Hierarchical tree snipping: clustering guided by prior knowledge.
BACKGROUND
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.


BACKGROUND
A java package is available at http://www.cs.bgu.ac.il/~dotna/ TreeSnipping
Dotan-Cohen
Dikla
D
Department of Computer Science, Ben Gurion University, Beer Sheva 84105, Israel. dotna@cs.bgu.ac.il
Melkman
Avraham A
AA
Kasif
Simon
S
D000465:Algorithms; D001185:Artificial Intelligence; D020869:Gene Expression Profiling / Q000379:methods*; D005810:Multigene Family / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D020543:Proteome / Q000378:metabolism*
D020543:Proteome