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    Using null models to infer microbial co-occurrence networks

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    Author
    Connor, Nora cc
    Barberán, Albert
    Clauset, Aaron
    Affiliation
    Univ Arizona, Dept Soil Water & Environm Sci
    Issue Date
    2017-05-11
    
    Metadata
    Show full item record
    Publisher
    PUBLIC LIBRARY SCIENCE
    Citation
    Using null models to infer microbial co-occurrence networks 2017, 12 (5):e0176751 PLOS ONE
    Journal
    PLOS ONE
    Rights
    © 2017 Connor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License
    Collection Information
    This 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.
    Abstract
    Although 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.
    Note
    Open access journal.
    ISSN
    1932-6203
    DOI
    10.1371/journal.pone.0176751
    Version
    Final published version
    Additional Links
    http://dx.plos.org/10.1371/journal.pone.0176751
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pone.0176751
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