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Bioinformatics Vol. 17 no. 90001 2001
Pages S107-S114
© 2001 Oxford University Press

Identifying splits with clear separation: a new class discovery method for gene expression data

Anja von Heydebreck 1, Wolfgang Huber 2, Annemarie Poustka 2 and Martin Vingron 1

1 Division of Computational Molecular Biology, Max–Planck–Institute for Molecular Genetics, Ihnestr. 73, D–14195 Berlin, Germany
2 Division of Molecular Genome Analysis, German Cancer Research Center, INF 280, D–69120 Heidelberg, Germany

Received on February 5, 2001 ; revised on April 2, 2001 ; accepted on April 2, 2001

We present a new class discovery method for microarray gene expression data. Based on a collection of gene expression profiles from different tissue samples, the method searches for binary class distinctions in the set of samples that show clear separation in the expression levels of specific subsets of genes. Several mutually independent class distinctions may be found, which is difficult to obtain from most commonly used clustering algorithms. Each class distinction can be biologically interpreted in terms of its supporting genes. The mathematical characterization of the favored class distinctions is based on statistical concepts. By analyzing three data sets from cancer gene expression studies, we demonstrate that our method is able to detect biologically relevant structures, for example cancer subtypes, in an unsupervised fashion.

Contact: heydebre{at}molgen.mpg.de


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