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Nat Methods. 2020 Feb;17(2):137-145. doi: 10.1038/s41592-019-0654-x. Epub 2019 Dec 2.

Orchestrating single-cell analysis with Bioconductor.

Author information

1
Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
2
Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
3
Bioinformatics and Computational Biology, Genentech Inc., San Francisco, CA, USA.
4
Channing Division of Network Medicine, Brigham And Women's Hospital, Boston, MA, USA.
5
Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA.
6
Institute for Implementation Science in Population Health, City University of New York, New York, NY, USA.
7
Center for Thrombosis and Hemostasis, Mainz, Germany.
8
Institute of Medical Biostatistics, Epidemiology and Informatics, Mainz, Germany.
9
Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK.
10
Department of Statistical Sciences, University of Padua, Padua, Italy.
11
Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA.
12
Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
13
SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
14
European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
15
Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.
16
Fred Hutchinson Cancer Research Center, Seattle, WA, USA. rgottard@fredhutch.org.
17
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. shicks19@jhu.edu.

Abstract

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.

PMID:
31792435
DOI:
10.1038/s41592-019-0654-x

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