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Orchestrating single-cell analysis with Bioconductor

Nature Methods volume 17, pages 137–145 (2020)Cite this article

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A Publisher Correction to this article was published on 11 December 2019

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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.

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Fig. 1: Number of Bioconductor packages for the analysis of high-throughput sequencing data over ten years.
Fig. 2: Overview of the SingleCellExperiment class.
Fig. 3: Bioconductor workflow for analyzing single-cell data.
Fig. 4: Select visualizations derived from various Bioconductor workflows.

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Acknowledgements

Bioconductor is supported by the National Human Genome Research Institute (NHGRI) and National Cancer Institute (NCI) of the National Institutes of Health (NIH) (grant no. U41HG004059, U24CA180996), the European Union (EU) H2020 Personalizing Health and Care Program Action (contract number 633974) and the SOUND Consortium. In addition, M.M., S.C.H., R.G., W.H., A.T.L.L. and D.R. are supported by the Chan Zuckerberg Initiative (CZI) DAF (grant no. 2018-183201, 2018-183560), an advised fund of Silicon Valley Community Foundation. D.R., W.H., M.M. and S.C.H. are supported by 2019-002443 from the CZI. S.C.H. is supported by the NIH/NHGRI (grant no. R00HG009007). R.A.A. and R.G. are supported by the Integrated Immunotherapy Research Center at Fred Hutch. M.M. is supported by the NCI/NHGRI (grant no. U24CA232979). L.G. is supported by a research fellowship from the German Research Foundation (grant no. GE3023/1-1). L.W. and V.J.C. are supported by the NCI (grant no. U24CA18099). V.J.C. is additionally supported by NCI U01 CA214846 and Chan Zuckerberg Initiative DAF (grant no. 2018-183436). ATLL received support from CRUK (grant no. A17179) and the Wellcome Trust (grant no. WT/108437/Z/15). F.M. is supported by the German Federal Ministry of Education and Research (grant no. BMBF 01EO1003). M.L.S. is supported by the German Network for Bioinformatics Infrastructure (grant no. 031A537B). D.R. is supported by the Programma per Giovani Ricercatori Rita Levi Montalcini from the Italian Ministry of Education, University and Research. H.P. is supported by the NIH Bioconductor grant (no. U41HG004059).

Author information

Author notes

  1. Aaron T. L. Lun

    Present address: Bioinformatics and Computational Biology, Genentech Inc., San Francisco, CA, USA

Authors and Affiliations

  1. Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Robert A. Amezquita, Etienne Becht, Lindsay N. Carpp, Hervé Pagès & Raphael Gottardo

  2. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

    Aaron T. L. Lun

  3. Channing Division of Network Medicine, Brigham And Women’s Hospital, Boston, MA, USA

    Vince J. Carey

  4. Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA

    Ludwig Geistlinger & Levi Waldron

  5. Institute for Implementation Science in Population Health, City University of New York, New York, NY, USA

    Ludwig Geistlinger & Levi Waldron

  6. Center for Thrombosis and Hemostasis, Mainz, Germany

    Federico Marini

  7. Institute of Medical Biostatistics, Epidemiology and Informatics, Mainz, Germany

    Federico Marini

  8. Kennedy Institute of Rheumatology, University of Oxford, Oxford, UK

    Kevin Rue-Albrecht

  9. Department of Statistical Sciences, University of Padua, Padua, Italy

    Davide Risso

  10. Division of Biostatistics and Epidemiology, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, USA

    Davide Risso

  11. Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland

    Charlotte Soneson

  12. SIB Swiss Institute of Bioinformatics, Basel, Switzerland

    Charlotte Soneson

  13. European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany

    Mike L. Smith & Wolfgang Huber

  14. Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

    Martin Morgan

  15. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Stephanie C. Hicks

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  1. Robert A. Amezquita
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Contributions

E.B., V.J.C., L.N.C., L.G., F.M., K.R., D.R., C.S. and L.W. contributed equally to this work. S.C.H. and R.G. contributed equally to the supervision of this work. S.C.H. and R.G. conceptualized the manuscript. R.A.A., A.T.L.L., S.C.H. and R.G. wrote the manuscript with contributions and input from all authors. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Raphael Gottardo or Stephanie C. Hicks.

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Amezquita, R.A., Lun, A.T.L., Becht, E. et al. Orchestrating single-cell analysis with Bioconductor. Nat Methods 17, 137–145 (2020). https://doi.org/10.1038/s41592-019-0654-x

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