A mathematical framework to correct for compositionality in microbiome datasets

Published

January 6, 2026

Author(s)

Samuel Forry, Stephanie Servetas, Jason Kralj, Monique Hunter, Jennifer Dootz

Abstract

The increasing use of metagenomic sequencing (MGS) for microbiome analysis has significantly advanced our understanding of microbial communities and their roles in various biological processes, including human health, environmental cycling, and disease. However, the inherent compositionality of MGS data, where the relative abundance of each taxa depends on the abundance of all other taxa, complicates the measurement of individual taxa and the interpretation of microbiome data. Here we describe an experimental design that incorporates exogenous internal standards in routine MGS analyses to correct for compositional distortions. A mathematical framework was developed for using the observed internal standard relative abundance to calculate 'Scaled Abundances' for native taxa that were (i) independent of sample composition and (ii) directly proportional to actual biological abundances. Through rigorous analysis of mock community and human gut microbiome samples, we demonstrate that Scaled Abundances outperformed traditional relative abundance measurements in both precision and accuracy and enabled reliable, quantitative comparisons of individual microbiome taxa across varied sample compositions and across a wide range of taxa abundances. By providing a pathway to accurate taxa quantification, this approach holds significant potential for advancing microbiome research, particularly in clinical and environmental health applications where precise microbial profiling is critical.

Citation

Applied and Environmental Microbiology

Pub Type

Journals

Download Paper

https://doi.org/10.1128/aem.01126-25

Local Download

Keywords

Compositionality, scaled abundance, internal standard, experimental design, metagenomic sequencing

Citation

Forry, S. , Servetas, S. , Kralj, J. , Hunter, M. and Dootz, J. (2026), A mathematical framework to correct for compositionality in microbiome datasets, Applied and Environmental Microbiology, [online], https://doi.org/10.1128/aem.01126-25, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959770 (Accessed May 21, 2026)

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