ArticleNature communications2021
Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multi-omics time-series analysis in microbiome research: a systematic review.Briefings in bioinformatics · 2025Pooled it
- Meta-analysis of microbiome association networks reveal patterns of dysbiosis in diseased microbiomes.Scientific reports · 2022Pooled it
- Deconfounded, quantitative microbiome profiling identifies robust multiple sclerosis markers and clinical covariate associations.Gut microbes · 2026Article
- IRS: iterative reference selection improves normalization of microbiome sequencing data.Briefings in bioinformatics · 2026Article
- MATRIX: rapid quantification of total and active microbial cells with single-cell phenotypes for environmental microbiomes.mSystems · 2026Article
- The human breast milk microbiome: a landscape review of its composition, origins, and impact on infant health.Archives of microbiology · 2026Review
- Water mass specific genes dominate the Southern Ocean microbiome.Nature communications · 2026Article
- Host factors dictate gut microbiome alterations in chronic kidney disease more strongly than kidney function.Nature microbiology · 2026Article
- Diverse banana endophytes reveal potential genotype-driven community structure affected by domestication.Frontiers in microbiology · 2026Article
- Review
- Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI.Nature reviews. Gastroenterology & hepatology · 2025Review
- Article
- Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.Cancer research · 2025Review
- Year-Round Quantification, Structure and Dynamics of Epibacterial Communities From Diverse Macroalgae Reveal a Persistent Core Microbiota and Strong Host Specificities.Environmental microbiology reports · 2025Article
- Elementary methods provide more replicable results in microbial differential abundance analysis.Briefings in bioinformatics · 2025Article
- Bacteria invade the brain following intracortical microelectrode implantation, inducing gut-brain axis disruption and contributing to reduced microelectrode performance.Nature communications · 2025Article
- Review
- Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.Journal of machine learning research : JMLR · 2025Article
- Synthetic DNA spike-in standards for cross-domain absolute quantification of microbiomes by rRNA gene amplicon sequencing.ISME communications · 2025Article
- Protective effect and mechanism of Xiaoyu Xiezhuo decoction on ischemia-reperfusion induced acute kidney injury based on gut-kidney crosstalk.Renal failure · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
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