Skip to main content
SpringerLink
Log in

Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview

  • Protocol
  • First Online:
RNA Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2284))

Abstract

Thanks to innovative sample-preparation and sequencing technologies, gene expression in individual cells can now be measured for thousands of cells in a single experiment. Since its introduction, single-cell RNA sequencing (scRNA-seq) approaches have revolutionized the genomics field as they created unprecedented opportunities for resolving cell heterogeneity by exploring gene expression profiles at a single-cell resolution. However, the rapidly evolving field of scRNA-seq invoked the emergence of various analytics approaches aimed to maximize the full potential of this novel strategy. Unlike population-based RNA sequencing approaches, scRNA seq necessitates comprehensive computational tools to address high data complexity and keep up with the emerging single-cell associated challenges. Despite the vast number of analytical methods, a universal standardization is lacking. While this reflects the fields’ immaturity, it may also encumber a newcomer to blend in.

In this review, we aim to bridge over the abovementioned hurdle and propose four ready-to-use pipelines for scRNA-seq analysis easily accessible by a newcomer, that could fit various biological data types. Here we provide an overview of the currently available single-cell technologies for cell isolation and library preparation and a step by step guide that covers the entire canonical analytic workflow to analyse scRNA-seq data including read mapping, quality controls, gene expression quantification, normalization, feature selection, dimensionality reduction, and cell clustering useful for trajectory inference and differential expression. Such workflow guidelines will escort novices as well as expert users in the analysis of complex scRNA-seq datasets, thus further expanding the research potential of single-cell approaches in basic science, and envisaging its future implementation as best practice in the field.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang ET, Sandberg R, Luo S et al (2008) Alternative isoform regulation in human tissue transcriptomes. Nature 456:470–476

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Cacchiarelli D, Trapnell C, Ziller MJ et al (2015) Integrative analyses of human reprogramming reveal dynamic nature of induced pluripotency. Cell 162:412–424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Mitelman F, Johansson B, Mertens F (2007) The impact of translocations and gene fusions on cancer causation. Nat Rev Cancer 7:233–245

    Article  CAS  PubMed  Google Scholar 

  4. Trapnell C (2015) Defining cell types and states with single-cell genomics. Genome Res 25:1491–1498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hedlund E, Deng Q (2018) Single-cell RNA sequencing: technical advancements and biological applications. Mol Asp Med 59:36–46

    Article  CAS  Google Scholar 

  6. Hwang B, Lee JH, Bang D (2018) Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 50(96)

    Google Scholar 

  7. Supplementary Table 1: https://github.com/gambalab/scRNAseq_chapter/blob/master/tables/table1.xlsx

  8. Macosko EZ, Basu A, Satija R et al (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–1214

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Klein AM, Mazutis L, Akartuna I et al (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zheng GXY, Terry JM, Belgrader P et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Plasschaert LW, Žilionis R, Choo-Wing R et al (2018) A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature 560:377–381

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Suo S, Zhu Q, Saadatpour A et al (2018) Revealing the critical regulators of cell identity in the mouse cell atlas. Cell Rep 25:1436–1445.e3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Velasco S, Kedaigle AJ, Simmons SK et al (2019) Individual brain organoids reproducibly form cell diversity of the human cerebral cortex. Nature 570:523–527

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Fischer DS, Fiedler AK, Kernfeld EM et al (2019) Inferring population dynamics from single-cell RNA-sequencing time series data. Nat Biotechnol 37:461–468

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Liu Z, Wang L, Welch JD et al (2017) Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte. Nature 551:100–104

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Cacchiarelli D, Qiu X, Srivatsan S et al (2018) Aligning single-cell developmental and reprogramming trajectories identifies molecular determinants of myogenic reprogramming outcome. Cell Syst 7:258–268.e3

    Article  CAS  PubMed  Google Scholar 

  17. van Dijk D, Sharma R, Nainys J et al (2018) Recovering gene interactions from single-cell data using data diffusion. Cell 174:716–729.e27

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Hayashi T, Ozaki H, Sasagawa Y et al (2018) Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs. Nat Commun 9:619

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Savas P, Virassamy B, Ye C et al (2018) Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med 24:986–993

    Article  CAS  PubMed  Google Scholar 

  20. Moghe I, Loupy A, Solez K (2018) The human cell atlas project by the numbers: relationship to the Banff classification. Am. J. Transplant 18:1830

    Article  PubMed  Google Scholar 

  21. Ziegenhain C, Vieth B, Parekh S et al (2017) Comparative analysis of single-cell RNA sequencing methods. Mol Cell 65:631–643.e4

    Article  CAS  PubMed  Google Scholar 

  22. Senabouth A, Andersen S, Shi Q et al (2020) Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing. NAR Genom Bioinform 2. https://doi.org/10.1093/nargab/lqaa034

  23. Rosenberg AB, Roco CM, Muscat RA et al (2018) Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:176–182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tasic B, Yao Z, Graybuck LT et al (2018) Shared and distinct transcriptomic cell types across neocortical areas. Nature 563:72–78

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Guillaumet-Adkins A, Rodríguez-Esteban G, Mereu E et al (2017) Single-cell transcriptome conservation in cryopreserved cells and tissues. Genome Biol 18:45

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Wohnhaas CT, Leparc GG, Fernandez-Albert F et al (2019) DMSO cryopreservation is the method of choice to preserve cells for droplet-based single-cell RNA sequencing. Sci Rep 9:10699

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  28. Salomon R, Kaczorowski D, Valdes-Mora F et al (2019) Droplet-based single cell RNAseq tools: a practical guide. Lab Chip 19:1706–1727

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  30. Abate AR, Chen C-H, Agresti JJ, Weitz DA (2009) Beating Poisson encapsulation statistics using close-packed ordering. Lab on a Chip 9:2628

    Article  CAS  PubMed  Google Scholar 

  31. Zhang X, Li T, Liu F et al (2019) Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-Seq systems. Mol Cell 73:130–142.e5

    Article  PubMed  Google Scholar 

  32. Brazovskaja A, Treutlein B, Camp JG (2019) High-throughput single-cell transcriptomics on organoids. Curr Opin Biotechnol 55:167–171

    Article  CAS  PubMed  Google Scholar 

  33. Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16:133–145

    Article  CAS  PubMed  Google Scholar 

  34. Haque A, Engel J, Teichmann SA, Lönnberg T (2017) A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Medicine 9

    Google Scholar 

  35. Lähnemann D, Köster J, Szczurek E et al (2020) Eleven grand challenges in single-cell data science. Genome Biol 21:31

    Article  PubMed  PubMed Central  Google Scholar 

  36. scRNA-tools table page. https://www.scrna-tools.org/. Accessed 22 June 2020

  37. Luecken MD, Theis FJ (2019) Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol 15

    Google Scholar 

  38. Neu KE, Tang Q, Wilson PC, Khan AA (2017) Single-cell genomics: approaches and utility in immunology. Trends Immunol 38:140–149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Butler A, Hoffman P, Smibert P et al (2018) Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 36:411–420

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wolf FA, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15

    Article  PubMed  PubMed Central  Google Scholar 

  41. Trapnell C, Cacchiarelli D, Grimsby J et al (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32:381–386

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Cao J, Spielmann M, Qiu X et al (2019) The single-cell transcriptional landscape of mammalian organogenesis. Nature 566:496–502

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gambardella G, di Bernardo D (2019) A tool for visualization and analysis of single-cell RNA-Seq data based on text mining, Front Genet:10

    Google Scholar 

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

    Google Scholar 

  45. scRNAseq_chapter. Github. https://github.com/gambalab/scRNAseq_chapter

  46. Cock PJA, Fields CJ, Goto N et al (2010) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38:1767–1771

    Article  CAS  PubMed  Google Scholar 

  47. Dobin A, Davis CA, Schlesinger F et al (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15–21

    Article  CAS  PubMed  Google Scholar 

  48. Du Y, Huang Q, Arisdakessian C, Garmire LX (2020) Evaluation of STAR and Kallisto on single cell RNA-Seq data alignment. G3 10:1775–1783

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Lun ATL, participants in the 1st Human Cell Atlas Jamboree, Riesenfeld S, et al (2019) EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol 20

    Google Scholar 

  50. Wolock SL, Lopez R, Klein AM (2019) Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst 8:281–291.e9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. DePasquale EAK, Schnell DJ, Van Camp P-J et al (2019) DoubletDecon: deconvoluting doublets from single-cell RNA-sequencing data. Cell Rep 29:1718–1727.e8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. McGinnis CS, Murrow LM, Gartner ZJ (2019) DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst 8(4):329–337.e4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Rogalinska M (2016) The role of mitochondria in cancer induction, progression and changes in metabolism. Mini Rev Med Chem 16:524–530

    Article  CAS  PubMed  Google Scholar 

  54. Düchting H, Seurat G (2000) Seurat. Taschen

    Google Scholar 

  55. Robertson SE, Jones KS (1976) Relevance weighting of search terms. J Am Soc Inf Sci 27:129–146

    Article  Google Scholar 

  56. Marinov GK, Williams BA, McCue K et al (2014) From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing. Genome Res 24:496–510

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  58. Wu AR, Neff NF, Kalisky T et al (2014) Quantitative assessment of single-cell RNA-sequencing methods. Nat Methods 11:41–46

    Article  CAS  PubMed  Google Scholar 

  59. Robinson MD, Oshlack A (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11:R25

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11(10):R106

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lun ATL, Bach K, Marioni JC (2016) Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 17:75

    Article  PubMed  CAS  Google Scholar 

  62. Vallejos CA, Risso D, Scialdone A et al (2017) Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat Methods 14:565–571

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Tran HTN, Ang KS, Chevrier M et al (2020) A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 21:12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8:118–127

    Article  PubMed  Google Scholar 

  65. Büttner M, Miao Z, Wolf FA et al (2019) A test metric for assessing single-cell RNA-seq batch correction. Nat Methods 16:43–49

    Article  PubMed  CAS  Google Scholar 

  66. Stuart T, Butler A, Hoffman P et al (2019) Comprehensive integration of single-cell data. Cell 177:1888–1902.e21

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Chen H-IH, Jin Y, Huang Y, Chen Y (2016) Detection of high variability in gene expression from single-cell RNA-seq profiling. BMC Genomics 17(Suppl 7):508

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Pearson K (1901) LIII. On lines and planes of closest fit to systems of points in space. London Edinburgh Dublin Philos Mag J Sci 2:559–572

    Article  Google Scholar 

  69. Chung NC, Storey JD (2015) Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics 31:545–554

    Article  CAS  PubMed  Google Scholar 

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

    Google Scholar 

  71. McInnes L, Healy J, Melville J (2018) UMAP: Uniform Manifold Approximation and Projection for dimension reduction. arXiv [stat.ML]

    Google Scholar 

  72. Moon KR, Stanley JS, Burkhardt D et al (2018) Manifold learning-based methods for analyzing single-cell RNA-sequencing data. Curr Opin Syst Biol 7:36–46

    Article  Google Scholar 

  73. Andrews TS, Hemberg M (2018) Identifying cell populations with scRNASeq. Mol Aspects Med 59:114–122

    Article  CAS  PubMed  Google Scholar 

  74. Kim T, Chen IR, Lin Y et al (2019) Impact of similarity metrics on single-cell RNA-seq data clustering. Brief Bioinform 20:2316–2326

    Article  CAS  PubMed  Google Scholar 

  75. Kiselev VY, Kirschner K, Schaub MT et al (2017) SC3 – consensus clustering of single-cell RNA-Seq data. Nat Methods 14(5):483–486

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Langfelder P, Zhang B, Horvath S (2008) Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics 24:719–720

    Article  CAS  PubMed  Google Scholar 

  77. Lin P, Troup M, Ho JWK (2017) CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol 18:59

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Guo M, Wang H, Potter SS et al (2015) SINCERA: a pipeline for single-cell RNA-Seq profiling analysis. PLoS Comput Biol 11:e1004575

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Levine JH, Simonds EF, Bendall SC et al (2015) Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162:184–197

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E Stat Nonlin Soft Matter Phys 70:066111

    Article  PubMed  CAS  Google Scholar 

  82. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103:8577–8582

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Kharchenko PV, Silberstein L, Scadden DT (2014) Bayesian approach to single-cell differential expression analysis. Nat Methods 11:740–742

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Jaccard P (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaud sci nat 37:547–579

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  88. Schiebinger G, Shu J, Tabaka M et al (2019) Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176:1517

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. La Manno G, Soldatov R, Zeisel A et al (2018) RNA velocity of single cells. Nature 560:494–498

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  90. Argelaguet R, Clark SJ, Mohammed H et al (2019) Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature 576:487–491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Han KY, Kim K-T, Joung J-G et al (2018) SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome Res 28:75–87

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Moncada R, Barkley D, Wagner F et al (2020) Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol 38:333–342

    Article  CAS  PubMed  Google Scholar 

  94. Ståhl PL, Salmén F, Vickovic S et al (2016) Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78–82

    Article  PubMed  CAS  Google Scholar 

  95. Valdes-Mora F, Handler K, Law AMK et al (2018) Single-cell transcriptomics in cancer immunobiology: the future of precision oncology. Front Immunol 9:2582

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Shalek AK, Benson M (2017) Single-cell analyses to tailor treatments. Sci Transl Med 9. https://doi.org/10.1126/scitranslmed.aan4730

Download references

Acknowledgments

We thank Chiara Reggio and Bashir Sadet (10× Genomics) for their feedbacks. This work was supported by Fondazione Telethon Core Grant, Armenise-Harvard Foundation Career Development Award, European Research Council (grant agreement 759154, CellKarma), the Rita-Levi Montalcini program from MIUR (to D.C.) and by the STAR (Sostegno Territoriale alle Attività di Ricerca) grant of University of Naples Federico II and My First AIRC Grant (MFAG, 23162) (to G.G.). Conflict of interest – Gennaro Gambardella declares that he has no conflict of interest. Davide Cacchiarelli is the founder, shareholder, and consultant of Next Generation Diagnostic srl. Figures were created with BioRender software,©biorender.com. 

Author information

Author notes

  1. Shaked Slovin, Annamaria Carissimo and Francesco Panariello contributed equally with all other contributors.

Authors and Affiliations

  1. Telethon Institute of Genetics and Medicine (TIGEM), Armenise/Harvard Laboratory of Integrative Genomics, Pozzuoli, Italy

    Shaked Slovin, Francesco Panariello, Antonio Grimaldi, Valentina Bouché, Gennaro Gambardella & Davide Cacchiarelli

  2. Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, Naples, Italy

    Annamaria Carissimo

  3. Department of Chemical Materials and Industrial Engineering, University of Naples “Federico II”, Naples, Italy

    Gennaro Gambardella

  4. Department of Translational Medicine, University of Naples “Federico II”, Naples, Italy

    Davide Cacchiarelli

Authors
  1. Shaked Slovin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Annamaria Carissimo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesco Panariello
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Antonio Grimaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Valentina Bouché
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gennaro Gambardella
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Davide Cacchiarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Gennaro Gambardella or Davide Cacchiarelli .

Editor information

Editors and Affiliations

  1. Dipartimento Di Bioscienze Biotecnologie E Biofarmaceutica, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Ernesto Picardi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Slovin, S. et al. (2021). Single-Cell RNA Sequencing Analysis: A Step-by-Step Overview. In: Picardi, E. (eds) RNA Bioinformatics. Methods in Molecular Biology, vol 2284. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1307-8_19

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1307-8_19

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1306-1

  • Online ISBN: 978-1-0716-1307-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation