Scalable fold-change estimation from microbial metagenomics
- 1University of Washington
Abstract
Microbial sequencing data are subject to sample-specific and species-specific distortions that complicate inference about true microbial abundances. Recent work established assumptions under which relative fold-differences in microbial abundances can be estimated from sequencing data, accompanied by an estimation and testing method called radEmu. While radEmu has excellent error rates, its computational cost can be prohibitive for large-scale analyses. Here we introduce fastEmu, a scalable approximation to radEmu's testing methodology. fastEmu uses a small, data-driven subset of taxa to approximate the computationally expensive test statistic. In a metagenomic analysis, we show a reduction in per-taxon runtime from 4-8 hours to 0.5-45 seconds, and simulations illustrate no adverse impact on estimation error or Type I error rate control. fastEmu enables testing differential abundance for thousands of microbial taxa while retaining clear parameter interpretation and error rate control.
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BibTeX
@misc{teichman2026scalable,
title = {{Scalable fold-change estimation from microbial metagenomics}},
author = {S. Teichman and A. D. Willis},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.08.757531},
url = {https://www.biorxiv.org/content/10.64898/2026.10.08.757531v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Scalable fold-change estimation from microbial metagenomics
AU - S. Teichman
AU - A. D. Willis
PY - 2026
DA - 2026/10/08
PB - bioRxiv
DO - 10.64898/2026.10.08.757531
UR - https://www.biorxiv.org/content/10.64898/2026.10.08.757531v1
AB - Microbial sequencing data are subject to sample-specific and species-specific distortions that complicate inference about true microbial abundances. Recent work established assumptions under which relative fold-differences in microbial abundances can be estimated from sequencing data, accompanied by an estimation and testing method called radEmu. While radEmu has excellent error rates, its computational cost can be prohibitive for large-scale analyses. Here we introduce fastEmu, a scalable approximation to radEmu's testing methodology. fastEmu uses a small, data-driven subset of taxa to approximate the computationally expensive test statistic. In a metagenomic analysis, we show a reduction in per-taxon runtime from 4-8 hours to 0.5-45 seconds, and simulations illustrate no adverse impact on estimation error or Type I error rate control. fastEmu enables testing differential abundance for thousands of microbial taxa while retaining clear parameter interpretation and error rate control.
ER -