Advertisement

Differential Coexpression Network Analysis for Gene Expression Data

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

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 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 

References

  1. 1.
    Mitra K et al (2013) Integrative approaches for finding modular structure in biological networks. Nat Rev Genet 14(10):719–732CrossRefGoogle Scholar
  2. 2.
    Vidal M, Cusick ME, Barabasi AL (2011) Interactome networks and human disease. Cell 144(6):986–998CrossRefGoogle Scholar
  3. 3.
    Harrold JM, Ramanathan M, Mager DE (2013) Network-based approaches in drug discovery and early development. Clin Pharmacol Ther 94(6):651–658CrossRefGoogle Scholar
  4. 4.
    Robin X et al (2013) Personalized network-based treatments in oncology. Clin Pharmacol Ther 94(6):646–650CrossRefGoogle Scholar
  5. 5.
    Prieto C et al (2008) Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles. PLoS One 3(12):e3911CrossRefGoogle Scholar
  6. 6.
    Stanley D et al (2013) Genetic architecture of gene expression in the chicken. BMC Genomics 14:13CrossRefGoogle Scholar
  7. 7.
    van Noort V, Snel B, Huynen MA (2004) The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model. EMBO Rep 5(3):280–284CrossRefGoogle Scholar
  8. 8.
    Liu BH et al (2010) DCGL: an R package for identifying differentially coexpressed genes and links from gene expression microarray data. Bioinformatics 26(20):2637–2638CrossRefGoogle Scholar
  9. 9.
    Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559CrossRefGoogle Scholar
  10. 10.
    Santos Sde S et al (2015) CoGA: an R package to identify differentially co-expressed gene sets by analyzing the graph spectra. PLoS One 10(8):e0135831CrossRefGoogle Scholar
  11. 11.
    Jiang Z et al (2016) Differential coexpression analysis reveals extensive rewiring of Arabidopsis gene coexpression in response to pseudomonas syringae infection. Sci Rep 6:35064CrossRefGoogle Scholar
  12. 12.
    Yu H et al (2011) Link-based quantitative methods to identify differentially coexpressed genes and gene pairs. BMC Bioinformatics 12:315CrossRefGoogle Scholar
  13. 13.
    Ruan J, Dean AK, Zhang W (2010) A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst Biol 4:8CrossRefGoogle Scholar
  14. 14.
    Elo LL et al (2007) Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics 23(16):2096–2103CrossRefGoogle Scholar
  15. 15.
    Jiang X, Zhang H, Quan X (2016) Differentially Coexpressed disease gene identification based on gene Coexpression network. Biomed Res Int 2016:3962761PubMedPubMedCentralGoogle Scholar
  16. 16.
    Yang J et al (2013) DCGL v2.0: an R package for unveiling differential regulation from differential co-expression. PLoS One 8(11):e79729CrossRefGoogle Scholar
  17. 17.
    Watson M (2006) CoXpress: differential co-expression in gene expression data. BMC Bioinformatics 7:509CrossRefGoogle Scholar
  18. 18.
    Tesson BM, Breitling R, Jansen RC (2010) DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics 11:497CrossRefGoogle Scholar
  19. 19.
    Choi Y, Kendziorski C (2009) Statistical methods for gene set co-expression analysis. Bioinformatics 25(21):2780–2786CrossRefGoogle Scholar
  20. 20.
    Rahmatallah Y, Emmert-Streib F, Glazko G (2014) Gene sets net correlations analysis (GSNCA): a multivariate differential coexpression test for gene sets. Bioinformatics 30(3):360–368CrossRefGoogle Scholar
  21. 21.
    Amar D, Safer H, Shamir R (2013) Dissection of regulatory networks that are altered in disease via differential co-expression. PLoS Comput Biol 9(3):e1002955CrossRefGoogle Scholar
  22. 22.
    Lai Y et al (2004) A statistical method for identifying differential gene-gene co-expression patterns. Bioinformatics 20(17):3146–3155CrossRefGoogle Scholar
  23. 23.
    Choi JK et al (2005) Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics 21(24):4348–4355CrossRefGoogle Scholar
  24. 24.
    Yoon SH, Kim JS, Song HH (2003) Statistical inference methods for detecting altered gene associations. Genome Inform 14:54–63PubMedGoogle Scholar
  25. 25.
    Li KC (2002) Genome-wide coexpression dynamics: theory and application. Proc Natl Acad Sci USA 99(26):16875–16880CrossRefGoogle Scholar
  26. 26.
    McKenzie AT et al (2016) DGCA: a comprehensive R package for differential gene correlation analysis. BMC Syst Biol 10(1):106CrossRefGoogle Scholar
  27. 27.
    Fukushima A (2013) DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 518(1):209–214CrossRefGoogle Scholar
  28. 28.
    Dawson JA, Ye S, Kendziorski C (2012) R/EBcoexpress: an empirical Bayesian framework for discovering differential co-expression. Bioinformatics 28(14):1939–1940CrossRefGoogle Scholar
  29. 29.
    Siska C, Bowler R, Kechris K (2016) The discordant method: a novel approach for differential correlation. Bioinformatics 32(5):690–696CrossRefGoogle Scholar
  30. 30.
    Deng SP, Zhu L, Huang DS (2015) Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. BMC Genomics 16(Suppl 3):S4CrossRefGoogle Scholar
  31. 31.
    Jia X et al (2014) Cancer-risk module identification and module-based disease risk evaluation: a case study on lung cancer. PLoS One 9(3):e92395CrossRefGoogle Scholar
  32. 32.
    Hong S et al (2011) Gene co-expression network and functional module analysis of ovarian cancer. Int J Comput Biol Drug Des 4(2):147–164CrossRefGoogle Scholar
  33. 33.
    Ivliev AE et al (2016) Drug repositioning through systematic Mining of Gene Coexpression Networks in cancer. PLoS One 11(11):e0165059CrossRefGoogle Scholar
  34. 34.
    Giulietti M et al (2016) Weighted gene co-expression network analysis reveals key genes involved in pancreatic ductal adenocarcinoma development. Cell Oncol (Dordr) 39(4):379–388CrossRefGoogle Scholar
  35. 35.
    Gu Y et al (2017) Identification of prognostic genes in kidney renal clear cell carcinoma by RNAseq data analysis. Mol Med Rep 15(4):1661–1667CrossRefGoogle Scholar
  36. 36.
    Oros Klein K et al (2016) Gene Coexpression analyses differentiate networks associated with diverse cancers Harboring TP53 missense or null mutations. Front Genet 7:137CrossRefGoogle Scholar
  37. 37.
    Li C et al (2013) Gene expression patterns combined with bioinformatics analysis identify genes associated with cholangiocarcinoma. Comput Biol Chem 47:192–197CrossRefGoogle Scholar
  38. 38.
    Cao MS et al (2015) Differential network analysis reveals dysfunctional regulatory networks in gastric carcinogenesis. Am J Cancer Res 5(9):2605–2625PubMedPubMedCentralGoogle Scholar

Copyright information

© 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

Personalised recommendations