Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data

Nature Protocols volume 16, pages 1–9 (2021)Cite this article

Subjects

Abstract

Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Here we present an overview of the computational workflow involved in processing scRNA-seq data. We discuss some of the most common tasks and the tools available for addressing central biological questions. In this article and our companion website (https://scrnaseq-course.cog.sanger.ac.uk/website/index.html), we provide guidelines regarding best practices for performing computational analyses. This tutorial provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the workflow.

Similar content being viewed by others

References

  1. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    CAS  PubMed  Google Scholar 

  2. Svensson, V. et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14, 381–387 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643 (2017).

    CAS  PubMed  Google Scholar 

  4. Han, X. et al. Mapping the mouse cell atlas by microwell-seq. Cell 172, 1091–1107 (2018).

    CAS  PubMed  Google Scholar 

  5. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    CAS  PubMed  Google Scholar 

  7. Hagemann-Jensen, M. et al. Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020).

  8. Zhang, X. et al. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems. Mol. Cell 73, 130–142 (2019).

  9. Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

    CAS  PubMed  Google Scholar 

  10. Sarkar, A. K. & Stephens, M. Separating measurement and expression models clarifies confusion in single cell RNA-seq analysis. Preprint at https://www.biorxiv.org/content/10.1101/2020.04.07.030007v1 (2020).

  11. 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 

  12. Lun, A. T. 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 

  13. 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 

  14. Amezquita, R. A. et al. Orchestrating single-cell analysis with bioconductor. Nat. Methods 17, 137–145 (2020).

    CAS  PubMed  Google Scholar 

  15. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).

  16. Lareau, C. A., Ma, S., Duarte, F. M. & Buenrostro, J. D. Inference and effects of barcode multiplets in droplet-based single-cell assays. Nat. Commun. 11, 866 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Bais, A. S. & Kostka, D. scds: computational annotation of doublets in single-cell RNA sequencing data. Bioinformatics 36, 1150–1158 (2020).

    CAS  PubMed  Google Scholar 

  19. Marinov, G. K. et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 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 

  22. Lun, A. T. L., Calero-Nieto, F. J., Haim-Vilmovsky, L., Göttgens, B. & Marioni, J. C. Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data. Genome Res. 27, 1795–1806 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Bacher, R. et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods 14, 584–586 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

  25. Tang, W. et al. bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data. Bioinformatics 36, 1174–1181 (2020).

    CAS  PubMed  Google Scholar 

  26. Baran-Gale, J., Chandra, T. & Kirschner, K. Experimental design for single-cell RNA sequencing. Brief Funct. Genomics 17, 233–239 (2018).

    CAS  PubMed  Google Scholar 

  27. Stein, C. K. et al. Removing batch effects from purified plasma cell gene expression microarrays with modified ComBat. BMC Bioinformatics 16, 63 (2015).

    PubMed  PubMed Central  Google Scholar 

  28. 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 

  29. 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 

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

    PubMed  PubMed Central  Google Scholar 

  31. Gong, W., Kwak, I.-Y., Pota, P., Koyano-Nakagawa, N. & Garry, D. J. DrImpute: imputing dropout events in single cell RNA sequencing data. BMC Bioinformatics 19, 220 (2018).

    PubMed  PubMed Central  Google Scholar 

  32. 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 

  33. Andrews, T. S. & Hemberg, M. False signals induced by single-cell imputation. F1000Research 7, 1740 (2018).

    PubMed  Google Scholar 

  34. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Wang, J. et al. Data denoising with transfer learning in single-cell transcriptomics. Nat. Methods 16, 875–878 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Elyanow, R., Dumitrascu, B., Engelhardt, B. E. & Raphael, B. J. netNMF-sc: leveraging gene–gene interactions for imputation and dimensionality reduction in single-cell expression analysis. Genome Res. 30, 195–204 (2020).

  38. Scialdone, A. et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015).

    CAS  PubMed  Google Scholar 

  39. 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 

  40. Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014).

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  42. Yip, S. H., Sham, P. C. & Wang, J. Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data. Brief Bioinformatics 20, 1583–1589 (2019).

    CAS  PubMed  Google Scholar 

  43. Svensson, V. Droplet scRNA-seq is not zero-inflated. Nat. Biotechnol. 38, 147–150 (2020).

    CAS  PubMed  Google Scholar 

  44. Jiang, L., Chen, H., Pinello, L. & Yuan, G.-C. GiniClust: detecting rare cell types from single-cell gene expression data with Gini index. Genome Biol. 17, 144 (2016).

    PubMed  PubMed Central  Google Scholar 

  45. Sun, S., Zhu, J., Ma, Y. & Zhou, X. Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol. 20, 269 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Peres-Neto, P. R., Jackson, D. A. & Somers, K. M. How many principal components? stopping rules for determining the number of non-trivial axes revisited. Comput. Stat. Data Anal. 49, 974–997 (2005).

    Google Scholar 

  47. McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Software 3, 861 (2018).

    Google Scholar 

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

    Google Scholar 

  49. Kobak, D. & Linderman, G. C. UMAP does not preserve global structure any better than t-SNE when using the same initialization. Preprint at https://www.biorxiv.org/content/10.1101/2019.12.19.877522v1 (2019).

  50. Hartigan, J. A. & Wong, M. A. Algorithm AS 136: a k-means clustering algorithm. Appl. Stat. 28, 100–108 (1979).

    Google Scholar 

  51. 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 

  52. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).

    Google Scholar 

  53. Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 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 

  55. Duò, A., Robinson, M. D. & Soneson, C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Research 7, 1141 (2018).

    PubMed  Google Scholar 

  56. Freytag, S., Tian, L., Lönnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10× Genomics single-cell RNA-sequencing data. F1000Research 7, 1297 (2018).

    PubMed  PubMed Central  Google Scholar 

  57. 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 

  58. Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience 7, giy083 (2018).

  59. 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 

  60. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    CAS  PubMed  Google Scholar 

  61. Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    CAS  PubMed  Google Scholar 

  62. 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 

  63. Chen, J., Schlitzer, A., Chakarov, S., Ginhoux, F. & Poidinger, M. Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development. Nat. Commun. 7, 11988 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    PubMed  PubMed Central  Google Scholar 

  65. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0591-3 (2020).

  66. Zhang, J. M., Kamath, G. M. & Tse, D. N. Valid post-clustering differential analysis for single-cell RNA-seq. Cell Syst. 9, 383–392 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 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 

  68. 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 

  69. Vieth, B., Parekh, S., Ziegenhain, C., Enard, W. & Hellmann, I. A systematic evaluation of single cell RNA-seq analysis pipelines. Nat. Commun. 10, 4667 (2019).

    PubMed  PubMed Central  Google Scholar 

  70. 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 

  71. Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    PubMed  PubMed Central  Google Scholar 

  72. 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 https://www.biorxiv.org/content/10.1101/713412v1 (2019).

  73. Baran, Y. et al. MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol. 20, 206 (2019).

    PubMed  PubMed Central  Google Scholar 

  74. 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 

  75. Abdelaal, T. et al. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol. 20, 194 (2019).

    PubMed  PubMed Central  Google Scholar 

  76. Crow, M., Paul, A., Ballouz, S., Huang, Z. J. & Gillis, J. Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nat. Commun. 9, 884 (2018).

    PubMed  PubMed Central  Google Scholar 

  77. Macaulay, I. C., Ponting, C. P. & Voet, T. Single-cell multiomics: multiple measurements from single cells. Trends Genet. 33, 155–168 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    CAS  PubMed  Google Scholar 

  79. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    PubMed  Google Scholar 

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

  82. Brunet Avalos, C., Maier, G. L., Bruggmann, R. & Sprecher, S. G. Single cell transcriptome atlas of the Drosophila larval brain. eLife 8, e50354 (2019).

  83. Tabula Muris Consortium. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Google Scholar 

  84. 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 

  85. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Pliner, H. A., Shendure, J. & Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16, 983–986 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  88. Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019).

    PubMed  PubMed Central  Google Scholar 

  89. Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    PubMed  PubMed Central  Google Scholar 

  90. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank J. Eliasova for help with Fig. 1. We also thank S. Ballerau, M. Büttner, M. Do Nascimentoo Lopes Primo, J. Lee, R. Lyu, E. Madissoon, R. Martinez Nunez, S. Y. Müller, K. Polanski, P. Qiao and J. Westoby for their contributions to teaching the course and developing the material.

Author information

Authors and Affiliations

  1. Wellcome Sanger Institute, Hinxton, UK

    Tallulah S. Andrews, Vladimir Yu Kiselev & Martin Hemberg

  2. Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Victoria, Australia

    Davis McCarthy

  3. Melbourne Integrative Genomics, Faculty of Science, University of Melbourne, Melbourne, Victoria, Australia

    Davis McCarthy

Authors
  1. Tallulah S. Andrews
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladimir Yu Kiselev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Davis McCarthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Hemberg
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

T.S.A., V.Y.K., D.M. and M.H. planned the tutorial and wrote the text together.

Corresponding author

Correspondence to Martin Hemberg.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Andrews, T.S., Kiselev, V.Y., McCarthy, D. et al. Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data. Nat Protoc 16, 1–9 (2021). https://doi.org/10.1038/s41596-020-00409-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-020-00409-w

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing