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Computing Power and Sample Size for the False Discovery Rate in Multiple Applications.

Yonghui Ni1, Anna Eames Seffernick1, Arzu Onar-Thomas1

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.

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Summary
This summary is machine-generated.

Researchers developed a new method using a p-value histogram approximation to calculate statistical power and sample size for genomic analyses employing the false discovery rate (FDR). An R package, FDRsamplesize2, is now available for broader study applications.

Keywords:
false discovery ratemultiple testingpowerproportion of true null hypothesessample size

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Area of Science:

  • Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • The false discovery rate (FDR) is a critical metric for assessing statistical significance in genomic analyses with multiple hypothesis testing.
  • Adequate statistical power and appropriate sample size are essential for the successful planning and execution of such studies.

Purpose of the Study:

  • To derive a novel formula for computing statistical power and sample size specifically for analyses utilizing the FDR.
  • To introduce the R package FDRsamplesize2, enhancing the toolkit for power and sample size calculations in genomic research.

Main Methods:

  • A three-rectangle approximation of a p-value histogram was employed to derive the power and sample size formula.
  • The R package FDRsamplesize2 was developed, integrating the new formula and existing methods for comprehensive power calculations.

Main Results:

  • A new formula for calculating statistical power and sample size in FDR-controlled genomic analyses was successfully derived.
  • The FDRsamplesize2 R package offers advanced power calculation capabilities for diverse study designs, extending beyond current software limitations.

Conclusions:

  • The proposed method and FDRsamplesize2 package provide valuable tools for researchers conducting genomic data analyses.
  • These advancements facilitate more robust study designs and reliable statistical inference in the era of big data in genomics.