Abstract

Multiple testing has become an integral component in genomic analyses involving microarray experiments where a large number of hypotheses are tested simultaneously. However, before applying more computationally intensive methods, it is often desirable to complete an initial truncation of the variable set using a simpler and faster supervised method such as univariate regression. Once such a truncation is completed, multiple testing methods applied to any subsequent analysis no longer control the appropriate Type I error rates. Here we propose a modified marginal Benjamini & Hochberg step-up FDR controlling procedure for multi-stage analyses (FDR-MSA), which correctly controls Type I error in terms of the entire variable set when only a subset of the initial set of variables is tested. The method is presented with respect to a variable importance application. As the initial subset size increases, we observe convergence to the standard Benjamini & Hochberg step-up FDR controlling multiple testing procedures. We demonstrate the power and Type I error control through simulation and application to the Golub Leukemia data from 1999.

Submitted: July 21, 2008 · Accepted: December 23, 2008 · Published: February 4, 2009

Recommended Citation

Tuglus, Catherine and van der Laan, Mark J. (2009) "Modified FDR Controlling Procedure for Multi-Stage Analyses," Statistical Applications in Genetics and Molecular Biology: Vol. 8 : Iss. 1, Article 12.
DOI: 10.2202/1544-6115.1397
Available at: http://www.bepress.com/sagmb/vol8/iss1/art12