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Related Experiment Videos

Sample size calculations based on ranking and selection in microarray experiments.

Shigeyuki Matsui1, Shu Zeng, Takeharu Yamanaka

  • 1Department of Pharmacoepidemiology, School of Public Health, Kyoto University, Yoshidakonoe-cho, Sakyo-ku, Kyoto 606-8501, Japan. matsui@pbh.med.kyoto-u.ac.jp

Biometrics
|August 8, 2007
PubMed
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We developed formulas for calculating sample sizes in gene expression studies to identify top-ranking genes. These methods ensure selected gene subsets reliably contain informative genes for disease subtype analysis.

Area of Science:

  • Genomics
  • Biostatistics
  • Bioinformatics

Background:

  • Genome-wide screening studies are crucial for identifying disease-related genes.
  • Selecting truly informative genes from complex datasets presents statistical challenges.
  • Accurate sample size calculation is essential for the reliability of gene selection.

Purpose of the Study:

  • To develop and present formulae for calculating sample sizes in microarray-based genome-wide screening studies.
  • To control the probability of selecting a gene subset that includes a sufficient number of top-ranking informative genes.
  • To provide strategies for robust study designs when the number of informative genes and gene correlations are unknown.

Main Methods:

  • Utilizing the distribution of ordered statistics from independent genes to assess gene informativeness.

Related Experiment Videos

  • Developing formulae for sample size calculation that incorporate desired levels of confidence in gene selection.
  • Implementing conservative design strategies to address uncertainties in the number of informative genes and inter-gene correlations.

    • Main Results:

    • Established formulae for sample size determination in gene expression ranking and selection.
    • Demonstrated methods to control the probability of including genuinely informative genes in selected subsets.
    • Provided practical strategies for designing studies with unknown parameters, enhancing robustness.

    Conclusions:

    • The developed formulae offer a statistically sound approach to sample size calculation for gene selection in disease subtype analysis.
    • The proposed strategies enhance the reliability of identifying informative genes in genome-wide studies.
    • The application to multiple myeloma data validates the utility of these methods in clinical research.