Evidence map›Paper›PMID 42237424›Full record

ArticleMicrobiome2026

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

Lu Yang, Jun Chen

Abstract read
In one paragraph

Article in Microbiome, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lu YangDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
Jun ChenDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA. chen.jun2@mayo.edu.

Funding

Methods for microbiome compositional dataR01GM144351 · NIGMS · MAYO CLINIC ROCHESTER · PI Jun Chen, Xianyang Zhang · 2022 to 2026
$1.6M
NIGMS NIH HHS R01 GM144351
6 · The paper itself

Abstract

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.

Indexed as

BacteriaMetagenomicsMicrobiotaSoftwareCase-Control StudiesComputer SimulationCross-Sectional StudiesHumansResearch DesignRNA, Ribosomal, 16SRNA, Ribosomal, 16SCommunity-level powerCompositional dataMicrobiome study designPower analysisSimulation-based approachTaxon-level power

Identifiers

PMID42237424
PMCPMC13450569

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.