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PeerJ. 2017 Feb 9;5:e2969. doi: 10.7717/peerj.2969. eCollection 2017.

Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets.

Author information

1
Nicholas School of the Environment, Duke University , Durham , NC , United States.
2
Program for Computational Biology and Bioinformatics, Duke University, Durham, NC, United States; Medical Scientist Training Program, Duke University, Durham, NC, United States; Center for Genomic and Computational Biology, Duke University, Durham, NC, United States; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, United States.
3
Cooperative Institute for Research in Environmental Sciences, University of Colorado , Boulder , CO , United States.
4
Department of Earth Science and Engineering, Imperial College London, London, United Kingdom; Institute of Zoology, Zoological Society of London, London, United Kingdom.
5
Department of Ecology and Evolution, University of Colorado Boulder , Boulder , CO , United States.
6
Program for Computational Biology and Bioinformatics, Duke University, Durham, NC, United States; Department of Statistical Science, Mathematics, and Computer Science, Duke University, Durham, NC, United States.
7
Program for Computational Biology and Bioinformatics, Duke University, Durham, NC, United States; Center for Genomic and Computational Biology, Duke University, Durham, NC, United States; Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, United States.

Abstract

Marker gene sequencing of microbial communities has generated big datasets of microbial relative abundances varying across environmental conditions, sample sites and treatments. These data often come with putative phylogenies, providing unique opportunities to investigate how shared evolutionary history affects microbial abundance patterns. Here, we present a method to identify the phylogenetic factors driving patterns in microbial community composition. We use the method, "phylofactorization," to re-analyze datasets from the human body and soil microbial communities, demonstrating how phylofactorization is a dimensionality-reducing tool, an ordination-visualization tool, and an inferential tool for identifying edges in the phylogeny along which putative functional ecological traits may have arisen.

KEYWORDS:

Community phylogenetics; Compositional data; Factor analysis; Microbial biogeography; Microbiome; Phylofactorization; Sequence-count data

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