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Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets.
Washburne, Alex D; Silverman, Justin D; Leff, Jonathan W; Bennett, Dominic J; Darcy, John L; Mukherjee, Sayan; Fierer, Noah; David, Lawrence A.
Affiliation
  • Washburne AD; Nicholas School of the Environment, Duke University , Durham , NC , United States.
  • Silverman JD; 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
  • Leff JW; Cooperative Institute for Research in Environmental Sciences, University of Colorado , Boulder , CO , United States.
  • Bennett DJ; Department of Earth Science and Engineering, Imperial College London, London, United Kingdom; Institute of Zoology, Zoological Society of London, London, United Kingdom.
  • Darcy JL; Department of Ecology and Evolution, University of Colorado Boulder , Boulder , CO , United States.
  • Mukherjee S; 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.
  • Fierer N; Cooperative Institute for Research in Environmental Sciences, University of Colorado , Boulder , CO , United States.
  • David LA; 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.
PeerJ ; 5: e2969, 2017.
Article in En | MEDLINE | ID: mdl-28289558
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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: PeerJ Year: 2017 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: PeerJ Year: 2017 Type: Article Affiliation country: United States