False Discovery Control in Multiple Testing: A Brief Overview of Theories and Methodologies

TL;DR

This paper reviews recent advances in false discovery rate control methods for multiple testing, providing a conceptual framework, key ideas, and guidance for researchers in statistical methodology and applications.

Contribution

It offers a comprehensive overview of the latest FDR control methodologies, clarifies their underlying principles, and aids researchers in applying and developing these techniques.

Findings

Summarizes recent FDR control methods

Provides a conceptual framework for understanding FDR

Guides researchers in methodology application and development

Abstract

As the volume and complexity of data continue to expand across various scientific disciplines, the need for robust methods to account for the multiplicity of comparisons has grown widespread. A popular measure of type 1 error rate in multiple testing literature is the false discovery rate (FDR). The FDR provides a powerful and practical approach to large-scale multiple testing and has been successfully used in a wide range of applications. The concept of FDR has gained wide acceptance in the statistical community and various methods has been proposed to control the FDR. In this work, we review the latest developments in FDR control methodologies. We also develop a conceptual framework to better describe this vast literature; understand its intuition and key ideas; and provide guidance for the researcher interested in both the application and development of the methodology.