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Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data

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Computational Methods for Single-Cell Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1935))

Abstract

Single-cell RNA-Sequencing is a pioneering extension of bulk-based RNA-Sequencing technology. The “guilt-by-association” heuristic has led to the use of gene co-expression networks to identify genes that are believed to be associated with a common cellular function. Many methods that were developed for bulk-based RNA-Sequencing data can continue to be applied to single-cell data, and several of the most widely used methods are explored. Several methods for leveraging the novel time information contained in single-cell data when constructing gene co-expression networks, which allows for the incorporation of directed associations, are also discussed.

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Author information

Authors and Affiliations

  1. Mathematics Department, Bryant University, Smithfield, RI, USA

    Alicia T. Lamere

  2. Applied and Computational Mathematics and Statistics Department, University of Notre Dame, Notre Dame, IN, USA

    Jun Li

Authors
  1. Alicia T. Lamere
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  2. Jun Li
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Corresponding author

Correspondence to Alicia T. Lamere .

Editor information

Editors and Affiliations

  1. Dana–Farber Cancer Institute and Harvard Chan, School of Public Health, Boston, MA, USA

    Guo-Cheng Yuan

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Lamere, A.T., Li, J. (2019). Inference of Gene Co-expression Networks from Single-Cell RNA-Sequencing Data. In: Yuan, GC. (eds) Computational Methods for Single-Cell Data Analysis. Methods in Molecular Biology, vol 1935. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9057-3_10

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  • DOI: https://doi.org/10.1007/978-1-4939-9057-3_10

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9056-6

  • Online ISBN: 978-1-4939-9057-3

  • eBook Packages: Springer Protocols

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