Empirical Bayes Identication of Tumor Progression Genes from Microarray Data

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 propose a simple procedure for the identification of such genes, which we term tumor progression genes. The first stage involves estimation based on the proportional odds model. At the second stage, we calculate two quantities: a q-value, and a shrinkage estimator of the test statistic is constructed to adjust for the multiple testing problem. The relationship between the proposed method with the false discovery rate is studied. The proposed methods are applied to data from a prostate cancer microarray study.