A phylogenetic transform enhances analysis of compositional microbiota data
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
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Human Microbiome ProjectPublicly available at HMPDACC (v35 download of files 6, 9, and 10).
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Costello Skin SitesPublicly available as part of the FEMS Benchmark dataset (2011) provided Dan Knights.
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Global PatternsPublicly available and provided as part of the phyloseq R package as 'GlobalPatterns'.
Article and author information
Author details
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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