Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and...

Area of Science:

• Microbiome Research
• Computational Biology
• Host-Microbe Interactions

Background:

• Metagenomic sequencing is crucial for studying host-associated microbial communities.
• Microbiome data analysis faces challenges due to sparsity and compositionality.
• Clinical interpretation of microbiome data requires robust analytical methods.

Purpose of the Study:

• To evaluate computational and experimental approaches for microbiome data analysis.
• To benchmark analytical methods for diversity estimation, taxon associations, and taxon-metadata correlations.
• To assess method performance under varying microbial ecosystem loads and low microbial load dysbiosis.

Main Methods:

• Generated fecal metagenomes from simulated microbial communities.
• Benchmarked thirteen common analytical approaches.
• Evaluated performance based on diversity estimation, taxon-taxon associations, and taxon-metadata correlations.

Main Results:

• Quantitative approaches, including experimental ones, outperformed computational strategies.
• Quantitative methods improved true positive identification and reduced false positives.
• Methods correcting for sampling depth showed higher precision in low microbial load scenarios.

Conclusions:

• Advocating for wider adoption of experimental quantitative approaches in microbiome research.
• Suggesting preferred transformations for situations where microbial load determination is not feasible.
• Highlighting the importance of addressing microbial load variation for accurate microbiome analysis.