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<p>Power analysis is a critical step in designing a microbiome study. Existing power calculation tools for microbiome studies mainly rely on parametric models of the sequencing counts, which underestimate the complexity of microbiome data and could produce overly optimistic power estimates. In this work, we present a new simulation-based power analysis tool, <i>mPower</i>, for microbiome study design. The tool uses a real data-based semi-parametric simulation framework to generate realistic microbiome data, upon which the power assessment is performed. Coupled with a select differential analysis tool, our power tool supports different study designs, including cross-sectional, case-control, and matched-pair studies, with or without confounders. It allows power analysis for both community-level and taxon-level testing. By using microbiome reference datasets from different environments, the users could perform power calculation based on the environment of interest. The mPower is primarily designed for 16S amplicon sequencing data, and it also incorporates a parametric simulation framework that enables power analysis for shotgun metagenomic data. We showcase the application of mPower with several real-world examples. The web interface of <i>mPower</i> is available at <a href="https://microbiomestat.shinyapps.io/mPower/">https://microbiomestat.shinyapps.io/mPower/</a>.</p><p><MediaObject ID="MOESM2"><VideoObject FileRef="MediaObjects/40168_2026_2427_MOESM2_ESM.mp4" VideoID="DYSxzETYNg1VUzmTKHZMJV"><Caption Language="En" xml:lang="en"><CaptionContent><p>Video Abstract</p></CaptionContent></Caption></VideoObject></MediaObject></p>

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mPower: a real data-based power analysis tool for microbiome study design

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Lu Yang,
  • Jun Chen

摘要

Power analysis is a critical step in designing a microbiome study. Existing power calculation tools for microbiome studies mainly rely on parametric models of the sequencing counts, which underestimate the complexity of microbiome data and could produce overly optimistic power estimates. In this work, we present a new simulation-based power analysis tool, mPower, for microbiome study design. The tool uses a real data-based semi-parametric simulation framework to generate realistic microbiome data, upon which the power assessment is performed. Coupled with a select differential analysis tool, our power tool supports different study designs, including cross-sectional, case-control, and matched-pair studies, with or without confounders. It allows power analysis for both community-level and taxon-level testing. By using microbiome reference datasets from different environments, the users could perform power calculation based on the environment of interest. The mPower is primarily designed for 16S amplicon sequencing data, and it also incorporates a parametric simulation framework that enables power analysis for shotgun metagenomic data. We showcase the application of mPower with several real-world examples. The web interface of mPower is available at https://microbiomestat.shinyapps.io/mPower/.

Video Abstract