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@ARTICLE{Hummel:131053,
      author       = {M. Hummel$^*$ and D. Edelmann$^*$ and A.
                      Kopp-Schneider$^*$},
      title        = {{C}lustering of samples and variables with mixed-type
                      data.},
      journal      = {PLoS one},
      volume       = {12},
      number       = {11},
      issn         = {1932-6203},
      address      = {Lawrence, Kan.},
      publisher    = {PLoS},
      reportid     = {DKFZ-2017-06120},
      pages        = {e0188274 -},
      year         = {2017},
      abstract     = {Analysis of data measured on different scales is a relevant
                      challenge. Biomedical studies often focus on high-throughput
                      datasets of, e.g., quantitative measurements. However, the
                      need for integration of other features possibly measured on
                      different scales, e.g. clinical or cytogenetic factors,
                      becomes increasingly important. The analysis results (e.g. a
                      selection of relevant genes) are then visualized, while
                      adding further information, like clinical factors, on top.
                      However, a more integrative approach is desirable, where all
                      available data are analyzed jointly, and where also in the
                      visualization different data sources are combined in a more
                      natural way. Here we specifically target integrative
                      visualization and present a heatmap-style graphic display.
                      To this end, we develop and explore methods for clustering
                      mixed-type data, with special focus on clustering variables.
                      Clustering of variables does not receive as much attention
                      in the literature as does clustering of samples. We extend
                      the variables clustering methodology by two new approaches,
                      one based on the combination of different association
                      measures and the other on distance correlation. With
                      simulation studies we evaluate and compare different
                      clustering strategies. Applying specific methods for
                      mixed-type data proves to be comparable and in many cases
                      beneficial as compared to standard approaches applied to
                      corresponding quantitative or binarized data. Our two novel
                      approaches for mixed-type variables show similar or better
                      performance than the existing methods ClustOfVar and
                      bias-corrected mutual information. Further, in contrast to
                      ClustOfVar, our methods provide dissimilarity matrices,
                      which is an advantage, especially for the purpose of
                      visualization. Real data examples aim to give an impression
                      of various kinds of potential applications for the
                      integrative heatmap and other graphical displays based on
                      dissimilarity matrices. We demonstrate that the presented
                      integrative heatmap provides more information than common
                      data displays about the relationship among variables and
                      samples. The described clustering and visualization methods
                      are implemented in our R package CluMix available from
                      https://cran.r-project.org/web/packages/CluMix.},
      cin          = {C060},
      ddc          = {500},
      cid          = {I:(DE-He78)C060-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29182671},
      doi          = {10.1371/journal.pone.0188274},
      url          = {https://inrepo02.dkfz.de/record/131053},
}