A statistical model for describing and simulating microbial community profiles.

Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or...

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Autores principales:	Siyuan Ma, Boyu Ren, Himel Mallick, Yo Sup Moon, Emma Schwager, Sagun Maharjan, Timothy L Tickle, Yiren Lu, Rachel N Carmody, Eric A Franzosa, Lucas Janson, Curtis Huttenhower
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/b5a61e321c084730884928cb547eac54
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spelling	oai:doaj.org-article:b5a61e321c084730884928cb547eac542021-12-02T19:57:49ZA statistical model for describing and simulating microbial community profiles.1553-734X1553-735810.1371/journal.pcbi.1008913https://doaj.org/article/b5a61e321c084730884928cb547eac542021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1008913https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA's model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA's performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA's utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.Siyuan MaBoyu RenHimel MallickYo Sup MoonEmma SchwagerSagun MaharjanTimothy L TickleYiren LuRachel N CarmodyEric A FranzosaLucas JansonCurtis HuttenhowerPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1008913 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Siyuan Ma Boyu Ren Himel Mallick Yo Sup Moon Emma Schwager Sagun Maharjan Timothy L Tickle Yiren Lu Rachel N Carmody Eric A Franzosa Lucas Janson Curtis Huttenhower A statistical model for describing and simulating microbial community profiles.
description	Many methods have been developed for statistical analysis of microbial community profiles, but due to the complex nature of typical microbiome measurements (e.g. sparsity, zero-inflation, non-independence, and compositionality) and of the associated underlying biology, it is difficult to compare or evaluate such methods within a single systematic framework. To address this challenge, we developed SparseDOSSA (Sparse Data Observations for the Simulation of Synthetic Abundances): a statistical model of microbial ecological population structure, which can be used to parameterize real-world microbial community profiles and to simulate new, realistic profiles of known structure for methods evaluation. Specifically, SparseDOSSA's model captures marginal microbial feature abundances as a zero-inflated log-normal distribution, with additional model components for absolute cell counts and the sequence read generation process, microbe-microbe, and microbe-environment interactions. Together, these allow fully known covariance structure between synthetic features (i.e. "taxa") or between features and "phenotypes" to be simulated for method benchmarking. Here, we demonstrate SparseDOSSA's performance for 1) accurately modeling human-associated microbial population profiles; 2) generating synthetic communities with controlled population and ecological structures; 3) spiking-in true positive synthetic associations to benchmark analysis methods; and 4) recapitulating an end-to-end mouse microbiome feeding experiment. Together, these represent the most common analysis types in assessment of real microbial community environmental and epidemiological statistics, thus demonstrating SparseDOSSA's utility as a general-purpose aid for modeling communities and evaluating quantitative methods. An open-source implementation is available at http://huttenhower.sph.harvard.edu/sparsedossa2.
format	article
author	Siyuan Ma Boyu Ren Himel Mallick Yo Sup Moon Emma Schwager Sagun Maharjan Timothy L Tickle Yiren Lu Rachel N Carmody Eric A Franzosa Lucas Janson Curtis Huttenhower
author_facet	Siyuan Ma Boyu Ren Himel Mallick Yo Sup Moon Emma Schwager Sagun Maharjan Timothy L Tickle Yiren Lu Rachel N Carmody Eric A Franzosa Lucas Janson Curtis Huttenhower
author_sort	Siyuan Ma
title	A statistical model for describing and simulating microbial community profiles.
title_short	A statistical model for describing and simulating microbial community profiles.
title_full	A statistical model for describing and simulating microbial community profiles.
title_fullStr	A statistical model for describing and simulating microbial community profiles.
title_full_unstemmed	A statistical model for describing and simulating microbial community profiles.
title_sort	statistical model for describing and simulating microbial community profiles.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/b5a61e321c084730884928cb547eac54
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A statistical model for describing and simulating microbial community profiles.

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