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:

Tools for the analysis of high-dimensional single-cell RNA sequencing data

Nature Reviews Nephrology volume 16, pages 408–421 (2020)Cite this article

Subjects

Abstract

Breakthroughs in the development of high-throughput technologies for profiling transcriptomes at the single-cell level have helped biologists to understand the heterogeneity of cell populations, disease states and developmental lineages. However, these single-cell RNA sequencing (scRNA-seq) technologies generate an extraordinary amount of data, which creates analysis and interpretation challenges. Additionally, scRNA-seq datasets often contain technical sources of noise owing to incomplete RNA capture, PCR amplification biases and/or batch effects specific to the patient or sample. If not addressed, this technical noise can bias the analysis and interpretation of the data. In response to these challenges, a suite of computational tools has been developed to process, analyse and visualize scRNA-seq datasets. Although the specific steps of any given scRNA-seq analysis might differ depending on the biological questions being asked, a core workflow is used in most analyses. Typically, raw sequencing reads are processed into a gene expression matrix that is then normalized and scaled to remove technical noise. Next, cells are grouped according to similarities in their patterns of gene expression, which can be summarized in two or three dimensions for visualization on a scatterplot. These data can then be further analysed to provide an in-depth view of the cell types or developmental trajectories in the sample of interest.

Key points

  • As single-cell RNA sequencing datasets increase in scale and complexity, faster and more efficient computational tools for processing and analysis are required.

  • New computational tools that correct technical and batch effects can unlock additional heterogeneity and enable higher-resolution clustering and trajectory inference.

  • Graph-based methods for clustering and trajectory inference allow for the scalable analysis of large single-cell RNA sequencing datasets.

  • Visualization methods can distort the structure of the data and batch correction methods can reduce cell-type resolution; both methods should therefore be used with care and might require specific parameter tuning for each dataset.

  • High-level biological interpretation, such as cell-type annotation, remains challenging and time-consuming — new automated methods, alongside the creation of single-cell reference atlases, promise to address these issues.

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 single-cell RNA sequencing analysis pipeline.
Fig. 2: Pre-processing of single-cell RNA sequencing data.
Fig. 3: Integration of single-cell RNA sequencing data.
Fig. 4: Cell clustering in datasets with discrete cell types.
Fig. 5: Modelling continuous cellular states.
Fig. 6: Local and global structure in a dataset.

Similar content being viewed by others

References

  1. Ramsköld, D., Wang, E. T., Burge, C. B. & Sandberg, R. An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comput. Biol. 5, e1000598 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Potter, S. S. Single-cell RNA sequencing for the study of development, physiology and disease. Nat. Rev. Nephrol. 14, 479–492 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 12, eaam8999 (2018).

    Google Scholar 

  6. Cao, J. et al. Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing. Science 357, 661–667 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lake, B. B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat. Commun. 10, 2832 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Combes, A. N., Zappia, L., Er, P. X., Oshlack, A. & Little, M. H. Single-cell analysis reveals congruence between kidney organoids and human fetal kidney. Genome Med. 11, 3 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Chen, C. et al. Single-cell whole-genome analyses by linear amplification via transposon insertion (LIANTI). Science 356, 189–194 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Cusanovich, D. A. et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Linker, S. M. et al. Combined single-cell profiling of expression and DNA methylation reveals splicing regulation and heterogeneity. Genome Biol. 20, 30 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Gu, C., Liu, S., Wu, Q., Zhang, L. & Guo, F. Integrative single-cell analysis of transcriptome, DNA methylome and chromatin accessibility in mouse oocytes. Cell Res. 29, 110–123 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Amezquita, R. A. et al. Orchestrating single-cell analysis with Bioconductor. Nat. Methods 17, 137–145 (2020). A useful stepwise practical tutorial on how to perform scRNA-seq analysis in the R programming language using the Bioconductor suite of tools.

    CAS  PubMed  Google Scholar 

  18. 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 

  19. Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019). This tutorial discusses scRNA-seq analysis steps using the latest methods developed for each step.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Petukhov, V. et al. Accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments. Genome Biol. 19, 78 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Melsted, P. et al. Modular and efficient pre-processing of single-cell RNA-seq. Preprint at https://doi.org/10.1101/673285 (2019).

  22. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Smith, T. & Sudbery, I. UMI-tools: modelling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  26. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. van den Brink, S. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    Article  CAS  PubMed  Google Scholar 

  28. 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.e4 (2019).

    Article  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  31. 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 

  32. Wagner, A., Regev, A. & Yosef, N. Uncovering the vectors of cellular states with single cell genomics. Nat. Publ. Gr. 34, 1–53 (2016).

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Barkas, N. et al. pagoda2: a package for analyzing and interactively exploring large single-cell RNA-seq datasets. GitHub https://github.com/hms-dbmi/pagoda2 (2018).

  40. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S. & Theis, F. J. DCA: single cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10, 390 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. 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). A benchmark analysis of methods available for selecting over-dispersed genes.

    Article  PubMed Central  Google Scholar 

  45. Leek, J. T. et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010).

    Article  CAS  PubMed  Google Scholar 

  46. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16, 695–698 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020). A benchmark study of methods available for batch correction during analysis of scRNA-seq data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Leek, J. T. Svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 42, e161 (2014).

    Article  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  51. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. 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).

    Article  CAS  PubMed  Google Scholar 

  53. Saelens, W., Cannoodt, R., Todorov HelenaSaeys, Y., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools. Nat. Biotechnol. 37, 547–554 (2019). A benchmark analysis of methods for single-cell trajectory inference.

    Article  CAS  PubMed  Google Scholar 

  54. Bellman, R. On the theory of dynamic programming. Proc. Natl Acad. Sci. USA 38, 716–719 (1952).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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). A benchmark study of methods used for dimensionality reduction of scRNA-seq data.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Abdi, H. & Williams, L. J. Principal component analysis. Chemom. Intell. Lab. Syst. 2, 433–459 (2010).

    Google Scholar 

  57. Pierson, E. & Yau, C. ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 16, 241 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Lee, D. D. & Seung, H. S. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999).

    Article  CAS  PubMed  Google Scholar 

  60. Lin, X. & Boutros, P. C. Optimization and expansion of non-negative matrix factorization. BMC Bioinformatics 21, 7 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Roweis, S. T. & Saul, L. K. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000).

    Article  CAS  PubMed  Google Scholar 

  62. Angerer, P. et al. destiny: diffusion maps for large-scale single-cell data in R. Bioinformatics 32, 1241–1243 (2015).

    Article  PubMed  CAS  Google Scholar 

  63. Welch, J. D., Hartemink, A. J. & Prins, J. F. SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 17, 106 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).

    Article  CAS  PubMed  Google Scholar 

  65. Deng, Y., Bao, F., Dai, Q., Wu, L. F. & Altschuler, S. J. Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning. Nat. Methods 16, 311–314 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  67. Wagner, F., Yan, Y. & Yanai, I. K-nearest neighbor smoothing for single-cell RNA-seq data. Preprint at https://doi.org/10.1101/217737 (2017).

  68. van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Lin, P., Troup, M. & Ho, J. W. K. CIDR: ultrafast and accurate clustering through imputation for single cell RNA-seq data. Genome Biol. 18, 59 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. Andrews, T. S. & Hemberg, M. False signals induced by single-cell imputation. F1000Res. 7, 1740 (2019).

    Article  PubMed Central  Google Scholar 

  73. Lloyd, S. P. Least squares quantization in PCM. IEEE Trans. Inf. Theory 28, 129–137 (1982).

    Article  Google Scholar 

  74. Žurauskiene, J. & Yau, C. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinformatics 17, 140 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  76. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e22 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Duò, A., Robinson, M. D. & Soneson, C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Res. 7, 1141 (2018). A benchmark analysis of methods available for clustering in scRNA-seq data analysis.

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Li, H. et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49, 708–718 (2017).

    Article  CAS  PubMed  Google Scholar 

  82. Combes, A. N. et al. Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk. Development 146, dev178673 (2019).

    Article  PubMed  Google Scholar 

  83. Qiu, X. et al. Single-cell mRNA quantification and differential analysis with Census. Nat. Methods 14, 309–315 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Wu, Y., Tamayo, P. & Zhang, K. Visualizing and interpreting single-cell gene expression datasets with similarity weighted nonnegative embedding. Cell Syst. 7, 656–666.e4 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Google Scholar 

  89. McInnes, L. & Healy, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  90. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2018).

    Article  CAS  Google Scholar 

  91. Wattenberg, M., Viegas, F. & Johnson, I. How to use t-SNE effectively. Distill https://doi.org/10.23915/distill.00002 (2016).

    Article  Google Scholar 

  92. van der Maaten, L. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014).

    Google Scholar 

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

  94. Kobak, D. & Berens, P. The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10, 5416 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  95. Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Ding, J., Condon, A. & Shah, S. P. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat. Commun. 9, 2002 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Abdelaal, T. et al. A comparison of automatic cell identification methods for single-cell RNA-sequencing data. Genome Biol. 20, 194 (2019). A benchmark study of methods available for automated cell-type classification in scRNA-seq data.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  99. Lun, A. T. L. & Marioni, J. C. Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data. Biostatistics 18, 451–464 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  100. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Suykens, J. A. K. & Vandewalle, J. Indefinite kernels in least squares support vector machines and principal component analysis. Neural Process. Lett. 43, 162–172 (2017).

    Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2017).

    Article  CAS  Google Scholar 

  104. Alquicira-Hernandez, J., Sathe, A., Ji, H. P., Nguyen, Q. & Powell, J. E. ScPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 20, 264 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  106. Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Denisenko, E. et al. Systematic bias assessment in solid tissue 10x scRNA-seq workflows. Preprint at https://doi.org/10.1101/832444 (2019).

  108. Lake, B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Krishnaswami, S. R. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat. Protoc. 11, 499–524 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors were supported by NIH grants U01MH098977, R01HL123755, U54HL145608, UH3DK114933 and R01HG009285.

Author information

Authors and Affiliations

  1. Department of Bioengineering, University of California at San Diego, La Jolla, CA, USA

    Yan Wu & Kun Zhang

Authors
  1. Yan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors researched data for the article, wrote the manuscript, made substantial contributions to discussions of the content and reviewed or edited the manuscript before submission.

Corresponding author

Correspondence to Kun Zhang.

Ethics declarations

Competing interests

Y.W. declares no competing interests. K.Z. is a co-founder, equity holder, scientific advisory board member and paid consultant of Singlera Genomics, which has no commercial interests related to this article.

Additional information

Peer review information

Nature Reviews Nephrology thanks B. J. Aronow and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

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

Related links

Broad Institute online single-cell data browser: https://portals.broadinstitute.org/single_cell

EMBL-EBI online single-cell data browser: https://www.ebi.ac.uk/gxa/sc/home

UCSC online single-cell data browser: https://cells.ucsc.edu/

Glossary

FASTQ file

A text file that stores DNA sequences and their associated quality metrics and metadata; a single sequence in a FASTQ file is called a ‘read’.

Counts matrix

An integer matrix (that is, numerical data arranged in a set of columns and rows) in which the columns typically correspond to cells, whereas the rows correspond to genes; each entry represents the number of molecules of that gene expressed in that cell.

Total counts

The total number of reads or UMIs in a given cell.

Size factor

An estimate of how much variation in sequencing depth or RNA capture efficiency affects the overall quantification of gene expression in a cell.

Over-dispersed genes

Genes that show a greater than expected variance between cells given their average expression, which suggests that they are expressed in a cell-type-specific manner.

Regression model

A model that compares the relationship between two variables. In the context of single-cell RNA sequencing, regression can assess relationships between observed gene expression, and technical and/or biological factors.

Mutual nearest neighbours

(MNNs). Cells from different batches that belong to each other’s set of k-nearest neighbours (that is, cells with the most similar gene expression patterns).

Dimensionality reduction

Summarizing a large set of variables with a smaller set of variables, while retaining as much information as possible.

Embedding

The set of variables that remains after running some form of dimensional reduction.

Dropout

The absence of a detectable gene or transcript in a cell.

Classification

A machine learning task in which an algorithm learns the relevant features that distinguish the different classes of a training dataset to predict the classes of an unknown test dataset.

Cell hashing

A technique that attaches unique molecular barcodes to multiple batches of samples for pooling and processing in one batch, which not only improves the experimental throughput but also reduces technical batch differences.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Zhang, K. Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol 16, 408–421 (2020). https://doi.org/10.1038/s41581-020-0262-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41581-020-0262-0

This article is cited by

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