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Analysis of composition of microbiomes: a novel method for studying microbial composition
Open accessAbstract
BACKGROUND: Understanding the factors regulating our microbiota is important but requires appropriate statistical methodology. When comparing two or more populations most existing approaches either discount the underlying compositional structure in the microbiome data or use probability models such as the multinomial and Dirichlet-multinomial distributions, which may impose a correlation structure not suitable for microbiome data. OBJECTIVE: To develop a methodology that accounts for compositional constraints to reduce false discoveries in detecting differentially abundant taxa at an ecosystem level, while maintaining high statistical power. METHODS: We introduced a novel statistical framework called analysis of composition of microbiomes (ANCOM). ANCOM accounts for the underlying structure in the data and can be used for comparing the composition of microbiomes in two or more populations. ANCOM makes no distributional assumptions and can be implemented in a linear model framework to adjust for covariates as well as model longitudinal data. ANCOM also scales well to compare samples involving thousands of taxa. RESULTS: We compared the performance of ANCOM to the standard t-test and a recently published methodology called Zero Inflated Gaussian (ZIG) methodology (1) for drawing inferences on the mean taxa abundance in two or more populations. ANCOM controlled the false discovery rate (FDR) at the desired nominal level while also improving power, whereas the t-test and ZIG had inflated FDRs, in some instances as high as 68% for the t-test and 60% for ZIG. We illustrate the performance of ANCOM using two publicly available microbial datasets in the human gut, demonstrating its general applicability to testing hypotheses about compositional differences in microbial communities. CONCLUSION: Accounting for compositionality using log-ratio analysis results in significantly improved inference in microbiota survey data.
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“As such, this is a sensitive test for taxa that vary significantly and in an unusual way compared to the other taxa.”
From Population structure of human gut bacteria in a diverse cohort from rural Tanzania and Botswana (2019) · cites this paper as [40]
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“The ANCOM method accounts for the simplex nature of compositional data, and so does not suffer from spurious negative correlations imposed by the fact that (relative) abundances across all bacteria must sum to one within a given bacterial community.”
From Population structure of human gut bacteria in a diverse cohort from rural Tanzania and Botswana (2019) · cites this paper as [40]
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Metadata
- Authors
- Mandal, S., Van Treuren, W., White, R. A., Eggesbø, M., Knight, R., & Peddada, S. D.
- Affiliations
- Norwegian Institute of Public Health (NO)
- University of California San Diego (US)
- National Institute of Environmental Health Sciences (US)
- Corresponding author
- Peddada, Shyamal D.
- Venue
- Microbial Ecology in Health & Disease
- Publisher
- Co-Action Publishing
- Published
- 2015-05-29
- Volume
- 26
- Issue
- 0
- Pages
- 27663-27663
- ISSN
- 0891-060X (ISSN-L)
- 1651-2235
- PMC
- PMC4450248
- OpenAlex
- W1523266228
- Cited by
- 1,942
- References
- 21
- Citations by year
- 2026: 61 · 2025: 169 · 2024: 230 · 2023: 264 · 2022: 325 · 2021: 326 · 2020: 248 · 2019: 153 · 2018: 96 · 2017: 42 · 2016: 23 · 2015: 2
- FWCI
- 23.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- cc-by-nc
- Locations
- Microbial Ecology in Health and Disease (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- BIBSYS Brage (BIBSYS (Norway)) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- ANCOM, microbiota composition, compositional data, false discovery rate, differentially abundant taxa, log-ratio analysis, human gut microbiome, statistical power, covariate adjustment, longitudinal microbiome data
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