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Related Experiment Videos

Sample size calculation with dependence adjustment for FDR-control in microarray studies.

Yongzhao Shao1, Chi-Hong Tseng

  • 1Division of Biostatistics, NYU School of Medicine, 650 First Ave., Fifth Floor, New York, NY 10016, USA. shaoy01@med.nyu.edu

Statistics in Medicine
|March 1, 2007
PubMed
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This study presents a new method for determining sample sizes in genomic studies, accounting for multiple tests and gene co-regulation. This approach ensures sufficient statistical power while controlling false discovery rates in complex gene expression analysis.

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • DNA microarrays enable simultaneous monitoring of numerous gene expression levels.
  • Accurate statistical methods are essential for identifying differentially expressed genes, especially when accounting for multiple testing.
  • Gene co-regulation introduces dependencies among test statistics, complicating study design.

Purpose of the Study:

  • To develop a general approach for sample size calculation in two-way multiple comparisons.
  • To address the challenge of dependent test statistics in genomic studies.
  • To ensure adequate statistical power while controlling false discovery rates.

Main Methods:

  • A novel method for sample size determination is introduced.
  • The approach incorporates adjustments for multiple testing and dependencies among test statistics.

Related Experiment Videos

  • Numerical studies using simulated and real leukemia data were performed for validation.

    • Main Results:

    • The proposed method provides a robust framework for sample size calculation.
    • Demonstrated effectiveness in ensuring adequate power and controlling false discovery rates.
    • Validated using both simulated datasets and real-world gene expression data.

    Conclusions:

    • The developed method is crucial for planning large-scale genomic studies.
    • Accurate sample size determination is vital to avoid false positives and power loss.
    • This approach enhances the reliability of findings from gene expression analyses.