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Bioinformatics Vol. 19 no. 9 2003
Pages 1110-1115
© 2003 Oxford University Press

Novel clustering algorithm for microarray expression data in a truncated SVD space

David Horn * and Inon Axel

School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel

Received on April 29, 2002 ; revised on November 7, 2002 ; accepted on November 8, 2002

Motivation: This paper introduces the application of a novel clustering method to microarray expression data. Its first stage involves compression of dimensions that can be achieved by applying SVD to the gene–sample matrix in microarray problems. Thus the data (samples or genes) can be represented by vectors in a truncated space of low dimensionality, 4 and 5 in the examples studied here. We find it preferable to project all vectors onto the unit sphere before applying a clustering algorithm. The clustering algorithm used here is the quantum clustering method that has one free scale parameter. Although the method is not hierarchical, it can be modified to allow hierarchy in terms of this scale parameter

Results: We apply our method to three data sets. The results are very promising. On cancer cell data we obtain a dendrogram that reflects correct groupings of cells. In an AML/ALL data set we obtain very good clustering of samples into four classes of the data. Finally, in clustering of genes in yeast cell cycle data we obtain four groups in a problem that is estimated to contain five families

Availability: Software is available as Matlab programs at http://neuron.tau.ac.il/~horn/QC.htm

Contact: horn{at}post.tau.ac.il

* To whom correspondence should be addressed.


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