Error-rate and decision-theoretic methods of multiple testing: Which genes have high objective probabilities of differential expression?

Abstract

Given a multiple testing situation, the null hypotheses that appear to have sufficiently low probabilities of truth may be rejected using a simple, nonparametric method of decision theory. This applies not only to posterior levels of belief, but also to conditional probabilities in the sense of relative frequencies, as seen from their equality to local false discovery rates (dFDRs). This approach neither requires the estimation of probability densities, nor of their ratios. Decision theory can inform the selection of false discovery rate weights. Decision theory is applied to gene expression microarrays with discussion of the applicability of the assumption of weak dependence.