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

Alicia T Lamere; Jun Li

doi:10.1007/978-1-4939-9057-3_10

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

Methods Mol Biol. 2019:1935:141-153. doi: 10.1007/978-1-4939-9057-3_10.

Authors

Alicia T Lamere¹, Jun Li²

Affiliations

¹ Mathematics Department, Bryant University, Smithfield, RI, USA. alamere1@bryant.edu.
² Applied and Computational Mathematics and Statistics Department, University of Notre Dame, Notre Dame, IN, USA.

PMID: 30758825
DOI: 10.1007/978-1-4939-9057-3_10

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.

Keywords: Correlation coefficient; Count data; Directed network; Gene co-expression network; Gene regulatory network; Pseudotime; Single-cell RNA-Seq.

MeSH terms

Algorithms
Computational Biology / methods
Gene Expression / genetics*
Gene Expression Profiling / methods
Gene Regulatory Networks / genetics*
Humans
RNA / genetics*
Sequence Analysis, RNA / methods
Single-Cell Analysis / methods

Substances

RNA