Skip to main content

Integration of scATAC-Seq with scRNA-Seq Data

  • Protocol
  • First Online:
Single Cell Transcriptomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2584))

Abstract

Single-cell studies are enabling our understanding of the molecular processes of normal cell development and the onset of several pathologies. For instance, single-cell RNA sequencing (scRNA-Seq) measures the transcriptome-wide gene expression at a single-cell resolution, allowing for studying the heterogeneity among the cells of the same population and revealing complex and rare cell populations. On the other hand, single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-Seq) can be used to define transcriptional and epigenetic changes by analyzing the chromatin accessibility at the single-cell level. However, the integration of multi-omics data still remains one of the most difficult tasks in bioinformatics. In this chapter, we focus on the combination of scRNA-Seq and scATACSeq data to perform an integrative analysis of the single-cell transcriptome and chromatin accessibility of human fetal progenitors.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Similar content being viewed by others

References

  1. Gladka MM, Molenaar B, De Ruiter H, Van Der Elst S, Tsui H, Versteeg D, Lacraz GPA, Huibers MMH, Van Oudenaarden A, Van Rooij E (2018) Single-cell sequencing of the healthy and diseased heart reveals cytoskeleton-associated protein 4 as a new modulator of fibroblasts activation. Circulation 138(2):166–180

    Article  CAS  Google Scholar 

  2. Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, David E, Baruch K, Lara-Astaiso D, Toth B et al (2017) A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169(7):1276–1290

    Article  CAS  Google Scholar 

  3. Paul F, Arkin Y’a, Giladi A, Jaitin DA, Kenigsberg E, Keren-Shaul H, Winter D, Lara-Astiaso D, Gury M, Weiner A et al (2015) Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163(7):1663–1677

    Article  CAS  Google Scholar 

  4. Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM et al (2016) A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure. Cell Syst 3(4):346–360

    Article  CAS  Google Scholar 

  5. Villani A-C, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, Griesbeck M, Butler A, Zheng S, Lazo S et al (2017) Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356(6335):eaah4573

    Article  Google Scholar 

  6. Kernfeld EM, Genga RMJ, Neherin K, Magaletta ME, Xu P, Maehr R (2018) A single-cell transcriptomic atlas of thymus organogenesis resolves cell types and developmental maturation. Immunity 48(6):1258–1270

    Article  CAS  Google Scholar 

  7. Subramanian I, Verma S, Kumar S, Jere A, Anamika K (2020) Multi-omics data integration, interpretation, and its application. Bioinform Biol Insights 14:1177932219899051

    Article  Google Scholar 

  8. Lahnemann D, Koster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A et al (2020) Eleven grand challenges in single-cell data science. Genome Biol 21(1):1–35

    Article  Google Scholar 

  9. Forcato M, Romano O, Bicciato S (2021) Computational methods for the integrative analysis of single-cell data. Brief Bioinform 22(1):20–29

    Google Scholar 

  10. Janeway CA, Donald Capra J, Travers P, Walport M (1999) Immunobiology: the immune system in health and disease. Taylor & Francis Group, Abingdon-on-Thames

    Google Scholar 

  11. Tangherloni A, Riva SG, Spolaor S, Besozzi D, Nobile MS, Cazzaniga P (2021) The impact of representation on the optimization of marker panels for single-cell RNA data. In: Proceedings of the IEEE congress on evolutionary computation. IEEE, pp 1423–1430

    Google Scholar 

  12. Luecken MD, Theis FJ (2019) Current best practices in single-cell RNAseq analysis: a tutorial. Mol Syst Biol 15(6):e8746

    Article  Google Scholar 

  13. Luecken MD, Buttner M, Chaichoompu K, Danese A, Interlandi M, Mueller MF, Strobl DC, Zappia L, Dugas M, Colome-Tatche M et al (2020) Benchmarking atlas-level data integration in single-cell genomics. BioRxiv

    Google Scholar 

  14. Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, Chen J (2020) A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol 21(1):1–32

    Article  Google Scholar 

  15. Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010) Tackling the widespread and critical impact of batch effects in highthroughput data. Nat Rev Genet 11(10):733–739

    Article  CAS  Google Scholar 

  16. Alexander Wolf F, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19(1):15

    Article  Google Scholar 

  17. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52

    Article  CAS  Google Scholar 

  18. Becht E, McInnes L, Healy J, Dutertre C-A, Kwok IWH, Ng LG, Ginhoux F, Newell EW (2019) Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol 37(1):38

    Article  CAS  Google Scholar 

  19. Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33(5):495

    Article  CAS  Google Scholar 

  20. Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, Hao Y, Stoeckius M, Smibert P, Satija R (2019) Comprehensive integration of single-cell data. Cell 177(7):1888–1902.e21

    Article  CAS  Google Scholar 

  21. Hie B, Bryson B, Berger B (2019) Efficient integration of heterogeneous single-cell transcriptomes using scanorama. Nat Biotechnol 37(6):685–691

    Article  CAS  Google Scholar 

  22. Forcato M, Romano O, Bicciato S (2020) Computational methods for the integrative analysis of single-cell data. Brief Bioinform 22(3):bbaa042

    Article  Google Scholar 

  23. Argelaguet R, Cuomo ASE, Stegle O, Marioni JC (2021) Computational principles and challenges in single-cell data integration. Nat Biotechnol 39(10):1202–1215

    Article  CAS  Google Scholar 

  24. Miao Z, Humphreys BD, McMahon AP, Kim J (2021) Multi-omics integration in the age of million single-cell data. Nat Rev Nephrol 17(11):710–724

    Article  Google Scholar 

  25. Liu J, Gao C, Sodicoff J, Kozareva V, Macosko EZ, Welch JD (2020) Jointly defining cell types from multiple single-cell datasets using LIGER. Nat Protoc 15(11):3632–3662

    Article  CAS  Google Scholar 

  26. Wang C, Sun D, Huang X, Wan C, Li Z, Han Y, Qin Q, Fan J, Qiu X, Xie Y, Meyer CA, Brown M, Tang M, Long H, Liu T, Liu XS (2020) Integrative analyses of single-cell transcriptome and regulome using MAESTR2O. Genome Biol 21(1):198

    Article  CAS  Google Scholar 

  27. Haghverdi L, Lun ATL, Morgan MD, Marioni JC (2018) Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol 36(5):421–427

    Article  CAS  Google Scholar 

  28. Ma S, Zhang B, LaFave LM, Earl AS, Chiang Z, Yan H, Ding J, Brack A, Kartha VK, Tay T, Law T, Lareau C, Hsu Y-C, Regev A, Buenrostro JD (2020) Chromatin potential identified by shared single-cell profiling of RNA and chromatin. Cell 183(4):1103–1116.e20

    Article  CAS  Google Scholar 

  29. Ranzoni AM, Tangherloni A, Berest I, Riva SG, Myers B, Strzelecka PM, Xu J, Panada E, Mohorianu I, Zaugg JB, Cvejic A (2021) Integrative single-cell RNA-seq and ATACseq analysis of human developmental hematopoiesis. Cell Stem Cell 28(3):472–487.e7

    Article  CAS  Google Scholar 

  30. Tangherloni A, Ricciuti F, Besozzi D, Liò P, Cvejic A (2021) Analysis of single-cell RNA sequencing data based on autoencoders. BMC Bioinform 22(1):1–27

    Article  Google Scholar 

  31. Stuart T, Srivastava A, Madad S, Lareau CA, Satija R (2021) Single-cell chromatin state analysis with signac. Nat Methods 18(11):1333–1341

    Article  CAS  Google Scholar 

  32. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S (2019) Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods 16(12):1289–1296

    Article  CAS  Google Scholar 

  33. Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, Sandberg R (2014) Full-length rna-seq from single cells using smart-seq2. Nat Protoc 9(1):171–181

    Article  CAS  Google Scholar 

  34. Macaulay IC, Svensson V, Labalette C, Ferreira L, Hamey F, Voet T, Teichmann SA, Cvejic A (2016) Single-cell RNA-sequencing reveals a continuous spectrum of differentiation in hematopoietic cells. Cell Rep 14(4):966–977

    Article  CAS  Google Scholar 

  35. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR (2013) Star: ultrafast universal rna-seq aligner. Bioinformatics 29(1):15–21

    Article  CAS  Google Scholar 

  36. Chen X, Miragaia RJ, Natarajan KN, Teichmann SA (2018) A rapid and robust method for single cell chromatin accessibility profiling. Nat Commun 9(1):1–9

    Article  Google Scholar 

  37. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R (2009) The sequence alignment/map format and samtools. Bioinformatics 25(16):2078–2079

    Article  Google Scholar 

  38. Quinlan AR (2014) Bedtools: the swiss-army tool for genome feature analysis. Curr Protoc Bioinform 47(1):11–12

    Article  Google Scholar 

  39. Broad Institute (2018) Picard tools. http://broadinstitute.github.io/picard/

  40. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25(14):1754–1760

    Article  CAS  Google Scholar 

  41. Fang R, Preissl S, Yang L, Hou X, Lucero J, Wang X, Motamedi A, Shiau AK, Zhou X, Xie F et al (2021) Comprehensive analysis of single cell ATAC-seq data with snapatac. Nat Commun 12(1):1–15

    Article  Google Scholar 

  42. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W et al (2008) Model-based analysis of ChiP-seq (MACS). Genome Biol 9(9):1–9

    Article  Google Scholar 

  43. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

    Google Scholar 

  44. Polanski K, Young MD, Miao Z, Meyer KB, Teichmann SA, Park J-E (2020) BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36(3):964–965

    CAS  Google Scholar 

  45. Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60

    Article  Google Scholar 

  46. Wilcoxon F (1992) Individual comparisons by ranking methods. In: Breakthroughs in statistics. Springer, pp 196–202

    Chapter  Google Scholar 

  47. Dunn OJ (1961) Multiple comparisons among means. J Am Stat Assoc 56(293):52–64

    Article  Google Scholar 

  48. Amemiya HM, Kundaje A, Boyle AP (2019) The ENCODE blacklist: identification of problematic regions of the genome. Sci Rep 9(1):9354

    Article  Google Scholar 

  49. Cusanovich DA, Hill AJ, Aghamirzaie D, Daza RM, Pliner HA, Berletch JB, Filippova GN, Huang X, Christiansen L, DeWitt WS, Lee C, Regalado SG, Read DF, Steemers FJ, Disteche CM, Trapnell C, Shendure J (2018) A single-cell atlas of in vivo mammalian chromatin accessibility. Cell 174(5):1309–1324.e18

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrea Tangherloni .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Berest, I., Tangherloni, A. (2023). Integration of scATAC-Seq with scRNA-Seq Data. In: Calogero, R.A., Benes, V. (eds) Single Cell Transcriptomics. Methods in Molecular Biology, vol 2584. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2756-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2756-3_15

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2755-6

  • Online ISBN: 978-1-0716-2756-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics