A Powerful Discovery Procedure for Large-Scale Significance Testing, with Application to Comparative Microarray Experiments in Response to Different Biomaterials

Abstract

Several biomaterials have been widely used in the treatment of cancer. However, how these biomaterials alter gene expression is poorly understood. The problem of identifying genes that are differentially expressed across varying biological conditions or in response to different biomaterials based on microarray data is a typical multiple testing problem. In this paper, we focus on FDR control for large-scale multiple testing problems, by our proposed statistics and resampling method, a powerful FDR controlling procedure for large-scale multiple testing problems is provided. Simulations show that, our Fiducial estimator is accurate and stable than other five traditional methods, with satisfactory FDR control. In particular, we propose a generally applicable estimate of the proposed procedure for identifying differentially expressed genes in microarray experiments. This microarray method consistently shows favorable performance over the existing methods. For example, in testing for differential expression between two breast cancer tumor types, the proposed procedure provides increases from 37% to 127% in the number of genes called significant at a false discovery rate of 3%.