A probabilistic generative model for GO enrichment analysis.

Yong Lu*, Roni Rosenfeld, Itamar Simon, Gerard J. Nau, Ziv Bar-Joseph

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

53 Scopus citations


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.

Original languageAmerican English
Pages (from-to)e109
JournalNucleic Acids Research
Issue number17
StatePublished - Oct 2008
Externally publishedYes

Bibliographical note

Funding Information:
Funding for research and funding to pay the open access publication charges for this article were supported by NIH grant NO1 AI-500 and NSF CAREER award 0448453 to Z.B.-J.


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