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Orchestrating single-cell analysis with Bioconductor

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

This article has been updated


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 ( 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.


  1. 1.

    Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Robinson, M. D. et al. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  Google Scholar 

  3. 3.

    Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Aryee, M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363–1369 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Serratì, S. et al. Next-generation sequencing: advances and applications in cancer diagnosis. Onco. Targets Ther. 9, 7355–7365 (2016).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Nakato, R. & Shirahige, K. Recent advances in ChIP-seq analysis: from quality management to whole-genome annotation. Brief. Bioinform. 18, 279–290 (2017).

    CAS  PubMed  Google Scholar 

  9. 9.

    Kukurba, K. R. & Montgomery, S. B. RNA sequencing and analysis. Cold Spring Harb. Protoc. 2015, 951–969 (2015).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C. & Teichmann, S. A. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).

    CAS  PubMed  Google Scholar 

  11. 11.

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–401 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Tirosh., I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Karaayvaz, M. et al. Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nat. Commun. 9, 3588 (2018).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Jean Fan. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217–1227 (2018).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Levitin, H. M., Yuan, J. & Sims, P. A. Single-cell transcriptomic analysis of tumor heterogeneity. Trends Cancer 4, 264–268 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Paulson, K. G. et al. Acquired cancer resistance to combination immunotherapy from transcriptional loss of class I HLA. Nat. Commun. 9, 3868 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Zeisel, A. et al. Brain structure: cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    CAS  PubMed  Google Scholar 

  18. 18.

    Deng, Q., Ramsköld, D., Reinius, B. & Sandberg, R. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

    CAS  PubMed  Google Scholar 

  19. 19.

    Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).

    CAS  PubMed  Google Scholar 

  20. 20.

    Cannoodt, R., Saelens, W. & Saeys, Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46, 2496–2506 (2016).

    CAS  PubMed  Google Scholar 

  21. 21.

    Regev, A. et al. The Human cell atlas. eLife 6, e27041 (2017).

    PubMed  PubMed Central  Google Scholar 

  22. 22.

    Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The human cell atlas: from vision to reality. Nature 550, 451–453 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 173, 1307 (2018).

    CAS  PubMed  Google Scholar 

  24. 24.

    McDavid, A. et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics 29, 461–467 (2013).

    CAS  PubMed  Google Scholar 

  25. 25.

    Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19, 562–578 (2018).

    PubMed  Google Scholar 

  26. 26.

    Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

    PubMed  Google Scholar 

  29. 29.

    Ji, Z. & Ji, H. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117 (2016).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J.-P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018).

    PubMed  PubMed Central  Google Scholar 

  31. 31.

    Chambers, J. M. Object-oriented programming, functional programming and R. Stat. Sci. 29, 167–180 (2014).

    Google Scholar 

  32. 32.

    Tian, L. et al. scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput. Biol. 14, e1006361 (2018).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Wang, Z., Hu, J., Johnson, W. E. & Campbell, J. D. scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data. BMC Bioinform. 20, 222 (2019).

    Google Scholar 

  34. 34.

    Lun, AaronT. L. et al. Emptydrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Melsted, P. et al. Modular and efficient pre-processing of single-cell rna-seq. Preprint at bioRxiv (2019).

  37. 37.

    Srivastava, A., Malik, L., Smith, T., Sudbery, I. & Patro, R. Alevin efficiently estimates accurate gene abundances from dscRNA-seq data. Genome Biol. 20, 65 (2019).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Bais, A. S. & Kostka, D. scds: computational annotation of doublets in single cell RNA sequencing data. Bioinformatics (2019).

  40. 40.

    Ilicic, T. et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Vallejos, C. A., Risso, D. R., Scialdone, A., Dudoit, S. & Marioni, J. C. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14, 565–571 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Vallejos, C. A., Richardson, S. & Marioni, J. C. Beyond comparisons of means: understanding changes in gene expression at the single-cell level. Genome Biol. 17, 70 (2016).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Huang, M. et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat. Methods 15, 539–542 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Li, W. V. & Li, J. L. An accurate and robust imputation method scImpute for singlecell RNA-seq data. Nat. Commun. 9, 997 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

    Svensson, V. Droplet scRNA-seq is not zero-inflated. Preprint bioRxiv (2019).

  47. 47.

    Vieth, B., Ziegenhain, C., Parekh, S., Enard, W. & Hellmann, I. powsimR: power analysis for bulk and single cell RNA-seq experiments. Bioinformatics 33, 3486–3488 (2017).

    CAS  PubMed  Google Scholar 

  48. 48.

    Townes, F. W., Hicks, S. C., Aryee, M. J. & Irizarry, R. A. Feature selection and dimension reduction for single cell RNA-seq based on a multinomial model. Preprint at bioRxiv (2019).

  49. 49.

    Andrews, T. & Hemberg, M. False signals induced by single-cell imputation. F1000Res. (2019).

    PubMed Central  Google Scholar 

  50. 50.

    Andrews, T. & Hemberg, M. M3Drop: Dropout-based feature selection for scRNASeq. Bioinformatics 35, 2865–2867 (2019).

    PubMed  Google Scholar 

  51. 51.

    Yip, S. H., Sham, P. C. & Wang, J. Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Brief. Bioinform. 20, 1583–1589 (2018).

    PubMed Central  Google Scholar 

  52. 52.

    Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).

    PubMed  PubMed Central  Google Scholar 

  53. 53.

    van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  54. 54.

    Melville, J., McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv (2018).

  55. 55.

    Angerer., P. et al. Destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2016).

    CAS  PubMed  Google Scholar 

  56. 56.

    Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Lin, Y. et al. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proc. Natl. Acad. Sci. USA 116, 9775–9784 (2019).

    CAS  PubMed  Google Scholar 

  58. 58.

    Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).

    CAS  PubMed  Google Scholar 

  59. 59.

    Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Wang, B., Zhu, J., Pierson, E., Ramazzotti, D. & Batzoglou, S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat. Methods 14, 414–416 (2017).

    CAS  PubMed  Google Scholar 

  61. 61.

    Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Risso, D. et al. clusterExperiment and RSEC: a bioconductor package and framework for clustering of singlecell and other large gene expression datasets. PLoS Comp. Biol. 14, e1006378–16 (2018).

    Google Scholar 

  63. 63.

    Van den Berge, K. et al. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol. 19, 24 (2018).

    PubMed  PubMed Central  Google Scholar 

  64. 64.

    Korthauer, K. D. et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol. 17, 222 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018).

    CAS  PubMed  Google Scholar 

  66. 66.

    Wang, T., Li, B., Nelson, C. E. & Nabavi, S. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinform. 20, 40 (2019).

    Google Scholar 

  67. 67.

    Crowell, H. L. et al. On the discovery of population-specific state transitions from multi-sample multi-condition single-cell RNA sequencing data. Preprint at bioRxiv (2019).

  68. 68.

    Andrews, T. S. & Hemberg, M. Identifying cell populations with scRNASeq. Mol. Asp. Med. 59, 114–122 (2018).

    CAS  Google Scholar 

  69. 69.

    Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Campbell, K. R. & Yau, C. switchde: inference of switch-like differential expression along single-cell trajectories. Bioinformatics 33, 1241–1242 (2017).

    CAS  PubMed  Google Scholar 

  71. 71.

    Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    duVerle, D. A., Yotsukura, S., Nomura, S., Aburatani, H. & Tsuda, K. CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data. BMC Bioinform. 17, 363 (2016).

    Google Scholar 

  73. 73.

    Campbell, K. R. & Yau, C. Probabilistic modeling of bifurcations in single-cell gene expression data using a bayesian mixture of factor analyzers. Wellcome Open Res. 2, 19 (2017).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547 (2019).

    CAS  Google Scholar 

  75. 75.

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  Google Scholar 

  76. 76.

    Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, 353–361 (2017).

    Google Scholar 

  77. 77.

    Fabregat, A. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 44, 481–487 (2015).

    Google Scholar 

  78. 78.

    Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Geistlinger, L., Csaba, G. & Zimmer, R. Bioconductor’s EnrichmentBrowser: seamless navigation through combined results of set and network-based enrichment analysis. BMC Bioinform. 17, 45 (2016).

    Google Scholar 

  80. 80.

    Alhamdoosh, M. et al. Combining multiple tools outperforms individual methods in gene set enrichment analyses. Bioinformatics 33, 414–424 (2017).

    CAS  PubMed  Google Scholar 

  81. 81.

    Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Buettner, F., Pratanwanich, N., McCarthy, D. J., Marioni, J. C. & Stegle, O. fscLVM: scalable and versatile factor analysis for single-cell RNA-seq. Genome Biol. 18, 212 (2017).

    PubMed  PubMed Central  Google Scholar 

  83. 83.

    Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Zappia, L., Phipson, B. & Oshlack, A. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 18, 174 (2017).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Kimes, P. K. & Reyes, A. Reproducible and replicable comparisons using SummarizedBenchmark. Bioinformatics 35, 137–139 (2019).

    CAS  PubMed  Google Scholar 

  86. 86.

    Tian, L. et al. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat. Methods 16, 479–487 (2019).

    CAS  PubMed  Google Scholar 

  87. 87.

    Rue-Albrecht, K., Marini, F., Soneson, C. & Lun, A. T. L. iSEE: interactive SummarizedExperiment Explorer. F1000Res. 7, 741 (2018).

    PubMed  PubMed Central  Google Scholar 

  88. 88.

    Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    CAS  PubMed  Google Scholar 

  89. 89.

    Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Macaulay, IainC. et al. Separation and parallel sequencing of the genomes and transcriptomes of single cells using GT-seq. Nat. Protoc. 11, 2081–2103 (2016).

    CAS  PubMed  Google Scholar 

  91. 91.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Shahi, P., Kim, S. C., Haliburton, J. R., Gartner, Z. J. & Abate, A. R. Abseq: ultrahighthroughput single cell protein profiling with droplet microfluidic barcoding. Sci. Rep. 7, 44447 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).

    PubMed  PubMed Central  Google Scholar 

  96. 96.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Eddelbuettel, D. & François, R. Rcpp: seamless R and C++ integration. J. Stat. Softw. 40, 1–18 (2011).

    Google Scholar 

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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).

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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.

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Correspondence to Raphael Gottardo or Stephanie C. Hicks.

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R.G. declares ownership in CellSpace Biosciences.

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Peer review information Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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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).

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