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Elife. 2017 Feb 15;6. pii: e21887. doi: 10.7554/eLife.21887.

A phylogenetic transform enhances analysis of compositional microbiota data.

Silverman JD1,2,3, Washburne AD4,5, Mukherjee S1,6,7,8,9, David LA1,3,10.

Author information

1
Program in Computational Biology and Bioinformatics, Duke University, Durham, United States.
2
Medical Scientist Training Program, Duke University, Durham, United States.
3
Center for Genomic and Computational Biology, Duke University, Durham, United States.
4
Nicholas School of the Environment, Duke University, Durham, United States.
5
Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, United States.
6
Department of Statistical Science, Duke University, Durham, United States.
7
Department of Mathematics, Duke University, Durham, United States.
8
Department of Biostatistics and Bioinformatics, Duke University, Durham, United States.
9
Department of Computer Science, Duke University, Durham, United States.
10
Department of Molecular Genetics and Microbiology, Duke University, Durham, United States.

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.

KEYWORDS:

Phylogenetics; compositional data; evolutionary biology; genomics; human; infectious disease; metagenomics; microbial evolution; microbiology; microbiome

PMID:
28198697
PMCID:
PMC5328592
DOI:
10.7554/eLife.21887
[Indexed for MEDLINE]
Free PMC Article

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