Computational Systems Biology Methods and Protocols.7z

Chapter 9

Differential Coexpression Network Analysis for Gene

Expression Data

Bao-Hong Liu

Abstract

Gene expression profiling by microarray has been used to uncover molecular variations in many areas. The
traditional analysis method to gene expression profiling just focuses on the individual genes, and the
interactions among genes are ignored, while genes play their roles not by isolations but by interactions
with each other. Consequently, gene-to-gene coexpression analysis emerged as a powerful approach to solve
the above problems. Then complementary to the conventional differential expression analysis, the differ-
ential coexpression analysis can identify gene markers from the systematic level. There are three aspects for
differential coexpression network analysis including the network global topological comparison, differential
coexpression module identification, and differential coexpression genes and gene pairs identification. To
date, the coexpression network and differential coexpression analysis are widely used in a variety of areas in
response to environmental stresses, genetic differences, or disease changes. In this chapter, we reviewed the
existing methods for differential coexpression network analysis and discussed the applications to cancer
research.

KeywordsCoexpression, Differential coexpression network

1 Introduction

In biological systems, distinct groups of molecules that are func-

tionally coordinated, physically interacting or co-regulated, drive

complex biological processes. To dissect the complexity of

biological systems, a complete map of intermolecular interactions

is required. Networks provide a straightforward representation of

interactions between the nodes, and there are multiple types of

network including physical attachments underlying protein-protein

interaction network, kinase-substrate interaction network, protein-

DNA interaction network, and metabolic reaction network, as well

as functional associations such as epistasis, synthetic lethality rela-

tionships, and correlated expression between genes [1, 2]. These

various molecular networks have been successfully applied to

address different biological questions, such as identification of

Tao Huang (ed.),Computational Systems Biology: Methods and Protocols, Methods in Molecular Biology, vol. 1754,
https://doi.org/10.1007/978-1-4939-7717-8_9,©Springer Science+Business Media, LLC, part of Springer Nature 2018

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