A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies.

ABSTRACT:

Background

In microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.

Results

Our simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more tha

SUBMITTER: Wirbel J 

PROVIDER: S-EPMC11423519 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature