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W. Harry Feinstone Center for Genomic Research [T. R. S., X-R. H., P. D., L. X., G. N., E. O. G., C. H. S.], Department of Mathematical Sciences [X-R. H., P. D., G. N., E. O. G.], University of Memphis, Memphis, Tennessee 38152; Chemoprevention Center, University of Alabama at Birmingham, Birmingham, Alabama 35294 [C. G.]; Roche, Welwyn, United Kingdom [R. S.]; F. Hoffman-La Roche AG, Basel, Switzerland [M. S-G.]; Roche Bioscience, Palo Alto, California 94304 [D. K., M. J. T.]; Division of Cancer Prevention, National Cancer Institute, Rockville, Maryland 20852 [R. L.]; and Department of Environmental Health Sciences, Johns Hopkins School of Hygiene and Public Health, Baltimore, Maryland 21205 [T. A. P., T. W. K.]

Microarray technology has greatly aided the identification of genes that are expressed differentially. Statistical analysis of such data by multiple comparisons procedures has been slow to develop, in part, because methods to cluster the results of such comparisons in biologically meaningful ways have not been available. We isolated and analyzed, by Northern blot and GeneChip, replicate liver RNA samples (n = 4/group) from rats fed with control diet or diet containing one of three chemopreventive compounds, selected because their pharmacological activities, including RNA expression response, are relatively well understood. We report on a classification tree, based on the results of nonparametric multiple comparisons, which results in the bipolar hierarchical clustering of genes in relation to their response to treatment. In addition to identifying treatment-responsive genes, application of this procedure to our test study identified the known pharmacological relationships among the treatment groups without supervision. Also, small treatment-specific subsets of genes were identified that may be indicative of additional pharmacophores present in the test compounds.