Methods for Gene Coexpression Network Visualization and Analysis

  • Carlos Alberto Moreira-FilhoEmail author
  • Silvia Yumi Bando
  • Fernanda Bernardi Bertonha
  • Filipi Nascimento Silva
  • Luciano da Fontoura Costa


Gene network analysis is an important tool for studying the changes in steady states that characterize cell functional properties, the genome-environment interplay and the health-disease transitions. The integration of gene coexpression and protein interaction data is one current frontier of systems biology, leading, for instance, to the identification of unique and common drivers to disease conditions. In this chapter the fundamentals for gene coexpression network construction, visualization and analysis are revised, emphasizing its scale-free nature, the measures that express its most relevant topological features, and methods for network validation.


Temporal Lobe Epilepsy Node Degree Betweenness Centrality Scale Free Network Gene Coexpression Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Albert R (2005) Scale-free networks in cell biology. J Cell Sci 118:4947–4957PubMedCrossRefGoogle Scholar
  2. Albert R, Jeong H, Barabási AL (2008) Error and attack tolerance of complex networks. Nature 406:378–382CrossRefGoogle Scholar
  3. Allen KD, Coffman CJ, Golightly YM et al (2010) Comparison of pain measures among patients with osteoarthritis. J Pain 11:522–527PubMedCrossRefGoogle Scholar
  4. Bando SY, Alegro MC, Amaro E Jr et al (2011) Hippocampal CA3 transcriptome signature correlates with initial precipitating injury in refractory mesial temporal lobe epilepsy. PLoS One 6(10):e26268CrossRefGoogle Scholar
  5. Bando SY, Silva FN, Costa Lda F et al (2013) Complex network analysis of CA3 transcriptome reveals pathogenic and compensatory pathways in refractory temporal lobe epilepsy. PLoS One 8(11):e79913CrossRefGoogle Scholar
  6. Barabási AL, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113PubMedCrossRefGoogle Scholar
  7. Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network based approach to human disease. Nat Rev Genet 13:56–68CrossRefGoogle Scholar
  8. Benson M, Breitling R (2006) Network Theory to understand microarray studies of complex diseases. Curr Mol Med 6:695–701PubMedCrossRefGoogle Scholar
  9. Brandes U (2001) A Faster Algorithm for Betweenness Centrality. J Math Sociol 25:163–177CrossRefGoogle Scholar
  10. Brazma A, Hingcamp P, Quackenbush J et al (2001) Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat Genet 29:365–371PubMedCrossRefGoogle Scholar
  11. Cai JJ, Borenstein E, Petrov DA (2010) Broker genes in human disease. Genome Biol Evol 2:815–825PubMedCentralPubMedCrossRefGoogle Scholar
  12. Carter H, Hofree M, Ideker T (2013) Genotype to phenotype via network analysis. Curr Opin Genet Dev 23:611–621PubMedCrossRefGoogle Scholar
  13. Clauset A, Shallizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51:661–703CrossRefGoogle Scholar
  14. Chuang H-Y, Hofree M, Ideker Y (2010) A decade of systems biology. Annu Rev Cell Dev Biol 26:721–744PubMedCentralPubMedCrossRefGoogle Scholar
  15. Costa L da F, Tognetti MAR, Silva FN (2008) Concentric characterization and classification of complex network nodes: application to an institutional collaboration network. Phys A 387:6201–6214CrossRefGoogle Scholar
  16. Costa L da F, Oliveira ON Jr, Travieso G et al (2011) Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Adv Phys 60:329–412CrossRefGoogle Scholar
  17. Costanzo M, Baryshnikova A, Bellay J et al (2010) The genetic landscape of a cell. Science 327:425–431PubMedCrossRefGoogle Scholar
  18. Cristino AS, Williams SM, Hawi Z, An JY, Bellgrove MA, Schwartz CE, Costa Lda F, Claudianos C (2014) Neurodevelopmental and neuropsychiatric disorders represent an interconnected molecular system. Mol Psychiatry 19:294–301PubMedCrossRefGoogle Scholar
  19. De Las Rivas J, Fontanillo C (2010) Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol 6:e1000807CrossRefGoogle Scholar
  20. Del Rio G, Koschutzki D, Coello G (2009) How to identify essential genes from molecular networks? BMC Syst Biol 3:102PubMedCentralPubMedCrossRefGoogle Scholar
  21. Elo LL, Järvenpää H, Oresic M et al (2007) Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics 23:2096–2103PubMedCrossRefGoogle Scholar
  22. Faro A, Giordano D, Spampinato C (2012) Combining literature text mining with microarray data: advances for system biology modeling. Brief Bioinform 13:61–82PubMedCrossRefGoogle Scholar
  23. Flake GW, Lawrence SR, Giles CL et al (2002) Self-organization and identification of Web communities. IEEE Computer 35:66–71CrossRefGoogle Scholar
  24. Freeman LC (1978) Centrality in social networks: conceptual clarification. Soc Netw 1:215–239CrossRefGoogle Scholar
  25. Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement software. Pract Exp 21:1129–1164CrossRefGoogle Scholar
  26. Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci U S A 99:7821–7826PubMedCentralPubMedCrossRefGoogle Scholar
  27. Herbst A, Jurinovic V, Krebs S et al (2014) Comprehensive analysis of β-catenin target genes in colorectal carcinoma cell lines with deregulated Wnt/β-catenin signaling. BMC Genomics 15:74PubMedCentralPubMedCrossRefGoogle Scholar
  28. Horvath S, Dong J (2008) Geometric interpretation of gene coexpression network analysis. PLoS Comput Biol 4:e1000117CrossRefGoogle Scholar
  29. Ideker T, Krogan NJ (2012) Differential network biology. Mol Syst Biol 8:565PubMedCentralPubMedCrossRefGoogle Scholar
  30. Ishiwata RR, Morioka MS, Ogishima S et al (2009) BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network. Bioinformatics 25:543–544PubMedCrossRefGoogle Scholar
  31. Kim Y-A, Wuchty S, Przytycka TM (2011) Identifying causal genes and dysregulated pathways in complex diseases. PLoS Comput Biol 7(3):e1001095Google Scholar
  32. Langfelder P, Mischel PS, Horvath S (2013) When is hub gene selection better than standard meta-analysis? PLoS ONE 8:e61505CrossRefGoogle Scholar
  33. Lee WP, Tzou WS (2009) Computational methods for discovering gene networks from expression data. Brief Bioinform 10:408–423PubMedGoogle Scholar
  34. Li A, Horwath S (2009) Network module detection: affinity search technique with the multi-node topological overlap measure. BMC Res Notes 2:142PubMedCentralPubMedCrossRefGoogle Scholar
  35. Liu YY, Slotine JJ, Barabási AL (2011) Controllability of complex networks. Nature 473(7346):167–173PubMedCrossRefGoogle Scholar
  36. Liu YY, Slotine JJ, Barabási AL (2012) Control centrality and hierarchical structure in complex networks. PLoS ONE 7(9):e44459Google Scholar
  37. Mcauley JJ, Costa L da F, Caetano TS (2007) Rich-club phenomenon across complex network hierarchies. Appl Phy Lett 91:084103CrossRefGoogle Scholar
  38. Masuda N, Konno N (2006) VIP-club phenomenon: emergence of elites and masterminds in social networks. Soc Netw 28:297–309CrossRefGoogle Scholar
  39. Milo R, Shen-Orr S, Itzkovitz S et al (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827PubMedCrossRefGoogle Scholar
  40. Miron M, Woody OZ, Marcil A et al (2006) A methodology for global validation of microarray experiments. BMC Bioinform 7:333CrossRefGoogle Scholar
  41. Newman MEJ (2006) Modularity and community structure in networks. PNAS 103:8577–8582PubMedCentralPubMedCrossRefGoogle Scholar
  42. Newman MEJ (2010) Networks: an Introduction. Oxford University, New YorkCrossRefGoogle Scholar
  43. Pavlopoulos GA, O’Donoghue SI, Satagopam VP et al (2008) Arena3D: visualization of biological networks in 3D. BMC Systems Biology 2:104 (–0509/2/104)
  44. Prifti E, Zucker JD, Clement K et al (2008) Funnet: an integrative tool for exploring transcriptional interactions. Bioinformatics 24:2636–2638PubMedCrossRefGoogle Scholar
  45. R Core Team (2012) R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria (
  46. Ravasz E, Somera AL, Mongru DA (2002) Hierarchical organization of modularity in metabolic networks. Science 297:1551–1555PubMedCrossRefGoogle Scholar
  47. Rosenkrantz JT, Aarts H, Abee T et al (2013) Non-essential genes form the hubs of genome scale protein function and environmental gene expression networks in Salmonella enterica serovar Typhimurium. BMC Microbiol 13:294PubMedCentralPubMedCrossRefGoogle Scholar
  48. Saeed AS, White J et al (2003) TM4: a free, open-source system for microarray data management and analysis. Biotechniques 34:374–378PubMedGoogle Scholar
  49. Sahni N, Yi S, Zhong Q et al (2013) Edgotype: a fundamental link between genotype and phenotype. Curr Opin Genet Dev 23:649–657PubMedCentralPubMedCrossRefGoogle Scholar
  50. Saito R, Smoot ME, Ono K et al (2012) A travel guide to cytoscape plugins. Nat Methods 9:1069–1076PubMedCentralPubMedCrossRefGoogle Scholar
  51. Schroeder A, Mueller O, Stocker S et al (2006) The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7:3PubMedCentralPubMedCrossRefGoogle Scholar
  52. Shen-Orr SS, Milo R, Mangan S et al (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31:64–68PubMedCrossRefGoogle Scholar
  53. Shi L, Perkins RG, Fang H et al (2008) Reproducible and reliable microarray results through quality control: good laboratory proficiency and appropriate data analysis practices are essential. Curr Opin Biotechnol 19:10–18PubMedCrossRefGoogle Scholar
  54. Sieberts SK, Schadt EE (2007) Moving toward a system genetics view of disease. Mamm Genome 18:389–401PubMedCentralPubMedCrossRefGoogle Scholar
  55. Silva FN, Rodrigues FA, Oliveira ON Jr et al (2013) Quantifying the interdisciplinarity of scientific journals and fields. J Informetr 7:469–477CrossRefGoogle Scholar
  56. Song L, Langfelder P, Horvath S (2012) Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinform 13:328CrossRefGoogle Scholar
  57. Taylor IW, Linding R, Wade-Farley D et al (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature Biotech 27:199–204CrossRefGoogle Scholar
  58. True L, Feng Z (2005) Immunohistochemical validation of expression microarray results. J Mol Diagn 7:149–151PubMedCentralPubMedCrossRefGoogle Scholar
  59. Tuck DP, Kluger HM, Kluger Y (2006) Characterizing disease states from topological properties of transcriptional regulatory networks. BMC Bioinform 7:236CrossRefGoogle Scholar
  60. Villa-Vialaneix N, Liaubet L, Laurent T et al (2013) The structure of a gene co-expression network reveals biological functions underlying eQTLs. PLoS One 8:e60045CrossRefGoogle Scholar
  61. Wang H, Zheng H (2012) Correlation of genetic features with dynamic modularity in the yeast interactome: a view from the structural perspective. IEEE Trans Nanobiosciences 11:244–250CrossRefGoogle Scholar
  62. Wang Q, Tang B, Song L et al (2013) 3DScapeCS: application of 3 dimensional, parallel, dynamic network visualization in Cytoscape BMC Bioinformatics 14:322 (–2105/14/322)
  63. Wang XD, Huang JL, Yang L et al (2014) Identification of human disease genes from interactome network using graphlet interaction. PLoS One 9:e86142Google Scholar
  64. Watkinson J, Liang KC, Wang X (2009) Inference of regulatory gene interactions from expression data using three-way mutual information. Ann NY Acad Sci 1158:302–313PubMedCrossRefGoogle Scholar
  65. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small word’ networks. Nature 393:440–442PubMedCrossRefGoogle Scholar
  66. Weirauch MT (2011) Gene expression network for the analysis of cDNA microarray data. In: Dehmer M, Emmert-Streib F, Graber A, Salvador A (eds) Applied statistics for network biology: methods in systems biology, vol 1. Wiley, Weinheim, pp 215–250CrossRefGoogle Scholar
  67. Weiss JM, Karma A, MacLellan WR et al (2012) “Good enough solutions” and the genetics of complex diseases. Circ Res 111:493–504PubMedCentralPubMedCrossRefGoogle Scholar
  68. Winterbach W, Van Mieghem P, Reinders M et al (2013) Topology of molecular interaction networks. BMC Syst Biol 7:90PubMedCentralPubMedCrossRefGoogle Scholar
  69. Wu X, Wang W, Zheng WX (2012) Inferring topologies of complex networks with hidden variables. Phys Rev E 86:046106CrossRefGoogle Scholar
  70. Yu H, Kim PM, Sprecher E et al (2007) The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3:e59Google Scholar
  71. Yuan Z, Zhao C, Di Z et al (2013) Exact controllability of complex networks. Nat Commun 4:2447PubMedCentralPubMedGoogle Scholar
  72. Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4:Article17Google Scholar
  73. Zhang J, Ji Y, Zhang L (2007) Extracting three-way gene interactions from microarray data. Bioinformatics 23:2903–2909PubMedCrossRefGoogle Scholar
  74. Zhu X, Gerstein M, Snyder M (2007) Getting connected: analysis and principles of biological networks. Genes Dev 21:1010–1024PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Carlos Alberto Moreira-Filho
    • 1
    Email author
  • Silvia Yumi Bando
    • 1
  • Fernanda Bernardi Bertonha
    • 1
  • Filipi Nascimento Silva
    • 2
  • Luciano da Fontoura Costa
    • 2
  1. 1.Departamento de PediatriaFaculdade de Medicina da Universidade de São PauloSão PauloBrazil
  2. 2.Instituto de Física de São CarlosUniversidade de São PauloSão CarlosBrazil

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