify both general and specific enriched categories which were overlooked by other methods.


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