A phylogenetic transform enhances analysis of compositional microbiota data

Justin D. Silverman, Alex D. Washburne, Sayan Mukherjee, Lawrence A. David

    Research output: Contribution to journalArticlepeer-review

    67 Scopus citations

    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.

    Original languageEnglish (US)
    Article numbere21887
    JournaleLife
    Volume6
    DOIs
    StatePublished - Feb 15 2017

    All Science Journal Classification (ASJC) codes

    • Neuroscience(all)
    • Immunology and Microbiology(all)
    • Biochemistry, Genetics and Molecular Biology(all)

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