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Bioinformatics Advance Access originally published online on June 3, 2009
Bioinformatics 2009 25(14):1789-1795; doi:10.1093/bioinformatics/btp327
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© 2009 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.

Seeing the forest for the trees: using the Gene Ontology to restructure hierarchical clustering

Dikla Dotan-Cohen 1,*, Simon Kasif 2,3,4,5 and Avraham A. Melkman 1

1 Department of Computer Science, Ben-Gurion University, Beer Sheva, Israel 84105, 2 Department of Biomedical Engineering, 3 Center for Advanced Genomic Technology, 4 Bioinformatics Program, Boston University, MA 02215 and 5 Children's Hospital Boston, Harvard/MIT Program in Health Sciences and Technology, 300 Longwood Avenue, Boston, MA 02115, USA

* To whom correspondence should be addressed.


  Abstract

Motivation: There is a growing interest in improving the cluster analysis of expression data by incorporating into it prior knowledge, such as the Gene Ontology (GO) annotations of genes, in order to improve the biological relevance of the clusters that are subjected to subsequent scrutiny. The structure of the GO is another source of background knowledge that can be exploited through the use of semantic similarity.

Results: We propose here a novel algorithm that integrates semantic similarities (derived from the ontology structure) into the procedure of deriving clusters from the dendrogram constructed during expression-based hierarchical clustering. Our approach can handle the multiple annotations, from different levels of the GO hierarchy, which most genes have. Moreover, it treats annotated and unannotated genes in a uniform manner. Consequently, the clusters obtained by our algorithm are characterized by significantly enriched annotations. In both cross-validation tests and when using an external index such as protein–protein interactions, our algorithm performs better than previous approaches. When applied to human cancer expression data, our algorithm identifies, among others, clusters of genes related to immune response and glucose metabolism. These clusters are also supported by protein–protein interaction data.

Contact: dotna{at}cs.bgu.ac.il

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: David Rocke

Received on December 7, 2008; revised on April 28, 2009; accepted on May 15, 2009

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