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Bias in the estimation of false discovery rate in microarray studies

Yudi Pawitan1, Karuturi R Krishna Murthy, Stefan Michiels

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. yudi.pawitan@meb.ki.se

Bioinformatics (Oxford, England)
|August 18, 2005
PubMed
Abstract

Insights

This study introduces a new mixture model to accurately estimate the proportion of non-differentially expressed genes (pi(0)) in microarray analysis. This improved estimation reduces bias and increases statistical power in identifying significant gene expression changes.

Area of Science:

  • Genomics
  • Statistical Bioinformatics

Background:

  • The false discovery rate (FDR) is crucial for microarray studies, but its accuracy depends on estimating pi(0), the proportion of non-differentially expressed genes.
  • Current methods often overestimate pi(0) and FDR, leading to reduced statistical power in identifying significant genes.

Purpose of the Study:

  • To develop an improved method for estimating pi(0) in two-sample microarray comparisons.
  • To address the bias in standard pi(0) estimation, particularly when pi(0) is far from 1 or test statistic non-centrality parameters are small.

Main Methods:

  • Derived a natural mixture model for the test statistic in two-sample comparisons.
  • Developed an explicit bias formula for standard pi(0) estimation.
  • Proposed a practical, likelihood-based procedure for improved pi(0) estimation using the mixture model.

Main Results:

  • Identified significant bias in standard pi(0) estimation under specific conditions.
  • Demonstrated that mixture-model estimates of pi(0) are less biased than standard estimates through simulations.
  • Explained discrepancies between non-parametric and model-based pi(0) estimates.

Conclusions:

  • The proposed mixture model and likelihood-based procedure offer a more accurate estimation of pi(0) for microarray data.
  • This improved estimation enhances the reliability of FDR calculations and increases statistical power.
  • An R-package, OCplus, is available for implementing these methods.

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