Group-wise normalization in differential abundance analysis of microbiome samples.

ABSTRACT:

Background

A key challenge in differential abundance analysis (DAA) of microbial sequencing data is that the counts for each sample are compositional, resulting in potentially biased comparisons of the absolute abundance across study groups. Normalization-based DAA methods rely on external normalization factors that account for compositionality by standardizing the counts onto a common numerical scale. However, existing normalization methods have struggled to maintain the false discovery rate in settings where the variance or compositional bias is large. This article proposes a novel framework for normalization that can reduce bias in DAA by re-conceptualizing normalization as a group-level task. We present two new normalization methods within the group-wise framework: group-wise r

SUBMITTER: Clark-Boucher D 

PROVIDER: S-EPMC12308967 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature