Microbial trend analysis for common dynamic trend, group comparison, and classification in longitudinal microbiome study

Chan Wang,Martin J Blaser,Huilin Li,Jiyuan Hu

doi:10.1186/s12864-021-07948-w

Chan Wang, Martin J Blaser + Show 2 more

Open Access

https://doi.org/10.1186/s12864-021-07948-w

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Abstract

BackgroundThe human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. With an increasing number of longitudinal microbiome studies, scientists are eager to learn the comprehensive characterization of microbial dynamics and their implications to the health and disease-related phenotypes. However, due to the challenging structure of longitudinal microbiome data, few analytic methods are available to characterize the microbial dynamics over time.ResultsWe propose a microbial trend analysis (MTA) framework for the high-dimensional and phylogenetically-based longitudinal microbiome data. In particular, MTA can perform three tasks: 1) capture the common microbial dynamic trends for a group of subjects at the community level and identify the dominant taxa; 2) examine whether or not the microbial overall dynamic trends are significantly different between groups; 3) classify an individual subject based on its longitudinal microbial profiling. Our extensive simulations demonstrate that the proposed MTA framework is robust and powerful in hypothesis testing, taxon identification, and subject classification. Our real data analyses further illustrate the utility of MTA through a longitudinal study in mice.ConclusionsThe proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.

Highlights

The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease
We explore the performance of microbial trend analysis (MTA) through the simulation study and illustrate the utility of MTA to investigate the relationship between antibiotic usage and the microbial dynamics using a longitudinal murine study
We evaluated the performance of classification in terms of receiver operating characteristic (ROC) curve and area under the curve (AUC) [33]

Summary

Introduction

The human microbiome is inherently dynamic and its dynamic nature plays a critical role in maintaining health and driving disease. Lloyd-Price et al (2019) [6] reported that periods of IBD disease activity were distinguished by increases in temporal variability of gut microbiome, with taxonomic, functional, and biochemical shifts of microbiota Such scientific results provide insights into the characterization of the microbial dynamics and raise further questions about understanding these underlying microbial dynamics as well. Several parametric methods have been proposed to elucidate the microbial dynamic changes, including mixed-effects model [14,15,16], generalized Lotka-Volterra equations [17, 18], time series models [19,20,21], and state-space models [22, 23]. Since these methods need to do the modeling or testing taxon by taxon, the large number of taxa can inevitably affect the statistical power after the multiple testing corrections, even at high taxonomic ranks

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