ZAP: z-value adaptive procedures for false discovery rate control with side information

Speaker: Dennis Leung
Affiliation: University of Melbourne

Abstract

Adaptive multiple testing with covariates is an important research direction that has gained major attention in recent years, as it has been widely recognized that leveraging side information provided by auxiliary covariates can improve the power of testing procedures for controlling the false discovery rate (FDR), e.g. in the differential expression analysis of RNA-sequencing data, the average read depths across samples can provide useful side information alongside individual p-values, and incorporating such information promises to improve the power of existing methods.  However, for two-sided hypotheses, the usual data processing step that transforms the primary statistics, generally known as z-values, into p-values not only leads to a loss of information carried by the main statistics but can also undermine the ability of the covariates to assist with the FDR inference. Motivated by this and building upon recent advances in false discovery rate research, we develop ZAP, a z-value based covariate-adaptive methodology. It operates on the intact structural information encoded jointly by the z-values and covariates, to mimic an optimal oracle testing procedure that is unattainable in practice; the power gain of ZAP can be substantial in comparison with p-value based methods.