Controlling the False Discovery Rate for the Sam Method

Abstract

The Significant Analysis of Microarray (SAM) proposed by Tusher, Tibshirani and Chu (2001) is nowadays a standard statistical procedure for detecting differentially expressed genes in microarray studies. Given a threshold △ of the deviation between a t-like statistic and its empirical expectation, an estimated false discovery rate (FDR) is reported in additional to the conclusion of significance. However, the deviation between the statistic and its expectation is not easy to interpret as a conventional error measure. In practice, researchers often found the determination of the △ is quite difficult. SAM suggests to try several different △`s in the analysis and use the result which is correspondent to an adequate FDR level. In this paper, we propose a SAM-based approach, in which, instead of △, the level of per-comparisonwise error rate (PCER) is specified. The new approach involves the kernel quantile estimation method in resampling data to improve the efficiency of the sample quantiles. To control the FDR of a conclusion, the BH step-up multiple testing procedure is utilized. Simulation studies are conducted to show that the proposed approach achieves adaptive control of FDR in various settings. The proposed approach is demonstrated with a real microarray dataset.