1. Genetics and Genomics
  2. Microbiology and Infectious Disease
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A phylogenetic transform enhances analysis of compositional microbiota data

  1. Justin D Silverman
  2. Alex D Washburne
  3. Sayan Mukherjee
  4. Lawrence A David  Is a corresponding author
  1. Duke University, United States
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Cite this article as: eLife 2017;6:e21887 doi: 10.7554/eLife.21887

Abstract

Surveys of microbial communities (microbiota), typically measured as relative abundance of species, have illustrated the importance of these communities in human health and disease. Yet, statistical artifacts commonly plague the analysis of relative abundance data. Here, we introduce the PhILR transform, which incorporates microbial evolutionary models with the isometric log-ratio transform to allow off-the-shelf statistical tools to be safely applied to microbiota surveys. We demonstrate that analyses of community-level structure can be applied to PhILR transformed data with performance on benchmarks rivaling or surpassing standard tools. Additionally, By decomposing distance in the PhILR transformed space, we identified neighboring clades that may have adapted to distinct human body sites. Decomposing variance revealed that covariation of bacterial clades within human body sites increases with phylogenetic relatedness. Together, these findings illustrate how the PhILR transform combines statistical and phylogenetic models to overcome compositional data challenges and enable evolutionary insights relevant to microbial communities.

Article and author information

Author details

  1. Justin D Silverman

    Program in Computational Biology and Bioinformatics, Duke University, Durham, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3063-2098
  2. Alex D Washburne

    Nicholas School of the Environment, Duke University, Durham, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Sayan Mukherjee

    Program in Computational Biology and Bioinformatics, Duke University, Durham, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Lawrence A David

    Program in Computational Biology and Bioinformatics, Duke University, Durham, United States
    For correspondence
    lawrence.david@duke.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3570-4767

Funding

Global Probiotics Council (Young Investigator Grant for Probiotics Research)

  • Lawrence A David

Searle Scholars Program (15-SSP-184 Research Agreement)

  • Lawrence A David

Alfred P. Sloan Foundation (BR2014-003)

  • Lawrence A David

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Anthony Fodor, University of North Carolina at Charlotte

Publication history

  1. Received: September 27, 2016
  2. Accepted: February 13, 2017
  3. Accepted Manuscript published: February 15, 2017 (version 1)
  4. Version of Record published: February 27, 2017 (version 2)

Copyright

© 2017, Silverman et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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