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Practical FDR-based sample size calculations in microarray experiments.

Jianhua Hu1, Fei Zou, Fred A Wright

  • 1Department of Biostatistics and Applied Mathematics, University of Texas M.D. Anderson Cancer Center, TX 77030-4009, USA. jhu@mdanderson.org

Bioinformatics (Oxford, England)
|June 4, 2005
PubMed
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Calculating appropriate sample sizes for microarray studies is crucial. This study presents a method to determine sample size based on the False Discovery Rate (FDR) and desired number of differentially expressed genes.

Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Genomics

Background:

  • Microarray studies require careful sample size planning due to experimental costs and material acquisition challenges.
  • The False Discovery Rate (FDR) is increasingly used in microarray analysis, necessitating FDR-based sample size calculations.

Purpose of the Study:

  • To develop and present a method for calculating sample size in microarray experiments.
  • To establish a direct link between sample size, FDR, and the number of differentially expressed genes to be detected.

Main Methods:

  • Fitting parametric models for differential gene expression using the Expectation-Maximization algorithm.
  • Connecting sample size directly to the desired False Discovery Rate (FDR) and the number of differentially expressed genes.

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Main Results:

  • The proposed method's effectiveness is demonstrated through simulations and analysis of a lung microarray dataset.
  • Recommendations include utilizing a small training set or existing relevant data to inform sample size calculations.

Conclusions:

  • The developed method provides a robust approach for sample size determination in microarray studies.
  • Availability of R code facilitates the implementation of this FDR-based sample size calculation method.