Robust and automatic definition of microbiome states

Area of Science:

• Microbiology
• Computational Biology
• Bioinformatics

Background:

• Analyzing microbiome dynamics is crucial for understanding microbial community evolution in various contexts.
• Current methods for defining microbiome states lack standardization and rigor, hindering the identification of subtle community variations.
• There is a need for objective, generalizable approaches to discern distinct microbial population configurations.

Purpose of the Study:

• To propose an automated, objective, and domain-independent procedure for defining statistically distinct microbiome states.
• To establish a robust method for identifying microbial community configurations across diverse datasets.
• To enable the use of defined microbiome states as general biomarkers.

Main Methods:

• Developed an automated, internally-validating procedure for microbiome state identification.
• Employed diverse techniques for stable cluster verification to ensure robustness.
• Applied the method to eight distinct longitudinal microbiome datasets from various ecological niches and species.

Main Results:

• Successfully detected discreet states even in datasets with highly similar bacterial communities.
• Demonstrated the algorithm's flexibility with filtered/unfiltered data and varying taxonomic levels.
• Generated a set of robustly defined microbiome states.

Conclusions:

• The proposed automated procedure provides a rigorous and generally applicable approach to microbiome state definition.
• The identified microbiome states can serve as valuable biomarkers for disease association, intervention monitoring, and population performance analysis.
• This method enhances the ability to analyze microbiome dynamics and understand microbial community evolution.