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dc.contributor.authorConnor, Nora
dc.contributor.authorBarberán, Albert
dc.contributor.authorClauset, Aaron
dc.date.accessioned2017-06-28T21:57:08Z
dc.date.available2017-06-28T21:57:08Z
dc.date.issued2017-05-11
dc.identifier.citationUsing null models to infer microbial co-occurrence networks 2017, 12 (5):e0176751 PLOS ONEen
dc.identifier.issn1932-6203
dc.identifier.doi10.1371/journal.pone.0176751
dc.identifier.urihttp://hdl.handle.net/10150/624498
dc.description.abstractAlthough microbial communities are ubiquitous in nature, relatively little is known about the structural and functional roles of their constituent organisms' underlying interactions. A common approach to study such questions begins with extracting a network of statistically significant pairwise co-occurrences from a matrix of observed operational taxonomic unit (OTU) abundances across sites. The structure of this network is assumed to encode information about ecological interactions and processes, resistance to perturbation, and the identity of keystone species. However, common methods for identifying these pairwise interactions can contaminate the network with spurious patterns that obscure true ecological signals. Here, we describe this problem in detail and develop a solution that incorporates null models to distinguish ecological signals from statistical noise. We apply these methods to the initial OTU abundance matrix and to the extracted network. We demonstrate this approach by applying it to a large soil microbiome data set and show that many previously reported patterns for these data are statistical artifacts. In contrast, we find the frequency of three-way interactions among microbial OTUs to be highly statistically significant. These results demonstrate the importance of using appropriate null models when studying observational microbiome data, and suggest that extracting and characterizing three-way interactions among OTUs is a promising direction for unraveling the structure and function of microbial ecosystems.
dc.language.isoenen
dc.publisherPUBLIC LIBRARY SCIENCEen
dc.relation.urlhttp://dx.plos.org/10.1371/journal.pone.0176751en
dc.rights© 2017 Connor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleUsing null models to infer microbial co-occurrence networksen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Dept Soil Water & Environm Scien
dc.identifier.journalPLOS ONEen
dc.description.noteOpen access journal.en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-06-29T05:04:36Z
html.description.abstractAlthough microbial communities are ubiquitous in nature, relatively little is known about the structural and functional roles of their constituent organisms' underlying interactions. A common approach to study such questions begins with extracting a network of statistically significant pairwise co-occurrences from a matrix of observed operational taxonomic unit (OTU) abundances across sites. The structure of this network is assumed to encode information about ecological interactions and processes, resistance to perturbation, and the identity of keystone species. However, common methods for identifying these pairwise interactions can contaminate the network with spurious patterns that obscure true ecological signals. Here, we describe this problem in detail and develop a solution that incorporates null models to distinguish ecological signals from statistical noise. We apply these methods to the initial OTU abundance matrix and to the extracted network. We demonstrate this approach by applying it to a large soil microbiome data set and show that many previously reported patterns for these data are statistical artifacts. In contrast, we find the frequency of three-way interactions among microbial OTUs to be highly statistically significant. These results demonstrate the importance of using appropriate null models when studying observational microbiome data, and suggest that extracting and characterizing three-way interactions among OTUs is a promising direction for unraveling the structure and function of microbial ecosystems.


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© 2017 Connor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as © 2017 Connor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.