A mathematical framework to correct for compositionality in microbiome data sets

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 taxon 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 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 taxon abundances. By providing a pathway to accurate taxon quantification, this approach holds significant potential for advancing microbiome research, particularly in clinical and environmental health applications where precise microbial profiling is critical.