Cited by 1,942Gut microbiota and healthMicrobial Community Ecology and PhysiologyGenomics and Phylogenetic Studies

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Analysis of composition of microbiomes: a novel method for studying microbial composition

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Mandal, Siddhartha, Van Treuren, Will, White, Richard A., Eggesbø, Merete, Knight, Rob, Peddada, Shyamal D.·2015·Microbial Ecology in Health & Disease

DOI 10.3402/mehd.v26.27663PMC PMC4450248

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Abstract

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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Fig. 1 Histogram of pairwise Pearson correlation between operational taxonomic units in the global gut data set.CC BY-NC · PubMed Central

Fig. 2 Comparison of (a) false discovery rate and (b) statistical power to detect differentially abundant microbial taxa by t-test, ZIG, and analysis of composition of microbiomes, based on 100 simulated data sets consisting of 500 (top panels) and 1,000 (bottom panels) taxa. Value of π ranges from 0.05 to 0.25. Power for the t-test is unity over the entire range of π and is not shown on the plots.CC BY-NC · PubMed Central

Fig. 3 Unadjusted raw average OTU relative abundance and standard errors of Bacilli, Clostridia, and Gammaproteobacteria against the variables detected as having significant effects by application of ANCOM on the microbial dataset provided in LaRosa et al. (16). The mean OTU relative abundances for the two modes of birth at different gestational age categories are provided in the first row. The second row provides the mean OTU relative abundances at different ‘Day of life’ categories. The third row provides the mean OTU relative abundance for Bacilli against categories of breast milk variable and for Clostridia against categories of ‘Days on antibiotics’. Although, as in LaRosa et al. (16), ‘Day of life’ and ‘Days on antibiotics’ were analyzed as continuous variables, for simplicity of plotting in this figure they were discretized.CC BY-NC · PubMed Central

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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.
ORCID: Mandal, Siddhartha
White, Richard A.
Eggesbø, Merete
Knight, Rob
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
DOI: 10.3402/mehd.v26.27663
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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Analysis of composition of microbiomes: a novel method for studying microbial composition

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