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Updated: Sep 13, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

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

Dylan Clark-Boucher¹, Brent A Coull², Harrison T Reeder³

¹Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA. dclarkboucher@fas.harvard.edu.

BMC Bioinformatics

|July 29, 2025

Summary

New group-level normalization methods, group-wise relative log expression (G-RLE) and fold-truncated sum scaling (FTSS), improve differential abundance analysis of microbial sequencing data by reducing bias and maintaining false discovery rates.

Keywords:

Compositional data Differential abundance analysis Microbiome Normalization

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Area of Science:

Microbiome research
Bioinformatics
Statistical analysis

Background:

Microbial sequencing data presents challenges in differential abundance analysis (DAA) due to its compositional nature, potentially biasing absolute abundance comparisons.
Existing normalization methods for DAA struggle to control the false discovery rate (FDR) under high variance or compositional bias.
Normalization is typically sample-level, but this study re-conceptualizes it as a group-level task to mitigate bias.

Purpose of the Study:

To introduce a novel group-level normalization framework for microbial sequencing data.
To develop and evaluate new normalization methods, group-wise relative log expression (G-RLE) and fold-truncated sum scaling (FTSS), within this framework.
To improve the accuracy and robustness of differential abundance analysis.

Main Methods:

Developed a group-wise normalization framework for compositional data.
Introduced two new normalization methods: G-RLE and FTSS.
Evaluated methods using model-based and synthetic data simulations, comparing performance against existing DAA normalization techniques.

Main Results:

G-RLE and FTSS demonstrated higher statistical power in identifying differentially abundant taxa compared to existing methods.
The novel methods successfully maintained the false discovery rate in challenging simulation scenarios where other methods failed.
Optimal performance was achieved using FTSS normalization in conjunction with the MetagenomeSeq DAA method.

Conclusions:

The proposed group-level normalization frameworks provide more robust statistical inference for DAA of compositional sequence count data.
These methods possess a strong mathematical basis, validated performance, and available software, enhancing rigor in microbiome research.
The novel approaches offer improvements for reproducible and reliable microbiome data analysis.