# Robust phylogenetic tree-based microbiome association test using repeatedly measured data for composition bias

## TL;DR

This paper introduces mTMAT, a new method for analyzing longitudinal microbiome data that is robust to compositional bias and can detect microbial associations with host diseases over time.

## Contribution

The novel contribution is mTMAT, a statistical method for longitudinal microbiome analysis that addresses compositional bias using abundance ratios and generalized estimating equations.

## Key findings

- mTMAT is robust to compositional bias and statistically powerful in simulations.
- mTMAT can detect microbial taxa associated with host diseases using repeatedly measured 16S rRNA data.
- The method enables deeper insights into bacterial pathology over time.

## Abstract

The effects of microbiota on the host phenotypes can differ substantially depending on their age. Longitudinally measured microbiome data allow for the detection of the age modification effect and are useful for the detection of microorganisms related to the progression of disease whose identification change over time. Moreover, longitudinal analysis facilitates the estimation of the within-subject covariate effect, is robust to the between-subject confounders, and provides better evidence for the causal relationship than cross-sectional studies. However, this method of analysis is limited by compositional bias, and few statistical methods can estimate the effect of microbiota on host diseases with repeatedly measured 16S rRNA gene data. Herein, we propose mTMAT, which is applicable to longitudinal microbiome data and is robust to compositional bias.

mTMAT normalized the microbial abundance and utilized the ratio of the pooled abundance for association analysis. mTMAT is based on generalized estimating equations with a robust variance estimator and can be applied to repeatedly measured microbiome data. The robustness of mTMAT against compositional bias is underscored by its utilization of abundance ratios.

With extensive simulation studies, we showed that mTMAT is statistically relatively powerful and is robust to compositional bias. mTMAT enables detection of microbial taxa associated with host diseases using repeatedly measured 16S rRNA gene data and can provide deeper insights into bacterial pathology.

The online version contains supplementary material available at 10.1186/s12859-024-06002-2.