False Discovery Rate Computation: Illustrations and Modifications

TL;DR

This paper presents a new R package for computing and adjusting false discovery rates, emphasizing clarity between adjusted p-values and FDR estimates, and introduces improved methods for estimating the null proportion.

Contribution

It introduces a user-friendly R package with enhanced methods for FDR estimation and control, including new approaches for estimating the null proportion, supported by extensive illustrations.

Findings

The package includes various FDR estimation and control methods.

New methods for estimating the null proportion are proposed and evaluated.

The package facilitates easy visualization of FDR results.

Abstract

False discovery rates (FDR) are an essential component of statistical inference, representing the propensity for an observed result to be mistaken. FDR estimates should accompany observed results to help the user contextualize the relevance and potential impact of findings. This paper introduces a new user-friendly R package for computing FDRs and adjusting p-values for FDR control. These tools respect the critical difference between the adjusted p-value and the estimated FDR for a particular finding, which are sometimes numerically identical but are often confused in practice. Newly augmented methods for estimating the null proportion of findings - an important part of the FDR estimation procedure - are proposed and evaluated. The package is broad, encompassing a variety of methods for FDR estimation and FDR control, and includes plotting functions for easy display of results. Through extensive illustrations, we strongly encourage wider reporting of false discovery rates for observed findings.