How to cite the R package partition

Millstein, J., Battaglin, F., Barrett, M., Cao, S., Zhang, W., Stintzing, S., Heinemann, V., & Lenz, H.-J. (2020). Partition: a surjective mapping approach for dimensionality reduction. Bioinformatics, 36(3), 676–681.

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Millstein J, Battaglin F, Barrett M, Cao S, Zhang W, Stintzing S, et al. Partition: a surjective mapping approach for dimensionality reduction. Bioinformatics. 2020 Feb 1;36(3):676–81.

@ARTICLE{Millstein2020-fy,
  title     = "Partition: a surjective mapping approach for dimensionality
               reduction",
  author    = "Millstein, Joshua and Battaglin, Francesca and Barrett, Malcolm
               and Cao, Shu and Zhang, Wu and Stintzing, Sebastian and
               Heinemann, Volker and Lenz, Heinz-Josef",
  abstract  = "Abstract Motivation Large amounts of information generated by
               genomic technologies are accompanied by statistical and
               computational challenges due to redundancy, badly behaved data
               and noise. Dimensionality reduction (DR) methods have been
               developed to mitigate these challenges. However, many approaches
               are not scalable to large dimensions or result in excessive
               information loss. Results The proposed approach partitions data
               into subsets of related features and summarizes each into one
               and only one new feature, thus defining a surjective mapping. A
               constraint on information loss determines the size of the
               reduced dataset. Simulation studies demonstrate that when
               multiple related features are associated with a response, this
               approach can substantially increase the number of true
               associations detected as compared to principal components
               analysis, non-negative matrix factorization or no DR. This
               increase in true discoveries is explained both by a reduced
               multiple-testing challenge and a reduction in extraneous noise.
               In an application to real data collected from metastatic
               colorectal cancer tumors, more associations between gene
               expression features and progression free survival and response
               to treatment were detected in the reduced than in the full
               untransformed dataset. Availability and implementation Freely
               available R package from CRAN,
               https://cran.r-project.org/package=partition. Supplementary
               information Supplementary data are available at Bioinformatics
               online.",
  journal   = "Bioinformatics",
  publisher = "Oxford University Press (OUP)",
  volume    =  36,
  number    =  3,
  pages     = "676--681",
  month     =  feb,
  year      =  2020,
  url       = "http://dx.doi.org/10.1093/bioinformatics/btz661",
  copyright = "https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model",
  language  = "en",
  issn      = "1367-4803, 1460-2059",
  doi       = "10.1093/bioinformatics/btz661"
}

TY  - JOUR
AU  - Millstein, Joshua
AU  - Battaglin, Francesca
AU  - Barrett, Malcolm
AU  - Cao, Shu
AU  - Zhang, Wu
AU  - Stintzing, Sebastian
AU  - Heinemann, Volker
AU  - Lenz, Heinz-Josef
AD  - Department of Preventive Medicine, CA 90033, USA; Department of Medicine,
      Division of Medical Oncology, Norris Comprehensive Cancer Center, Keck
      School of Medicine, University of Southern California, Los Angeles, CA
      90033, USA; Clinical and Experimental Oncology Department, Medical
      Oncology Unit 1, Veneto Institute of Oncology IOV-IRCCS, Padua 35128,
      Italy; Department of Medicine, Division of Medical Oncology, Norris
      Comprehensive Cancer Center, Keck School of Medicine, University of
      Southern California, Los Angeles, CA 90033, USA; Medical Department,
      Division of Oncology and Hematology, Charité Universitaetsmedizin Berlin,
      Berlin 10117, Germany; Department of Medicine III, University Hospital
      Munich, Munich 80336, Germany
TI  - Partition: a surjective mapping approach for dimensionality reduction
T2  - Bioinformatics
VL  - 36
IS  - 3
SP  - 676-681
PY  - 2020
DA  - 2020/2/1
PB  - Oxford University Press (OUP)
AB  - Abstract Motivation Large amounts of information generated by genomic
      technologies are accompanied by statistical and computational challenges
      due to redundancy, badly behaved data and noise. Dimensionality reduction
      (DR) methods have been developed to mitigate these challenges. However,
      many approaches are not scalable to large dimensions or result in
      excessive information loss. Results The proposed approach partitions data
      into subsets of related features and summarizes each into one and only one
      new feature, thus defining a surjective mapping. A constraint on
      information loss determines the size of the reduced dataset. Simulation
      studies demonstrate that when multiple related features are associated
      with a response, this approach can substantially increase the number of
      true associations detected as compared to principal components analysis,
      non-negative matrix factorization or no DR. This increase in true
      discoveries is explained both by a reduced multiple-testing challenge and
      a reduction in extraneous noise. In an application to real data collected
      from metastatic colorectal cancer tumors, more associations between gene
      expression features and progression free survival and response to
      treatment were detected in the reduced than in the full untransformed
      dataset. Availability and implementation Freely available R package from
      CRAN, https://cran.r-project.org/package=partition. Supplementary
      information Supplementary data are available at Bioinformatics online.
SN  - 1367-4803
DO  - 10.1093/bioinformatics/btz661
UR  - http://dx.doi.org/10.1093/bioinformatics/btz661
ER  -