Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
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While metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.
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Changes in the relative abundance of a microbial taxon are inherently compensated by equivalent increases or decreases in the remaining taxa, resulting in correlation biases that adversely affect downstream analysis.
“The changes in the relative abundance of a microbial taxon are inherently compensated by equivalent increases or decreases in the remaining taxa, resulting in correlation biases that adversely affect downstream analysis.719”
From Microbiota–gut–brain axis and its therapeutic applications in neurodegenerative diseases (2024) · cites this paper as [719]
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- Authors
- Lloréns-Rico, V., Vieira-Silva, S., Gonçalves, P. J., Falony, G., & Raes, J.
- Affiliations
- Center of Advanced European Studies and Research (DE)
- Rega Institute for Medical Research (BE)
- VIB-KU Leuven Center for Microbiology (BE)
- Corresponding author
- Raes, Jeroen
- Venue
- Nature Communications
- Published
- 2021-06-11
- Volume
- 12
- Issue
- 1
- Pages
- 3562-3562
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8196019
- OpenAlex
- W3171380437
- Cited by
- 73
- References
- 38
- Citations by year
- 2026: 14 · 2025: 12 · 2024: 21 · 2023: 8 · 2022: 16 · 2021: 2
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Socio-Environmental Systems Modeling (acceptedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- Digital Access to Libraries (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fonds Wetenschappelijk Onderzoek (12V9418N)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Keywords
- metagenomics, microbial load, diversity estimation, dysbiosis, inflammatory diseases, benchmarking, false positive detection
- MeSH terms
- Electronic Data Processing, Classification, Humans, Selection Bias, Computational Biology, Benchmarking, Metagenome, Metagenomics, Microbiota, Dysbiosis
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