Quantifying compositional variability in microbial communities with FAVA.

Area of Science:

• Microbiology
• Bioinformatics
• Ecology

Background:

• Microbiome composition varies significantly across hosts, environments, and time.
• Existing statistical methods struggle to capture this complex variability effectively.
• Understanding microbiome variability is crucial for ecological and health-related studies.

Purpose of the Study:

• To introduce FAVA, a novel normalized measure for quantifying compositional variability in microbiome samples.
• To provide a standardized index for comparing microbiome variability across diverse datasets.
• To develop a flexible tool adaptable to taxonomic, functional, phylogenetic, and spatial/temporal data.

Main Methods:

• FAVA is defined using population-genetic statistics, treating samples as populations and taxa as alleles.
• The measure yields a single index (0-1) representing overall compositional dissimilarity.
• Extensions incorporate phylogenetic relationships and sample-specific metadata (e.g., location, time).

Main Results:

• FAVA provides commensurable variability indices across datasets with varying sample sizes and taxonomic resolutions.
• Applied to ruminant microbiomes, FAVA revealed distinct variability patterns across gastrointestinal regions.
• In human gut microbiome studies, FAVA quantified increased temporal variability post-antibiotic and its duration.

Conclusions:

• FAVA offers a robust and versatile method for assessing microbiome compositional variability.
• The tool facilitates standardized comparisons and deeper insights into microbiome dynamics.
• Implemented as an R package, FAVA integrates seamlessly into existing microbiome analysis workflows.