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

Background The 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. Results We 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. Conclusions The proposed MTA framework is an attractive and effective tool in investigating dynamic microbial pattern from longitudinal microbiome studies.

Automated summary

In this study, a microbial trend analysis (MTA) framework was proposed for high-dimensional and phylogenetically-based longitudinal microbiome data. MTA can capture common microbial dynamic trends at the community level, examine differences in microbial dynamic trends between groups, and classify individual subjects based on their longitudinal microbial profiles. The study showed that the MTA framework is robust and effective in hypothesis testing, taxon identification, and subject classification.