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Sample size for identifying differentially expressed genes in microarray experiments.

Sue-Jane Wang1, James J Chen

  • 1Division of Biometrics II, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, 5600 Fishers Lane, Rockville, Maryland 20857, USA. wangs@cder.fda.gov

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 8, 2004
PubMed
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Calculating sample size for microarray experiments is crucial. This study proposes a practical method to determine the number of arrays needed to identify a target percentage of truly altered genes, ensuring manageable experimental scale.

Area of Science:

  • Genomics
  • Biostatistics

Background:

  • Microarray technology enables simultaneous gene expression analysis.
  • Identifying differentially expressed genes requires careful sample size calculation.
  • Factors influencing sample size include significance level, statistical power, fraction of altered genes, and effect sizes.

Purpose of the Study:

  • To propose a method for calculating the number of arrays required in microarray experiments.
  • To determine the sample size needed to detect a specified percentage of truly altered genes.
  • To provide practical guidance for planning gene expression studies.

Main Methods:

  • Developed a sample size calculation method based on significance level (alpha), statistical power (1-beta), fraction of altered genes (eta), effect size (Delta), and detection percentage (lambda).

Related Experiment Videos

  • Tabulated required array numbers for one-sample and two-sample t-tests with 10,000 genes.
  • Utilized an example dataset to illustrate the proposed approach.

    • Main Results:

    • The proposed method provides manageable array numbers for identifying up to 90% of truly altered genes.
    • For a standardized effect size of at least 2.0, fewer than 14 arrays (two-sample t-test) or 10 arrays (one-sample t-test) are needed.
    • The method is practical, especially as the cost per array decreases.

    Conclusions:

    • The proposed method offers a simple and intuitive approach to sample size determination in microarray experiments.
    • It is suitable for experiments where the correlation structure among genes cannot be assumed.
    • This facilitates efficient planning of gene expression studies.