Semiparametric methods for identification of tumor progression genes from microarray data

Debashis Ghosh and Arul Chinnaiyan
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Debashis Ghosh: University of Michigan
Arul Chinnaiyan: University of Michigan Pathology and Urology

No 1039, The University of Michigan Department of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: The use of microarray data has become quite commonplace in medical and scientific experiments. We focus here on microarray data generated from cancer studies. It is potentially important for the discovery of biomarkers to identify genes whose expression levels correlate with tumor progression. In this article, we develop statistical procedures for the identification of such genes, which we term tumor progression genes. Two methods are considered in this paper. The first is use of a proportional odds procedure, combined with false discovery rate estimation techniques to adjust for the multiple testing problem. The second method is based on order-restricted estimation procedures. The proposed methods are applied to data from a prostate cancer study. In addition, their finite-sample properties are compared using simulated data.

Keywords: gene expression; metastasis; mixture models; multiple comparisons; prostate cancer (search for similar items in EconPapers)
Date: 2004-07-11
Note: oai:bepress.com:umichbiostat-1039
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