Differential Coexpression Network Analysis for Gene Expression Data

  • Bao-Hong Liu
Part of the Methods in Molecular Biology book series (MIMB, volume 1754)


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 differential 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.

Key words

Coexpression Differential coexpression network 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Veterinary Etiological Biology; Key Laboratory of Veterinary Parasitology of Gansu Province; Lanzhou Veterinary Research InstituteChinese Academy of Agricultural SciencesLanzhouPeople’s Republic of China
  2. 2.Jiangsu Co-Innovation Center for Prevention and Control of Animal Infectious Diseases and ZoonosesYangzhouPeople’s Republic of China

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