Group-wise normalization in differential abundance analysis of microbiome samples

TL;DR

This paper introduces a novel group-wise normalization framework for microbiome differential abundance analysis, improving power and FDR control in challenging compositional data scenarios.

Contribution

It proposes two new normalization methods, G-RLE and FTSS, that enhance differential abundance detection accuracy over existing techniques.

Findings

G-RLE and FTSS outperform existing methods in simulations.

FTSS combined with MetagenomeSeq yields best results.

Methods maintain false discovery rate under high variance.

Abstract

A key challenge in differential abundance analysis of microbial samples is that the counts for each sample are compositional, resulting in biased comparisons of the absolute abundance across study groups. Normalization-based differential abundance analysis methods rely on external normalization factors that account for the compositionality by standardizing the counts onto a common numerical scale. However, existing normalization methods have struggled at maintaining 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 differential abundance analysis by re-conceptualizing normalization as a group-level task. We present two normalization methods within the group-wise framework: group-wise relative log expression (G-RLE) and fold-truncated sum scaling (FTSS). G-RLE and FTSS achieve higher statistical power for identifying differentially abundant taxa than existing methods in model-based and synthetic data simulation settings, while maintaining the false discovery rate in challenging scenarios where existing methods suffer. The best results are obtained from using FTSS normalization with the differential abundance analysis method MetagenomeSeq. Code for implementing the methods and replicating the analysis can be found at our GitHub page (https://github.com/dclarkboucher/microbiome_groupwise_normalization).