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

Document Type

Book Chapter


This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.


gene co-expression network; gene regulatory network; single-cell RNA-seq; correlation coefficient; count data; directed network; pseudotime

Identifier Data




Publication Source

Computational Methods for Single-Cell Data Analysis


Single-cell RNA-sequencing is a pioneering extension of bulk-base 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.