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Quick calculation for sample size while controlling false discovery rate with application to microarray analysis.

Peng Liu1, J T Gene Hwang

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA. pliu@iastate.edu

Bioinformatics (Oxford, England)
|January 24, 2007
PubMed
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Calculating sample size for microarray and proteomic experiments is crucial. A new method controls the false discovery rate (FDR), offering a more powerful approach than traditional type I error control.

Area of Science:

  • Biostatistics
  • Genomics
  • Proteomics

Background:

  • Accurate sample size calculation is critical for experimental design, particularly in high-throughput studies like microarrays and proteomics where sample replication is limited.
  • Traditional sample size estimation methods based on Type I error control (e.g., family-wise error rate) are inadequate for multiple testing scenarios common in these experiments.
  • Controlling the false discovery rate (FDR) or positive FDR (pFDR) is a more powerful and appropriate strategy for managing errors in such analyses.

Purpose of the Study:

  • To develop and present a straightforward method for sample size calculation that specifically controls the false discovery rate (FDR).
  • To provide a computationally efficient approach for determining the necessary sample size in experiments with multiple testing.
  • To demonstrate the applicability of the proposed method to common statistical tests like t-tests and F-tests.

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Main Methods:

  • The study proposes a novel approach for sample size determination tailored to FDR control.
  • The method is illustrated using two-sample t-tests and F-tests, common in comparative omics studies.
  • The calculation is designed to be computationally simple and requires minimal resources.

Main Results:

  • The proposed method provides a direct way to calculate sample size while controlling the FDR.
  • Simulations conducted using the calculated sample sizes demonstrate that the desired statistical power is achievable with the q-value procedure.
  • The approach is validated for its effectiveness in practical experimental design.

Conclusions:

  • The developed method offers a practical and computationally efficient solution for sample size calculation in microarray and proteomic studies when controlling FDR.
  • This approach enhances the statistical rigor of experimental design in high-throughput omics research.
  • A Matlab code is available to implement these sample size calculation methods upon request.