Orchestrating single-cell analysis with Bioconductor


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.

Change history

  • 11 December 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


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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

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.

Correspondence to Raphael Gottardo or Stephanie C. Hicks.

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R.G. declares ownership in CellSpace Biosciences.

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Amezquita, R.A., Lun, A.T.L., Becht, E. et al. Orchestrating single-cell analysis with Bioconductor. Nat Methods (2019). https://doi.org/10.1038/s41592-019-0654-x

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