Hierarchical tree snipping: Clustering guided by prior knowledge

Dikla Dotan-Cohen, Avraham A. Melkman, Simon Kasif

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.

Original languageEnglish
Pages (from-to)3335-3342
Number of pages8
Issue number24
StatePublished - 1 Jan 2007

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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