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Sample size for FDR-control in microarray data analysis.

Sin-Ho Jung1

  • 1Department of Biostatistics and Bioinformatics, CALGB Statistical Center Hock Plaza, Suite 802,2424 Erwin Road Duke University Durham, NC 27705, USA. jung005@mc.duke.edu

Bioinformatics (Oxford, England)
|April 23, 2005
PubMed
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This study introduces a new sample size calculation method for microarray experiments to identify differentially expressing genes. It ensures a desired number of true discoveries while controlling the false discovery rate, crucial for robust gene expression analysis.

Area of Science:

  • Genomics
  • Biostatistics
  • Bioinformatics

Background:

  • Microarray experiments are vital for identifying differentially expressing genes between patient groups.
  • Accurate sample size calculation is essential for reliable results in gene expression studies.
  • Controlling the false discovery rate (FDR) is a key challenge in genomic data analysis.

Purpose of the Study:

  • To propose a novel sample size calculation method for microarray experiments.
  • To achieve a specified number of true rejections (discoveries) while maintaining a desired false discovery rate.
  • To provide a practical tool for researchers planning gene expression studies.

Main Methods:

  • The method incorporates parameters such as group allocation proportion, number of genes, number of differentially expressing genes, and effect sizes.

Related Experiment Videos

  • A closed-form solution is available when effect sizes are uniform; otherwise, a numerical method is employed.
  • Simulation studies were performed to validate the accuracy of the proposed sample size formula.

    • Main Results:

    • The developed sample size calculation method accurately determines the required sample size in practical scenarios.
    • The method effectively controls the false discovery rate at the desired level.
    • Validation through simulation studies confirms the method's reliability.

    Conclusions:

    • The proposed sample size calculation method is accurate and reliable for microarray-based gene expression studies.
    • This method aids researchers in designing experiments to maximize true discoveries while controlling false positives.
    • The approach is demonstrated with a real-world study, highlighting its practical applicability.