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Robust estimation of the false discovery rate.

Stan Pounds¹, Cheng Cheng

¹Department of Biostatistics, St. Jude Children's Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA.

Bioinformatics (Oxford, England)

|June 17, 2006

Summary

A new method accurately estimates the false discovery rate (FDR) without assuming continuous p-values or two-sided tests. This robust approach performs well in simulations and is freely available for use.

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Area of Science:

Biostatistics
Statistical inference
Genomics

Background:

Current false discovery rate (FDR) estimation methods rely on assumptions of continuous p-values and two-sided tests.
These assumptions limit reliable FDR estimation with discrete p-values or one-sided tests.

Purpose of the Study:

To develop a robust and simple method for estimating the false discovery rate (FDR).
To overcome limitations of existing methods that assume continuous p-values and two-sided tests.

Main Methods:

A novel statistical approach for FDR estimation was developed.
The method's performance was evaluated through simulations and compared against existing techniques.

Main Results:

The proposed method provides a reliable estimation of the FDR.
It is robust, does not require assumptions about p-value distribution or test type, and demonstrates desirable large-sample properties.
The method performed competitively against four other approaches in real data simulations.

Conclusions:

A simple, robust, and assumption-free method for FDR estimation is presented.
This approach offers a reliable alternative for scenarios with discrete or one-sided p-values.
Software implementations are available for practical application.

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