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@ARTICLE{Krger:274055,
      author       = {Kröger, Charlotte and Müller, Sophie and Leidner,
                      Jacqueline and Kroeber, Theresa and Warnat-Herresthal,
                      Stefanie and Spintge, Jannis Bastian and Zajac, Timo and
                      Neubauer, Anna and Frolov, Aleksej and Carraro, Caterina and
                      Jessen, Frank and Puccio, Simone and Aschenbrenner, Anna C
                      and Schultze, Joachim L and Pecht, Tal and Beyer, Marc D and
                      Bonaguro, Lorenzo},
      collaboration = {Group, DELCODE Study},
      othercontributors = {Freiesleben, Silka Dawn and Altenstein, Slawek and
                          Rauchmann, Boris Stephan and Kilimann, Ingo and Coenjaerts,
                          Marie and Spottke, Annika and Peters, Oliver and Priller,
                          Josef and Perneczky, Robert and Teipel, Stefan and Düzel,
                          Emrah},
      title        = {{U}nveiling the power of high-dimensional cytometry data
                      with cy{CONDOR}.},
      journal      = {Nature Communications},
      volume       = {15},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {DZNE-2025-00036},
      pages        = {10702},
      year         = {2024},
      abstract     = {High-dimensional cytometry (HDC) is a powerful technology
                      for studying single-cell phenotypes in complex biological
                      systems. Although technological developments and
                      affordability have made HDC broadly available in recent
                      years, technological advances were not coupled with an
                      adequate development of analytical methods that can take
                      full advantage of the complex data generated. While several
                      analytical platforms and bioinformatics tools have become
                      available for the analysis of HDC data, these are either
                      web-hosted with limited scalability or designed for expert
                      computational biologists, making their use unapproachable
                      for wet lab scientists. Additionally, end-to-end HDC data
                      analysis is further hampered due to missing unified
                      analytical ecosystems, requiring researchers to navigate
                      multiple platforms and software packages to complete the
                      analysis. To bridge this data analysis gap in HDC we develop
                      cyCONDOR, an easy-to-use computational framework covering
                      not only all essential steps of cytometry data analysis but
                      also including an array of downstream functions and tools to
                      expand the biological interpretation of the data. The
                      comprehensive suite of features of cyCONDOR, including
                      guided pre-processing, clustering, dimensionality reduction,
                      and machine learning algorithms, facilitates the seamless
                      integration of cyCONDOR into clinically relevant settings,
                      where scalability and disease classification are paramount
                      for the widespread adoption of HDC in clinical practice.
                      Additionally, the advanced analytical features of cyCONDOR,
                      such as pseudotime analysis and batch integration, provide
                      researchers with the tools to extract deeper insights from
                      their data. We use cyCONDOR on a variety of data from
                      different tissues and technologies demonstrating its
                      versatility to assist the analysis of high-dimensional data
                      from preprocessing to biological interpretation.},
      keywords     = {Software / Flow Cytometry: methods / Computational Biology:
                      methods / Humans / Single-Cell Analysis: methods /
                      Algorithms / Animals / Machine Learning / Mice / Cluster
                      Analysis},
      cin          = {AG Schultze / AG Aschenbrenner / AG Beyer / AG Jessen /
                      PRECISE},
      ddc          = {500},
      cid          = {I:(DE-2719)1013038 / I:(DE-2719)5000082 /
                      I:(DE-2719)1013035 / I:(DE-2719)1011102 /
                      I:(DE-2719)1013031},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354) / 353
                      - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-354 / G:(DE-HGF)POF4-353},
      experiment   = {EXP:(DE-2719)DELCODE-20140101 /
                      EXP:(DE-2719)PRECISE-20190321},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39702306},
      pmc          = {pmc:PMC11659560},
      doi          = {10.1038/s41467-024-55179-w},
      url          = {https://pub.dzne.de/record/274055},
}