Network Inference from Single-Cell Transcriptomic Data

  • Helena TodorovEmail author
  • Robrecht Cannoodt
  • Wouter Saelens
  • Yvan Saeys
Part of the Methods in Molecular Biology book series (MIMB, volume 1883)


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.

Key words

Network inference Single cell Gene regulatory networks Transcriptomics 


  1. 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):751–759PubMedPubMedCentralCrossRefGoogle Scholar
  2. 2.
    Stegle O, Teichmann SA, Marioni JC (2015) Computational and analytical challenges in single-cell transcriptomics. Nat Rev Genet 16(3):133–145PubMedPubMedCentralCrossRefGoogle Scholar
  3. 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):1093–1095PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Grün D, Kester L, van Oudenaarden A (2014) Validation of noise models for single-cell transcriptomics. Nat Methods 11(6):637–640PubMedPubMedCentralCrossRefGoogle Scholar
  5. 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):496–510PubMedPubMedCentralCrossRefGoogle Scholar
  6. 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):381–387PubMedPubMedCentralCrossRefGoogle Scholar
  7. 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):75PubMedPubMedCentralCrossRefGoogle Scholar
  8. 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.e27Google Scholar
  9. 9.
    Pierson E, Yau C (2015) ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol 16(1):241PubMedPubMedCentralCrossRefGoogle Scholar
  10. 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):284PubMedPubMedCentralCrossRefGoogle Scholar
  11. 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):24Google Scholar
  12. 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):565–571PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Microbiol 8(10):717–729PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Svensson V, Vento-Tormo R, Teichmann SA (2018) Exponential scaling of single-cell RNA-seq in the past decade. Nat Protocols 13(4):599–604PubMedPubMedCentralCrossRefGoogle Scholar
  15. 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):269–276PubMedPubMedCentralCrossRefGoogle Scholar
  16. 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–e164PubMedPubMedCentralCrossRefGoogle Scholar
  17. 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):1083–1086PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Chan TE, Stumpf MPH, Babtie AC (2017) Gene regulatory network inference from single-cell data using multivariate information measures. Cell Syst 5(3):251–267PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Filippi S, Holmes CC (2016) A Bayesian non-parametric approach to testing for dependence between random variables. Bayesian Anal 12(4):919–938CrossRefGoogle Scholar
  20. 20.
    Papp B, Oliver S (2005) Genome-wide analysis of the context-dependence of regulatory networks. Genome Biol 6(2):206PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Xu R, Nettleton D, Nordman DJ (2016) Case-specific random forests. J Comput Graph Stat 25(1):49–65CrossRefGoogle Scholar
  22. 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–i96PubMedPubMedCentralCrossRefGoogle Scholar
  23. 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 217–222CrossRefGoogle Scholar
  24. 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):964–970CrossRefGoogle Scholar
  25. 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):2314–2321PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Ideker T, Krogan NJ (2012) Differential network biology. Mol Syst Biol 8(565):565PubMedPubMedCentralGoogle Scholar
  27. 27.
    Gill R, Datta S, Datta S (2014) Differential network analysis in human cancer research. Curr Pharm Des 20(1):4–10PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Cannoodt R, Saelens W, Saeys Y (2016) Computational methods for trajectory inference from single-cell transcriptomics. Eur J Immunol 46(11):2496–2506PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Kuijjer ML, Tung M, Yuan G, Quackenbush J, Glass K (2018) Estimating sample-specific regulatory networks. arXiv preprint arXiv:150506440v2Google Scholar
  30. 30.
    Eren K, Deveci M, Kucuktunc O, Catalyurek UV (2013) A comparative analysis of biclustering algorithms for gene expression data. Brief Bioinf 14(3):279–292CrossRefGoogle Scholar
  31. 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):e12776PubMedPubMedCentralCrossRefGoogle Scholar
  32. 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):796–804PubMedPubMedCentralCrossRefGoogle Scholar
  33. 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):1883–1896PubMedPubMedCentralCrossRefGoogle Scholar
  34. 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):1853–1866PubMedPubMedCentralCrossRefGoogle Scholar
  35. 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):1867–1882PubMedPubMedCentralCrossRefGoogle Scholar
  36. 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):297–301PubMedPubMedCentralCrossRefGoogle Scholar
  37. 37.
    Äijö T, Bonneau R (2016) Biophysically motivated regulatory network inference: progress and prospects. Hum Hered 81(2):62–77PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Petralia F, Wang P, Yang J, Tu Z (2015) Integrative random forest for gene regulatory network inference. Bioinformatics 31(12):i197–i205PubMedPubMedCentralCrossRefGoogle Scholar
  39. 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):486–490PubMedPubMedCentralCrossRefGoogle Scholar
  40. 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):910–914PubMedPubMedCentralCrossRefGoogle Scholar
  41. 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):142PubMedPubMedCentralGoogle Scholar
  42. 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):817–820PubMedPubMedCentralCrossRefGoogle Scholar
  43. 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):1386–1397PubMedPubMedCentralCrossRefGoogle Scholar
  44. 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):134–147PubMedPubMedCentralCrossRefGoogle Scholar
  45. 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):59–64PubMedPubMedCentralCrossRefGoogle Scholar
  46. 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):1165–1172PubMedPubMedCentralCrossRefGoogle Scholar
  47. 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):229–232PubMedPubMedCentralCrossRefGoogle Scholar
  48. 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):781Google Scholar
  49. 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):188PubMedPubMedCentralCrossRefGoogle Scholar
  50. 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):269–275PubMedPubMedCentralCrossRefGoogle Scholar
  51. 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):6286–6291PubMedPubMedCentralCrossRefGoogle Scholar
  52. 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):366–370PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Helena Todorov
    • 1
    • 2
    • 3
    Email author
  • Robrecht Cannoodt
    • 1
    • 4
  • Wouter Saelens
    • 1
    • 5
  • Yvan Saeys
    • 1
    • 5
  1. 1.Data Mining and Modelling for BiomedicineVIB Center for Inflammation ResearchGhentBelgium
  2. 2.Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGhentBelgium
  3. 3.Centre International de Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1CNRS, UMR5308, École Normale Supérieure de Lyon, Univ LyonLyonFrance
  4. 4.Center for Medical GeneticsGhent University HospitalGhentBelgium
  5. 5.Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGhentBelgium

Personalised recommendations