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Bioinformatics Vol. 19 no. 6 2003
Pages 686-693
© 2003 Oxford University Press

Directed indices for exploring gene expression data

Michael LeBlanc 1,*, Charles Kooperberg 1, Thomas M. Grogan 2 and Thomas P. Miller 2

1 Fred Hutchinson Cancer Research Center, PO Box 19024, Seattle, WA 98109, USA
2 Arizona Cancer Center, 1515 N. Campbell Ave, Tucson, AZ 85724, USA

Received on May 25, 2002 ; revised on August 20, 2002 and November 16, 2002 ; accepted on November 29, 2002

Motivation: Large expression studies with clinical outcome data are becoming available for analysis. An important goal is to identify genes or clusters of genes where expression is related to patient outcome. While clustering methods are useful data exploration tools, they do not directly allow one to relate the expression data to clinical outcome. Alternatively, methods which rank genes based on their univariate significance do not incorporate gene function or relationships to genes that have been previously identified. In addition, after sifting through potentially thousands of genes, summary estimates (e.g. regression coefficients or error rates) algorithms should address the potentially large bias introduced by gene selection.

Results: We developed a gene index technique that generalizes methods that rank genes by their univariate associations to patient outcome. Genes are ordered based on simultaneously linking their expression both to patient outcome and to a specific gene of interest. The technique can also be used to suggest profiles of gene expression related to patient outcome. A cross-validation method is shown to be important for reducing bias due to adaptive gene selection. The methods are illustrated on a recently collected gene expression data set based on 160 patients with diffuse large cell lymphoma (DLCL).

Availability: A program written in the R language implementing the gene index can be obtained at http://www.crab.org/papers/

Contact: mikel{at}crab.org

* To whom correspondence should be addressed.


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