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Related Experiment Videos

Estimation of false discovery rates in multiple testing: application to gene microarray data.

Chen-An Tsai1, Huey-miin Hsueh, James J Chen

  • 1Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, USA.

Biometrics
|February 19, 2004
PubMed
Summary
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This study models false discovery rates in gene expression analysis, proposing methods to estimate error proportions in DNA microarray experiments. A bootstrap procedure shows promise for accurate estimation, even with correlated data.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • DNA microarray experiments involve high-dimensional data with thousands of simultaneous gene comparisons.
  • Accurate assessment of false discovery rates is critical for reliable interpretation of significant findings.

Purpose of the Study:

  • To propose and evaluate models for the distribution of rejections and false rejections in gene expression analysis.
  • To introduce and compare parametric and bootstrap methods for estimating various false discovery rate measures.

Main Methods:

  • Development of probability models for the number of rejections and the conditional distribution of false rejections.
  • Derivation of distributions under independence and equicorrelated models.
  • Presentation of parametric and bootstrap procedures for False Discovery Rate (FDR) estimation.

Related Experiment Videos

  • Monte Carlo simulations to assess method performance.

    • Main Results:

    • Models for distributions of rejections and false rejections are derived under different correlation assumptions.
    • Equivalence of positive FDR, conditional FDR, and marginal FDR is shown within a Bayesian framework.
    • Bootstrap procedure demonstrates good performance, robust to correlated alternative hypotheses.

    Conclusions:

    • The proposed models provide a framework for understanding error rates in high-throughput genomic studies.
    • The bootstrap method is a reliable approach for estimating FDR in gene expression analysis.
    • Accurate FDR estimation is essential for valid conclusions from toxicogenomic microarray experiments.