Nonparametric false discovery rate control for identifying simultaneous signals

TL;DR

This paper introduces a nonparametric method for detecting simultaneous signals across multiple tests without requiring null distribution knowledge, effectively controlling false discoveries and demonstrating strong empirical performance.

Contribution

It presents a novel nonparametric procedure for joint analysis of multiple test sequences, capable of asymptotic false discovery rate control without null distribution assumptions.

Findings

Asymptotic FDR control demonstrated in simulations

High power and error control in empirical tests

Successfully identified biologically relevant signals

Abstract

It is frequently of interest to jointly analyze multiple sequences of multiple tests in order to identify simultaneous signals, defined as features tested in multiple studies whose test statistics are non-null in each. In many problems, however, the null distributions of the test statistics may be complicated or even unknown, and there do not currently exist any procedures that can be employed in these cases. This paper proposes a new nonparametric procedure that can identify simultaneous signals across multiple studies even without knowing the null distributions of the test statistics. The method is shown to asymptotically control the false discovery rate, and in simulations had excellent power and error control. In an analysis of gene expression and histone acetylation patterns in the brains of mice exposed to a conspecific intruder, it identified genes that were both differentially expressed and next to differentially accessible chromatin. The proposed method is available in the R package github.com/sdzhao/ssa.