Orchestrating single-cell analysis with Bioconductor.
Journal article

Orchestrating single-cell analysis with Bioconductor.

  • Amezquita RA Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  • Lun ATL Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
  • Becht E Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  • Carey VJ Channing Division of Network Medicine, Brigham And Women's Hospital, Boston, MA, USA.
  • Carpp LN Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  • Geistlinger L Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA.
  • Marini F Center for Thrombosis and Hemostasis, Mainz, Germany.
  • Rue-Albrecht K Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
  • Risso D Department of Statistical Sciences, University of Padua, Padua, Italy.
  • Soneson C Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
  • Waldron L Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA.
  • Pagès H Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
  • Smith ML European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
  • Huber W European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
  • Morgan M Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
  • Gottardo R Fred Hutchinson Cancer Research Center, Seattle, WA, USA. rgottard@fredhutch.org.
  • Hicks SC Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. shicks19@jhu.edu.
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  • 2019-12-04
Published in:
  • Nature methods. - 2020
English Recent technological advancements have enabled the profiling of a large number of genome-wide features in individual cells. However, single-cell data present unique challenges that require the development of specialized methods and software infrastructure to successfully derive biological insights. The Bioconductor project has rapidly grown to meet these demands, hosting community-developed open-source software distributed as R packages. Featuring state-of-the-art computational methods, standardized data infrastructure and interactive data visualization tools, we present an overview and online book (https://osca.bioconductor.org) of single-cell methods for prospective users.
Language
  • English
Open access status
green
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Persistent URL
https://sonar.ch/global/documents/93801
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