Sparse and Compositionally Robust Inference of Microbial Ecological Networks

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain e...

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Main Authors:	Kurtz, Zachary D., Müller, Christian L., Miraldi, Emily R., Littman, Dan R., Blaser, Martin J., Bonneau, Richard A.
Format:	Online
Language:	English
Published:	Public Library of Science 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423992/

id	pubmed-4423992
recordtype	oai_dc
spelling	pubmed-44239922015-05-13 Sparse and Compositionally Robust Inference of Microbial Ecological Networks Kurtz, Zachary D. Müller, Christian L. Miraldi, Emily R. Littman, Dan R. Blaser, Martin J. Bonneau, Richard A. Research Article 16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project. Public Library of Science 2015-05-07 /pmc/articles/PMC4423992/ /pubmed/25950956 http://dx.doi.org/10.1371/journal.pcbi.1004226 Text en © 2015 Kurtz et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Kurtz, Zachary D. Müller, Christian L. Miraldi, Emily R. Littman, Dan R. Blaser, Martin J. Bonneau, Richard A.
spellingShingle	Kurtz, Zachary D. Müller, Christian L. Miraldi, Emily R. Littman, Dan R. Blaser, Martin J. Bonneau, Richard A. Sparse and Compositionally Robust Inference of Microbial Ecological Networks
author_facet	Kurtz, Zachary D. Müller, Christian L. Miraldi, Emily R. Littman, Dan R. Blaser, Martin J. Bonneau, Richard A.
author_sort	Kurtz, Zachary D.
title	Sparse and Compositionally Robust Inference of Microbial Ecological Networks
title_short	Sparse and Compositionally Robust Inference of Microbial Ecological Networks
title_full	Sparse and Compositionally Robust Inference of Microbial Ecological Networks
title_fullStr	Sparse and Compositionally Robust Inference of Microbial Ecological Networks
title_full_unstemmed	Sparse and Compositionally Robust Inference of Microbial Ecological Networks
title_sort	sparse and compositionally robust inference of microbial ecological networks
description	16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.
publisher	Public Library of Science
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423992/
_version_	1613220804381638656

Sparse and Compositionally Robust Inference of Microbial Ecological Networks

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