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Network Inference from Single-Cell Transcriptomic Data

  • Helena TodorovEmail author
  • Robrecht Cannoodt
  • Wouter Saelens
  • Yvan Saeys
Protocol
Part of the Methods in Molecular Biology book series (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.

Key words

Network inference Single cell Gene regulatory networks Transcriptomics 

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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

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