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Related Experiment Videos

Identifying differentially expressed genes using false discovery rate controlling procedures.

Anat Reiner1, Daniel Yekutieli, Yoav Benjamini

  • 1Department of Statistics and Operations Research, The Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv 69978, Israel. anatr@post.tau.ac.il

Bioinformatics (Oxford, England)
|February 14, 2003
PubMed
Summary
This summary is machine-generated.

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This study introduces resampling-based methods to control the false discovery rate (FDR) in large-scale gene expression analysis. These advanced techniques improve statistical power compared to standard approaches, aiding in accurate gene identification.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • DNA microarrays enable simultaneous monitoring of thousands of gene expression levels.
  • Large-scale analyses increase the probability of false positive identifications (Type I errors).
  • Gene co-regulation and measurement error dependency complicate accurate gene differential expression identification.

Purpose of the Study:

  • To address the challenge of large multiplicity in gene expression analysis.
  • To implement and evaluate resampling-based False Discovery Rate (FDR) controlling procedures.
  • To compare the performance of novel FDR methods against the standard Benjamini-Hochberg procedure.

Main Methods:

  • Adoption of the False Discovery Rate (FDR) controlling approach.

Related Experiment Videos

  • Development of three resampling-based FDR controlling procedures accounting for test statistic distributions.
  • Comparison with the naive linear step-up procedure using simulated microarray data.

    • Main Results:

    • All four evaluated FDR controlling procedures effectively controlled the FDR at the desired level.
    • Resampling methods demonstrated substantially higher statistical power than family-wise error rate controlling procedures.
    • Resampling the joint distribution of test statistics yielded the highest power, despite algorithmic complexity.

    Conclusions:

    • Resampling-based FDR control offers improved power for identifying differentially expressed genes in large datasets.
    • The choice of resampling strategy impacts performance, with joint distribution resampling being most powerful.
    • These methods provide robust tools for analyzing complex gene expression data, with available R software.