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A mixture model approach to sample size estimation in two-sample comparative microarray experiments.

Tommy S Jørstad1, Herman Midelfart, Atle M Bones

  • 1Department of Biology, Norwegian University of Science and Technology, Høgskoleringen 5, NO-7491 Trondheim, Norway. Tommy.Jorstad@bio.ntnu.no

BMC Bioinformatics
|February 27, 2008
PubMed
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Accurate sample size calculation for microarray experiments is crucial. This study introduces a novel mixture model to estimate effect size distributions, improving sample size determination for controlling the False Discovery Rate (FDR).

Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Sample size determination is critical for microarray experiment design.
  • Existing methods for False Discovery Rate (FDR) control often require effect size distribution knowledge.
  • Accurate estimation of effect sizes enables precise sample size calculations.

Purpose of the Study:

  • To present a novel mixture model for estimating effect size distributions in two-sample comparative microarray studies.
  • To develop a closed-form algorithm for estimating noncentrality parameters.
  • To demonstrate the application of the model in sample size estimation for FDR control and other statistical measures.

Main Methods:

  • A mixture model approach was developed to estimate the distribution of effect sizes.

Related Experiment Videos

  • A novel, closed-form algorithm was derived for estimating noncentrality parameters.
  • The proposed method was used to calculate sample sizes controlling FDR, average power, and false nondiscovery rate.

    • Main Results:

    • The mixture model effectively estimates effect size distributions in comparative microarray data.
    • The novel algorithm accurately estimates noncentrality parameters for differentially expressed genes.
    • Sample size calculations using the proposed method showed excellent performance compared to existing approaches.

    Conclusions:

    • A new method for estimating sample sizes in two-sample comparative microarray studies was introduced.
    • The proposed method demonstrates superior performance compared to current sample size estimation techniques.