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BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely use...

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Autores principales: Li, Chenhao, Av-Shalom, Tamar V., Tan, Jun Wei Gerald, Kwah, Junmei Samantha, Chng, Kern Rei, Nagarajan, Niranjan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452072/
https://www.ncbi.nlm.nih.gov/pubmed/34495960
http://dx.doi.org/10.1371/journal.pcbi.1009343
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author Li, Chenhao
Av-Shalom, Tamar V.
Tan, Jun Wei Gerald
Kwah, Junmei Samantha
Chng, Kern Rei
Nagarajan, Niranjan
author_facet Li, Chenhao
Av-Shalom, Tamar V.
Tan, Jun Wei Gerald
Kwah, Junmei Samantha
Chng, Kern Rei
Nagarajan, Niranjan
author_sort Li, Chenhao
collection PubMed
description The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. CONCLUSION: BEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data.
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spelling pubmed-84520722021-09-21 BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data Li, Chenhao Av-Shalom, Tamar V. Tan, Jun Wei Gerald Kwah, Junmei Samantha Chng, Kern Rei Nagarajan, Niranjan PLoS Comput Biol Research Article The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. CONCLUSION: BEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data. Public Library of Science 2021-09-08 /pmc/articles/PMC8452072/ /pubmed/34495960 http://dx.doi.org/10.1371/journal.pcbi.1009343 Text en © 2021 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Li, Chenhao
Av-Shalom, Tamar V.
Tan, Jun Wei Gerald
Kwah, Junmei Samantha
Chng, Kern Rei
Nagarajan, Niranjan
BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data
title BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data
title_full BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data
title_fullStr BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data
title_full_unstemmed BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data
title_short BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data
title_sort beem-static: accurate inference of ecological interactions from cross-sectional microbiome data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8452072/
https://www.ncbi.nlm.nih.gov/pubmed/34495960
http://dx.doi.org/10.1371/journal.pcbi.1009343
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