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

By S. Mandal, William W. Van Treuren, Richard A. White, M. Eggesbø, Rob Knight, Shyamal Das Peddada. Microbial Ecology in Health and Disease, 2015

1,832 citations recorded by the source corpus.

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.

Subfields: Clinical Biochemistry, Food Science, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/de8578bfb43861ce8b679f95952a03b567d8db9c

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Citations in the corpus

QIIME 2 Enables Comprehensive End‐to‐End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data (2020, methodology): “We recommend reading the ANCOM paper (Mandal et al., 2015) before using this method.”
Analysis of compositions of microbiomes with bias correction (2020, methodology): “(2) Unbalanced microbial load in two experimental groups and unbalanced library size for each sample.”
Normalization and microbial differential abundance strategies depend upon data characteristics (2017, methodology): “ANCOM differential abundance testing is included in scikitbio (scikit-bio.org) and will be part of QIIME version 2.0.”
Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer (2019, positive): “To rule out spurious associations due to the compositional nature of microbial 162 relative abundance data, we additionally compared the results of this test with a method [25] 163 employing log-ratio transformation (and found highly correlated results, Supplementary Fig.”
scCODA is a Bayesian model for compositional single-cell data analysis (2021, methodology): “2d), we could con ﬁ rm recent ﬁ ndings that ANCOM and ANCOM-BC show increased numbers of false-positive results, especially in the low-sample setting 17,18 .”
Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson's Disease (2016, positive): “Tests were performed using ANCOM (Mandal et al., 2015) in scikit-bio 0.”
Multivariable association discovery in population-scale meta-omics studies (2021, methodology): “It is to be noted that in the presence of multiple covariates, ANCOM does not return statistically significant feature-metadata pairs with respect to every covariate in the model, making it infeasible for our multivariable setting.”
Microbiome Datasets Are Compositional: And This Is Not Optional (2017, methodology): “…to the supplementary and to other published examples (Macklaim et al., 2013; Fernandes et al., 2014; McMurrough et al., 2014; Lovell et al., 2015; Mandal et al., 2015; McMillan et al., 2015; Gloor and Reid, 2016; Gloor et al., 2016b; Bian et al., 2017; Silverman et al., 2017; Quinn et al.,…”
LinDA: linear models for differential abundance analysis of microbiome compositional data (2021, methodology): “The ANCOM proposed by [23] computes the pairwise ratios of the relative abundances and identifies the taxa with the most differential ratios.”