Bioinformatics Advance Access originally published online on November 7, 2007
Bioinformatics 2007 23(24):3335-3342; doi:10.1093/bioinformatics/btm526
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Hierarchical tree snipping: clustering guided by prior knowledge
1Department of Computer Science, Ben Gurion University, Beer Sheva 84105, Israel, 2Department of Biomedical Engineering, 3Center for Advanced Genomic Technology, 4Bioinformatics Program and 5Children's Hospital Boston, Harvard/MIT Program in Health Sciences and Technology, 300 Longwood Avenue, Boston, MA 02115, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: Hierarchical clustering is widely used to cluster genes into groups based on their expression similarity. This method first constructs a tree. Next this tree is partitioned into subtrees by cutting all edges at some level, thereby inducing a clustering. Unfortunately, the resulting clusters often do not exhibit significant functional coherence.
Results: To improve the biological significance of the clustering, we develop a new framework of partitioning by snipping—cutting selected edges at variable levels. The snipped edges are selected to induce clusters that are maximally consistent with partially available background knowledge such as functional classifications. Algorithms for two key applications are presented: functional prediction of genes, and discovery of functionally enriched clusters of co-expressed genes. Simulation results and cross-validation tests indicate that the algorithms perform well even when the actual number of clusters differs considerably from the requested number. Performance is improved compared with a previously proposed algorithm.
Availability: A java package is available at http://www.cs.bgu.ac.il/~dotna/ TreeSnipping
Contact: dotna{at}cs.bgu.ac.il
Supplementary information: Supplementary data are available at Bioinformatics online.
Received on April 22, 2007; revised on October 9, 2007; accepted on October 15, 2007
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