1. Genetics and Genomics
  2. Microbiology and Infectious Disease

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
https://doi.org/10.7554/eLife.21887
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Cite this article
  1. Justin D Silverman
  2. Alex D Washburne
  3. Sayan Mukherjee
  4. Lawrence A David
(2017)
A phylogenetic transform enhances analysis of compositional microbiota data
eLife 6:e21887.
https://doi.org/10.7554/eLife.21887
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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.

Data availability

The following previously published data sets were used
    1. Human Microbiome Project Consortium
    (2010) Human Microbiome Project
    Publicly available at HMPDACC (v35 download of files 6, 9, and 10).
    http://hmpdacc.org/HMQCP/
    1. Costello EK
    2. Lauber CL
    3. Hamady M
    4. Fierer N
    5. Gordon JI
    6. Knight R
    (2009) Costello Skin Sites
    Publicly available as part of the FEMS Benchmark dataset (2011) provided Dan Knights.
    http://www.knightslab.org/data

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.

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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  1. Justin D Silverman
  2. Alex D Washburne
  3. Sayan Mukherjee
  4. Lawrence A David
(2017)
A phylogenetic transform enhances analysis of compositional microbiota data
eLife 6:e21887.
https://doi.org/10.7554/eLife.21887

Share this article

https://doi.org/10.7554/eLife.21887

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