How to cite the Bioconductor package philr

Silverman, J. D., Washburne, A. D., Mukherjee, S., & David, L. A. (2017). A phylogenetic transform enhances analysis of compositional microbiota data. ELife, 6. https://doi.org/10.7554/elife.21887

1.

Silverman JD, Washburne AD, Mukherjee S, David LA. A phylogenetic transform enhances analysis of compositional microbiota data. Elife [Internet]. 2017 Feb 15;6. Available from: http://dx.doi.org/10.7554/elife.21887

@ARTICLE{Silverman2017-ry,
  title     = "A phylogenetic transform enhances analysis of compositional
               microbiota data",
  author    = "Silverman, Justin D and Washburne, Alex D and Mukherjee, Sayan
               and David, Lawrence A",
  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.",
  journal   = "Elife",
  publisher = "eLife Sciences Publications, Ltd",
  volume    =  6,
  month     =  feb,
  year      =  2017,
  url       = "http://dx.doi.org/10.7554/elife.21887",
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en",
  issn      = "2050-084X",
  doi       = "10.7554/elife.21887"
}

TY  - JOUR
AU  - Silverman, Justin D
AU  - Washburne, Alex D
AU  - Mukherjee, Sayan
AU  - David, Lawrence A
AD  - Program in Computational Biology and Bioinformatics, Duke University,
      Durham, United States; Medical Scientist Training Program, Duke
      University, Durham, United States; Center for Genomic and Computational
      Biology, Duke University, Durham, United States; Nicholas School of the
      Environment, Duke University, Durham, United States; Cooperative Institute
      for Research in Environmental Sciences (CIRES), University of Colorado,
      Boulder, United States; Program in Computational Biology and
      Bioinformatics, Duke University, Durham, United States; Department of
      Statistical Science, Duke University, Durham, United States; Department of
      Mathematics, Duke University, Durham, United States; Department of
      Biostatistics and Bioinformatics, Duke University, Durham, United States;
      Department of Computer Science, Duke University, Durham, United States;
      Program in Computational Biology and Bioinformatics, Duke University,
      Durham, United States; Center for Genomic and Computational Biology, Duke
      University, Durham, United States; Department of Molecular Genetics and
      Microbiology, Duke University, Durham, United States
TI  - A phylogenetic transform enhances analysis of compositional microbiota
      data
T2  - Elife
VL  - 6
PY  - 2017
DA  - 2017/2/15
PB  - eLife Sciences Publications, Ltd
AB  - 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.
SN  - 2050-084X
DO  - 10.7554/elife.21887
UR  - http://dx.doi.org/10.7554/elife.21887
ER  -