Interpreting UniFrac with absolute abundance: a conceptual and practical guide

Area of Science:

• Microbial Ecology
• Bioinformatics
• Phylogenetics

Background:

• Traditional microbial diversity metrics often neglect microbial load (absolute abundance), limiting ecological interpretations.
• Existing UniFrac distances, while incorporating phylogeny, typically use relative abundance, omitting crucial abundance variations.
• Increasing accessibility of absolute abundance quantification necessitates its integration into diversity analyses.

Purpose of the Study:

• To introduce Absolute UniFrac (AU), a novel metric extending Weighted UniFrac to include absolute microbial abundances.
• To develop and evaluate the generalized extension of Absolute UniFrac (GU^A) with a tunable parameter for balancing lineage contributions.
• To demonstrate the utility of AU and GU^A in capturing microbial load, composition, and phylogenetic relationships for ecological insights.

Main Methods:

• Development of Absolute UniFrac (AU) as a variant of Weighted UniFrac incorporating absolute abundances.
• Introduction of the generalized extension GU^A with a tunable parameter [Formula: see text] to modulate diversity contributions.
• Application and benchmarking using simulations and reanalysis of four diverse 16S rRNA metabarcoding datasets.

Main Results:

• Absolute UniFrac effectively captures microbial load, community composition, and phylogenetic structure.
• AU can improve statistical power for detecting ecological shifts, sometimes correlating strongly with cell abundance differences alone.
• GU^A, while computationally intensive, shows comparable sensitivity to load estimation noise as traditional metrics like Bray-Curtis dissimilarities.

Conclusions:

• Absolute UniFrac offers a powerful, integrated approach to microbial diversity analysis by combining phylogeny, composition, and microbial load.
• This three-dimensional integration provides microbial ecologists with enhanced tools for quantitative community comparisons.
• The development represents a significant step towards more ecologically meaningful interpretations of microbial community data.