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


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          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.


          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.


          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.


          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.


          Accounting for compositionality using log-ratio analysis results in significantly improved inference in microbiota survey data.

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          Most cited references12

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          Exposure to environmental microorganisms and childhood asthma.

          Children who grow up in environments that afford them a wide range of microbial exposures, such as traditional farms, are protected from childhood asthma and atopy. In previous studies, markers of microbial exposure have been inversely related to these conditions. In two cross-sectional studies, we compared children living on farms with those in a reference group with respect to the prevalence of asthma and atopy and to the diversity of microbial exposure. In one study--PARSIFAL (Prevention of Allergy-Risk Factors for Sensitization in Children Related to Farming and Anthroposophic Lifestyle)--samples of mattress dust were screened for bacterial DNA with the use of single-strand conformation polymorphism (SSCP) analyses to detect environmental bacteria that cannot be measured by means of culture techniques. In the other study--GABRIELA (Multidisciplinary Study to Identify the Genetic and Environmental Causes of Asthma in the European Community [GABRIEL] Advanced Study)--samples of settled dust from children's rooms were evaluated for bacterial and fungal taxa with the use of culture techniques. In both studies, children who lived on farms had lower prevalences of asthma and atopy and were exposed to a greater variety of environmental microorganisms than the children in the reference group. In turn, diversity of microbial exposure was inversely related to the risk of asthma (odds ratio for PARSIFAL, 0.62; 95% confidence interval [CI], 0.44 to 0.89; odds ratio for GABRIELA, 0.86; 95% CI, 0.75 to 0.99). In addition, the presence of certain more circumscribed exposures was also inversely related to the risk of asthma; this included exposure to species in the fungal taxon eurotium (adjusted odds ratio, 0.37; 95% CI, 0.18 to 0.76) and to a variety of bacterial species, including Listeria monocytogenes, bacillus species, corynebacterium species, and others (adjusted odds ratio, 0.57; 95% CI, 0.38 to 0.86). Children living on farms were exposed to a wider range of microbes than were children in the reference group, and this exposure explains a substantial fraction of the inverse relation between asthma and growing up on a farm. (Funded by the Deutsche Forschungsgemeinschaft and the European Commission.).
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            Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data

            This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The use of a fully parametric model for these data has the benefit over alternative non-parametric approaches such as bootstrapping and permutation testing, in that this model is able to retain more information contained in the data. This paper details the statistical approaches for several tests of hypothesis and power/sample size calculations, and applies them for illustration to taxonomic abundance distribution and rank abundance distribution data using HMP Jumpstart data on 24 subjects for saliva, subgingival, and supragingival samples. Software for running these analyses is available.
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              The human microbiome and its potential importance to pediatrics.

              The human body is home to more than 1 trillion microbes, with the gastrointestinal tract alone harboring a diverse array of commensal microbes that are believed to contribute to host nutrition, developmental regulation of intestinal angiogenesis, protection from pathogens, and development of the immune response. Recent advances in genome sequencing technologies and metagenomic analysis are providing a broader understanding of these resident microbes and highlighting differences between healthy and disease states. The aim of this review is to provide a detailed summary of current pediatric microbiome studies in the literature, in addition to highlighting recent findings and advancements in studies of the adult microbiome. This review also seeks to elucidate the development of, and factors that could lead to changes in, the composition and function of the human microbiome.

                Author and article information

                Microb Ecol Health Dis
                Microb. Ecol. Health Dis
                Microbial Ecology in Health and Disease
                Co-Action Publishing
                29 May 2015
                : 26
                : 10.3402/mehd.v26.27663
                [1 ]Department of Genes and Environment, Norwegian Institute of Public Health, Oslo, Norway
                [2 ]Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
                [3 ]Department of Health Statistics, Norwegian Institute of Public Health, Oslo, Norway
                [4 ]Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA
                [5 ]Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
                [6 ]Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, USA
                Author notes
                [* ]Correspondence to: Shyamal D. Peddada, PO Box 12233, Mail Drop A3-03, Research Triangle Park, Durham, NC 27709, USA, Email: peddada@ 123456niehs.nih.gov
                © 2015 Siddhartha Mandal et al.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License, permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 19 February 2015
                : 24 April 2015
                : 27 April 2015
                Original Article

                Microbiology & Virology
                constrained,relative abundance,log-ratio
                Microbiology & Virology
                constrained, relative abundance, log-ratio


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