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FDR-controlling testing procedures and sample size determination for microarrays.

Shuying S Li1, Jeannette Bigler, Johanna W Lampe

  • 1Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA. sli@fhcrc.org

Statistics in Medicine
|June 25, 2005
PubMed
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Accurate sample size calculations for microarray studies are crucial. Existing methods may overestimate statistical power by under-controlling the false discovery rate (FDR), especially with few positive genes. A new simulation-based approach offers improved accuracy.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical genetics

Background:

  • Microarrays are widely used to detect differential gene expression.
  • Accurate sample size determination is essential for reliable microarray study results.
  • False discovery rate (FDR) is a key metric for controlling errors in these analyses.

Purpose of the Study:

  • To evaluate the performance of existing FDR-controlling procedures in microarray studies under realistic conditions.
  • To develop a more accurate method for determining sample size and statistical power in gene expression studies.

Main Methods:

  • Conducted simulation studies to assess FDR control of existing procedures.
  • Evaluated procedure performance under varying proportions of positive genes.
  • Developed a novel simulation-based method for power and sample size estimation.

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Main Results:

  • Existing FDR-controlling procedures generally control FDR but under-control it when the proportion of positive genes is small.
  • This under-control leads to overestimation of statistical power and underestimation of required sample size.
  • The proposed simulation-based method provides more accurate estimates.

Conclusions:

  • Current FDR-controlling procedures may not be reliable for sample size calculations in all microarray scenarios, particularly those with a low proportion of true positives.
  • A simulation-based approach is recommended for more precise power and sample size determination in gene expression studies.
  • This work contributes to more robust experimental design in genomics research.