Over the past 10 years, the development of microarray biotechnology has become a popular and powerful tool in understanding biological processes at a molecular level [1]. Microarray technology allows for the simultaneous study of thousands of genes in an automated setting. These highthroughput assays are critical in a number of biomedical applications such as cancer classi cation [2], disease diagnosis [3], and drug discovery [4]. At the same time, there has been a call for innovation in the statistical science discipline to develop a new methodology to analyze these complex and high-dimensional genomic assays. Several statistical areas where we have witnessed increased

development include multiple comparisons and testing [5,6], high-dimensional data analyses [7], machine-learning techniques [8], Bayesian methods [9,10], and the design of experiments [11].