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Updated: Jun 27, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

SOME STEP-DOWN PROCEDURES CONTROLLING THE FALSE DISCOVERY RATE UNDER DEPENDENCE.

Yongchao Ge¹, Stuart C Sealfon, Terence P Speed

¹Mount Sinai School of Medicine.

Statistica Sinica

|November 20, 2008

Summary

This summary is machine-generated.

This study introduces a new step-down procedure to control the false discovery rate (FDR) in multiple testing. The eFDR method shows promise in controlling FDR and identifying false hypotheses, especially under data dependence.

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Area of Science:

Statistics
Biostatistics
Computational Biology

Background:

The false discovery rate (FDR) is a key metric in multiple hypothesis testing, offering an alternative to the familywise error rate (FWER).
Controlling FDR under various assumptions, particularly with dependent test statistics, remains an active area of statistical research.

Purpose of the Study:

To develop and evaluate a novel step-down procedure for controlling the FDR.
To incorporate dependence information into FDR control methods, building upon existing FWER procedures.

Main Methods:

Proposed a new step-down procedure with three variants: lFDR, eFDR, and hFDR.
Incorporated dependence information, similar to Westfall and Young's (1993) FWER procedure.
Conducted simulations using both independent and dependent data to assess performance.

Main Results:

The lFDR variant was found to be too optimistic; the hFDR variant was overly conservative.
The eFDR variant demonstrated effective FDR control for relevant hypotheses and suggested the number of false null hypotheses.
The hFDR procedure was theoretically proven to control FDR under subset pivotality and specific distributional assumptions.

Conclusions:

The eFDR procedure offers a practical approach for controlling FDR in the presence of dependent data.
The study provides a new tool for researchers dealing with multiple testing problems, enhancing statistical rigor.
The theoretical guarantees for hFDR under specific conditions add to the understanding of FDR control methodologies.