Orchestrating single-cell analysis with Bioconductor
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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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Existing computational best practices and tool recommendations for scRNA-seq are outdated or incomplete.
“although computational best practices and tool recommendations have previously been outlined for scRNA-seq2,5,6,17, they are either outdated or incomplete.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [2]
Removing cells with low total ADT count may remove cell types that do not express a specific set of proteins or express only a few.
“however, removing cells with a low total ADT count may remove cell types that do not express a specific set of proteins or express only a few2.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [2]
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BackgroundMethodInfluentialDifferential abundance testing on single-cell data using k-nearest neighbor graphs
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“We employ both SingleCellExperiment (version 1.30.1) [53] and Seurat (version 5.3.1) [54] throughout this study.”
Influential
- Authors
- Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., Waldron, L., Pagès, H., Smith, M. L., Huber, W., Morgan, M., Gottardo, R., & Hicks, S. C.
- Affiliations
- Fred Hutch Cancer Center (US)
- Genentech (US)
- Brigham and Women's Hospital (US)
- City University of New York (US)
- University of Padua (IT)
- SIB Swiss Institute of Bioinformatics (CH)
- European Molecular Biology Laboratory (DE)
- Roswell Park Comprehensive Cancer Center (US)
- Johns Hopkins University (US)
- Corresponding author
- Hicks, Stephanie C.
- ORCID
- Venue
- Nature Methods
- Published
- 2019-12-02
- Volume
- 17
- Issue
- 2
- Pages
- 137-145
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC7358058
- OpenAlex
- W2989807288
- Cited by
- 945
- References
- 185
- Citations by year
- 2026: 129 · 2025: 179 · 2024: 192 · 2023: 185 · 2022: 113 · 2021: 97 · 2020: 46 · 2019: 4
- FWCI
- 5.74
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-12-11)
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Padua Research Archive (University of Padova) (submittedVersion, cc-by-nc-nd)
- Funders
- Silicon Valley Community Foundation (2018-183201)
- Fred Hutchinson Cancer Research Center
- Wellcome Trust (WT/108437/Z/15, 108437)
- Chan Zuckerberg Initiative
- Cancer Research UK (A17179)
- European Commission (H2020, 633974)
- Deutsche Forschungsgemeinschaft (GE3023/1-1, 031A537B, 01EO1003)
- Bundesministerium für Bildung und Forschung (BMBF 01EO1003, 01EO1003, 031A537B)
- German Network for Bioinformatics Infrastructure (031 A537B)
- National Institutes of Health (01EO1003, H2020, 5u41hg004059-15)
- Horizon 2020 Framework Programme (633974)
- National Human Genome Research Institute (U41-HG004059, R00HG009007)
- National Cancer Institute (U24CA180996, U24CA232979, U01CA214846)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell analysis, Bioconductor, R package, open source software, single-cell data visualization, single-cell RNA sequencing
- MeSH terms
- Software, Genome, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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