How to cite the R package coca

Cabassi, A., & Kirk, P. D. W. (2020). Multiple kernel learning for integrative consensus clustering of omic datasets. Bioinformatics, 36(18), 4789–4796.

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Cabassi A, Kirk PDW. Multiple kernel learning for integrative consensus clustering of omic datasets. Bioinformatics. 2020 Sep 15;36(18):4789–96.

@ARTICLE{Cabassi2020-ue,
  title     = "Multiple kernel learning for integrative consensus clustering of
               omic datasets",
  author    = "Cabassi, Alessandra and Kirk, Paul D W",
  abstract  = "Abstract Motivation Diverse applications---particularly in
               tumour subtyping---have demonstrated the importance of
               integrative clustering techniques for combining information from
               multiple data sources. Cluster Of Clusters Analysis (COCA) is
               one such approach that has been widely applied in the context of
               tumour subtyping. However, the properties of COCA have never
               been systematically explored, and its robustness to the
               inclusion of noisy datasets is unclear. Results We rigorously
               benchmark COCA, and present Kernel Learning Integrative
               Clustering (KLIC) as an alternative strategy. KLIC frames the
               challenge of combining clustering structures as a multiple
               kernel learning problem, in which different datasets each
               provide a weighted contribution to the final clustering. This
               allows the contribution of noisy datasets to be down-weighted
               relative to more informative datasets. We compare the
               performances of KLIC and COCA in a variety of situations through
               simulation studies. We also present the output of KLIC and COCA
               in real data applications to cancer subtyping and
               transcriptional module discovery. Availability and
               implementation R packages klic and coca are available on the
               Comprehensive R Archive Network. Supplementary information
               Supplementary data are available at Bioinformatics online.",
  journal   = "Bioinformatics",
  publisher = "Oxford University Press (OUP)",
  volume    =  36,
  number    =  18,
  pages     = "4789--4796",
  month     =  sep,
  year      =  2020,
  url       = "http://dx.doi.org/10.1093/bioinformatics/btaa593",
  copyright = "http://creativecommons.org/licenses/by/4.0/",
  language  = "en",
  issn      = "1367-4803, 1460-2059",
  doi       = "10.1093/bioinformatics/btaa593"
}

TY  - JOUR
AU  - Cabassi, Alessandra
AU  - Kirk, Paul D W
AD  - MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK;
      MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK;
      Cambridge Institute of Therapeutic Immunology & Infectious Disease,
      University of Cambridge, Cambridge CB2 0AW, UK
TI  - Multiple kernel learning for integrative consensus clustering of omic
      datasets
T2  - Bioinformatics
VL  - 36
IS  - 18
SP  - 4789-4796
PY  - 2020
DA  - 2020/9/15
PB  - Oxford University Press (OUP)
AB  - Abstract Motivation Diverse applications—particularly in tumour
      subtyping—have demonstrated the importance of integrative clustering
      techniques for combining information from multiple data sources. Cluster
      Of Clusters Analysis (COCA) is one such approach that has been widely
      applied in the context of tumour subtyping. However, the properties of
      COCA have never been systematically explored, and its robustness to the
      inclusion of noisy datasets is unclear. Results We rigorously benchmark
      COCA, and present Kernel Learning Integrative Clustering (KLIC) as an
      alternative strategy. KLIC frames the challenge of combining clustering
      structures as a multiple kernel learning problem, in which different
      datasets each provide a weighted contribution to the final clustering.
      This allows the contribution of noisy datasets to be down-weighted
      relative to more informative datasets. We compare the performances of KLIC
      and COCA in a variety of situations through simulation studies. We also
      present the output of KLIC and COCA in real data applications to cancer
      subtyping and transcriptional module discovery. Availability and
      implementation R packages klic and coca are available on the Comprehensive
      R Archive Network. Supplementary information Supplementary data are
      available at Bioinformatics online.
SN  - 1367-4803
DO  - 10.1093/bioinformatics/btaa593
UR  - http://dx.doi.org/10.1093/bioinformatics/btaa593
ER  -