Skip to main content
SpringerLink
Log in
Book cover

Gene Regulatory Networks pp 235–249Cite as

Network Inference from Single-Cell Transcriptomic Data

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1883))

Abstract

Recent technological breakthroughs in single-cell RNA sequencing are revolutionizing modern experimental design in biology. The increasing size of the single-cell expression data from which networks can be inferred allows identifying more complex, non-linear dependencies between genes. Moreover, the inter-cellular variability that is observed in single-cell expression data can be used to infer not only one global network representing all the cells, but also numerous regulatory networks that are more specific to certain conditions. By experimentally perturbing certain genes, the deconvolution of the true contribution of these genes can also be greatly facilitated. In this chapter, we will therefore tackle the advantages of single-cell transcriptomic data and show how new methods exploit this novel data type to enhance the inference of gene regulatory networks.

This is a preview of subscription content, 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   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   199.99
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

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Padovan-Merhar O, Raj A (2013) Using variability in gene expression as a tool for studying gene regulation. Wiley Interdiscip Rev Syst Biol Med 5(6):751–759

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16(3):133–145

    Article  CAS  PubMed  Google Scholar 

  3. Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X, Proserpio V, Baying B, Benes V, Teichmann SA, Marioni JC, Heisler MG (2013) Accounting for technical noise in single-cell RNA-seq experiments. Nat Methods 10(11):1093–1095

    Article  CAS  PubMed  Google Scholar 

  4. Grün D, Kester L, van Oudenaarden A (2014) Validation of noise models for single-cell transcriptomics. Nat Methods 11(6):637–640

    Article  PubMed  Google Scholar 

  5. Marinov GK, Williams BA, McCue K, Schroth GP, Gertz J, Myers RM, Wold BJ (2014) From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res 24(3):496–510

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Svensson V, Natarajan KN, Ly LH, Miragaia RJ, Labalette C, Macaulay IC, Cvejic A, Teichmann SA (2017) Power analysis of single-cell RNA-sequencing experiments. Nat Methods 14(4):381–387

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lun ATL, Bach K, Marioni JC (2016) Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 17(1):75

    Article  PubMed  Google Scholar 

  8. Van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe’er D (2018) Recovering Gene Interactions from Single-Cell Data Using Data Diffusion. Cell 174(3):716–729.e27

    Google Scholar 

  9. Pierson E, Yau C (2015) ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol 16(1):241

    Article  PubMed  PubMed Central  Google Scholar 

  10. Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert JP (2018) A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun 9(1):284

    Article  PubMed  PubMed Central  Google Scholar 

  11. Berge KVd, Perraudeau F, Soneson C, Love MI, Risso D, Vert JP, Robinson MD, Dudoit S, Clement L (2018) Observation weights to unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol 19(1):24

    Google Scholar 

  12. Vallejos CA, Risso D, Scialdone A, Dudoit S, Marioni JC (2017) Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat Methods 14(6):565–571

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Microbiol 8(10):717–729

    Article  PubMed  Google Scholar 

  14. Svensson V, Vento-Tormo R, Teichmann SA (2018) Exponential scaling of single-cell RNA-seq in the past decade. Nat Protocols 13(4):599–604

    Article  CAS  PubMed  Google Scholar 

  15. Moignard V, Woodhouse S, Haghverdi L, Lilly AJ, Tanaka Y, Wilkinson AC, Buettner F, Macaulay IC, Jawaid W, Diamanti E, Nishikawa SI, Piterman N, Kouskoff V, Theis FJ, Fisher J, Göttgens B (2015) Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat Biotechnol 33(3):269–276

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Liu X, Wang Y, Ji H, Aihara K, Chen L (2016) Personalized characterization of diseases using sample-specific networks. Nucleic Acids Res 44(22):e164–e164

    Article  PubMed  PubMed Central  Google Scholar 

  17. Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, Rambow F, Marine JC, Geurts P, Aerts J, van den Oord J, Atak ZK, Wouters J, Aerts S (2017) SCENIC: single-cell regulatory network inference and clustering. Nat Methods 14(11):1083–1086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chan TE, Stumpf MPH, Babtie AC (2017) Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst 5(3):251–267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Filippi S, Holmes CC (2016) A Bayesian non-parametric approach to testing for dependence between random variables. Bayesian Anal 12(4):919–938

    Article  Google Scholar 

  20. Papp B, Oliver S (2005) Genome-wide analysis of the context-dependence of regulatory networks. Genome Biol 6(2):206

    Article  PubMed  PubMed Central  Google Scholar 

  21. Xu R, Nettleton D, Nordman DJ (2016) Case-specific random forests. J Comput Graph Stat 25(1):49–65

    Article  Google Scholar 

  22. Ocone A, Haghverdi L, Mueller NS, Theis FJ (2015) Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics 31(12):i89–i96

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wei J, Hu X, Zou X, Tian T (2016) Inference of genetic regulatory network for stem cell using single cells expression data. In: 2016 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, Piscataway, pp 217–222

    Chapter  Google Scholar 

  24. Castillo MS, Blanco D, Luna IMT, Carrion MC, Huang Y (2017) A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data. Bioinformatics 34(6):964–970

    Article  Google Scholar 

  25. Matsumoto H, Kiryu H, Furusawa C, Ko MSH, Ko SBH, Gouda N, Hayashi T, Nikaido I (2017) SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics 33(15):2314–2321

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ideker T, Krogan NJ (2012) Differential network biology. Mol Syst Biol 8(565):565

    PubMed  PubMed Central  Google Scholar 

  27. Gill R, Datta S, Datta S (2014) Differential network analysis in human cancer research. Curr Pharm Des 20(1):4–10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cannoodt R, Saelens W, Saeys Y (2016) Computational methods for trajectory inference from single-cell transcriptomics. Eur J Immunol 46(11):2496–2506

    Article  CAS  PubMed  Google Scholar 

  29. Kuijjer ML, Tung M, Yuan G, Quackenbush J, Glass K (2018) Estimating sample-specific regulatory networks. arXiv preprint arXiv:150506440v2

    Google Scholar 

  30. Eren K, Deveci M, Kucuktunc O, Catalyurek UV (2013) A comparative analysis of biclustering algorithms for gene expression data. Brief Bioinf 14(3):279–292

    Article  CAS  Google Scholar 

  31. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring regulatory networks from expression data using tree-based methods. PLoS One 5(9):e12776

    Article  PubMed  PubMed Central  Google Scholar 

  32. Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Aderhold A, Allison KR, Bonneau R, Camacho DM, Chen Y, Collins JJ, Cordero F, Costello JC, Crane M, Dondelinger F, Drton M, Esposito R, Foygel R, de la Fuente A, Gertheiss J, Geurts P, Greenfield A, Grzegorczyk M, Haury AC, Holmes B, Hothorn T, Husmeier D, Huynh-Thu VA, Irrthum A, Kellis M, Karlebach G, Küffner R, Lèbre S, De Leo V, Madar A, Mani S, Marbach D, Mordelet F, Ostrer H, Ouyang Z, Pandya R, Petri T, Pinna A, Poultney CS, Prill RJ, Rezny S, Ruskin HJ, Saeys Y, Shamir R, Sîrbu A, Song M, Soranzo N, Statnikov A, Stolovitzky G, Vega N, Vera-Licona P, Vert JP, Visconti A, Wang H, Wehenkel L, Windhager L, Zhang Y, Zimmer R, Kellis M, Collins JJ, Stolovitzky G (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9(8):796–804

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H, David E, Salame TM, Tanay A, van Oudenaarden A, Amit I (2016) Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167(7):1883–1896

    Article  CAS  PubMed  Google Scholar 

  34. Dixit A, Parnas O, Li B, Chen J, Fulco CP, Jerby-Arnon L, Marjanovic ND, Dionne D, Burks T, Raychowdhury R, Adamson B, Norman TM, Lander ES, Weissman JS, Friedman N, Regev A (2016) Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167(7):1853–1866

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Adamson B, Norman TM, Jost M, Cho MY, Nuñez JK, Chen Y, Villalta JE, Gilbert LA, Horlbeck MA, Hein MY, Pak RA, Gray AN, Gross CA, Dixit A, Parnas O, Regev A, Weissman JS (2016) A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167(7):1867–1882

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Datlinger P, Rendeiro AF, Schmidl C, Krausgruber T, Traxler P, Klughammer J, Schuster LC, Kuchler A, Alpar D, Bock C (2017) Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods 14(3):297–301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Äijö T, Bonneau R (2016) Biophysically motivated regulatory network inference: progress and prospects. Hum Hered 81(2):62–77

    Article  PubMed  Google Scholar 

  38. Petralia F, Wang P, Yang J, Tu Z (2015) Integrative random forest for gene regulatory network inference. Bioinformatics 31(12):i197–i205

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML, Snyder MP, Chang HY, Greenleaf WJ (2015) Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523(7561):486–490

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L, Gunderson KL, Steemers FJ, Trapnell C, Shendure J (2015) Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348(6237):910–914

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jin W, Tang Q, Wan M, Cui K, Zhang Y, Ren G, Ni B, Sklar J, Przytycka TM, Childs R, Levens D, Zhao K (2015) Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528(7580):142

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, Andrews SR, Stegle O, Reik W, Kelsey G (2014) Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods 11(8):817–820

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schönegger A, Klughammer J, Bock C (2015) Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep 10(8):1386–1397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Kind J, Pagie L, de Vries S, Nahidiazar L, Dey S, Bienko M, Zhan Y, Lajoie B, de Graaf C, Amendola M, Fudenberg G, Imakaev M, Mirny L, Jalink K, Dekker J, van Oudenaarden A, van Steensel B (2015) Genome-wide Maps of Nuclear Lamina Interactions in Single Human Cells. Cell 163(1):134–147

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E, Dean W, Laue ED, Tanay A, Fraser P (2013) Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502(7469):59–64

    Article  CAS  PubMed  Google Scholar 

  46. Rotem A, Ram O, Shoresh N, Sperling RA, Goren A, Weitz DA, Bernstein BE (2015) Single-cell ChIP-seq reveals cell subpopulationsdefined by chromatin state. Nat Biotechnol 33(11):1165–1172

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Angermueller C, Clark SJ, Lee HJ, Macaulay IC, Teng MJ, Hu TX, Krueger F, Smallwood SA, Ponting CP, Voet T, Kelsey G, Stegle O, Reik W (2016) Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods 13(3):229–232

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Clark SJ, Argelaguet R, Kapourani CA, Stubbs TM, Lee HJ, Alda-Catalinas C, Krueger F, Sanguinetti G, Kelsey G, Marioni JC, Stegle O, Reik W (2018) scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat Commun 9(1):781

    Google Scholar 

  49. Genshaft AS, Li S, Gallant CJ, Darmanis S, Prakadan SM, Ziegler CGK, Lundberg M, Fredriksson S, Hong J, Regev A, Livak KJ, Landegren U, Shalek AK (2016) Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol 17(1):188

    Article  PubMed  PubMed Central  Google Scholar 

  50. Frei AP, Bava FA, Zunder ER, Hsieh EWY, Chen SY, Nolan GP, Gherardini PF (2016) Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat Methods 13(3):269–275

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci U S A 107(14):6286–6291

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Marbach D, Lamparter D, Quon G, Kellis M, Kutalik Z, Bergmann S (2016) Tissue-specific regulatory circuits reveal variable modular perturbations across complex diseases. Nat Methods 13(4):366–370

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium

    Helena Todorov, Robrecht Cannoodt, Wouter Saelens & Yvan Saeys

  2. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Helena Todorov

  3. Centre International de Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon, Lyon, France

    Helena Todorov

  4. Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium

    Robrecht Cannoodt

  5. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Wouter Saelens & Yvan Saeys

Authors
  1. Helena Todorov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Robrecht Cannoodt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wouter Saelens
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yvan Saeys
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Helena Todorov .

Editor information

Editors and Affiliations

  1. School of Informatics, University of Edinburgh, Edinburgh, UK

    Guido Sanguinetti

  2. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Todorov, H., Cannoodt, R., Saelens, W., Saeys, Y. (2019). Network Inference from Single-Cell Transcriptomic Data. In: Sanguinetti, G., Huynh-Thu, V. (eds) Gene Regulatory Networks. Methods in Molecular Biology, vol 1883. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8882-2_10

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-8882-2_10

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8881-5

  • Online ISBN: 978-1-4939-8882-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation