Open Journal Systems

Weighted gene co-expression network nalysis inbiomedicine research

Guillermo Guillen-Gamez, Héctor Migallón

Article ID: 885
Vol 2, Issue 3, , Article identifier:

VIEWS - 287 (Abstract) 278 (PDF)


High-throughput biological technologies are now widely applied in biology and medicine, allowing scientists to monitor thousands of parameters simultaneously in a specific sample. However, it is still an enormous challenge to mine useful information from high-throughput data. The emergence of network biology provides deeper insights into complex bio-system and reveals the modularity in tissue/cellular networks. Correlation networks are increasingly used in bioinformatics applications. Weighted gene co-expression network analysis (WGCNA) tool can detect clusters of highly correlated genes. Therefore, we systematically reviewed the application of WGCNA in the study of disease diagnosis, pathogenesis and other related fields. First, we introduced principle, workflow, advantages and disadvantages of WGCNA. Second, we presented the application of WGCNA in disease, physiology, drug, evolution and genome annotation. Then, we indicated the application of WGCNA in newly developed high-throughput methods. We hope this review will help to promote the application of WGCNA in biomedicine research.


Weighted gene co-expression network analysis; high-throughput technology; disease; physiology; drug; evolution; genome annotation

Full Text:



Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005; 4(1): 17.

Horvath S. Weighted Network Analysis: Applications in Genomics and Systems Biology. New York: Springer 2011.

Wang L, Tang H, Thayanithy V, et al. Gene networks and microRNAs implicated in aggressive prostate cancer. Cancer Res 2009; 69(24):9490 - 9497.

Ivliev AE, t Hoen PAC, Sergeeva MG. Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma. Cancer Res 2010; 70(24): 10060-10070.

Hu Y, Wu G, Rusch M, et al. Integrated cross-species transcriptional network analysis of metastatic susceptibility. Proc Natl Acad Sci USA 2012; 109(8): 3184-31899.

Giotti B, Joshi A, Freeman TC. Meta-analysis reveals conserved cell cycle transcriptional network across multiple human cell types. BMC Genomics 2017; 18: 30.

Voineagu I, Wang XC, Johnston P, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 2011; 474(7351): 380-384.

Shirasaki DI, Greiner ER, Al-Ramahi I, et al. Network organization of the huntingtin proteomic interactome in mammalian brain. Neuron 2012; 75(1): 41-57.

Wisniewski N, Cadeiras M, Bondar G, et al. Weighted gene coexpression network analysis (WGCNA) modeling of multiorgan dysfunction syndrome after mechanical circulatory support therapy. J Heart Lun g Transpl 2013; 32(4): S223.

Chen C, Cheng L, Grennan K, et al. Two gene co-expression modules differentiate psychotics and controls. Mol Psych 2013; 18(12): 1308-1314.

Presson AP, Yoon NK, Bagryanova L, et al.Protein expression based multimarker analysis of breast cancer samples. BMC Cancer 2011; 11: 230.

Leuchter AF, Cook IA, Hunter AM, et al. Resting-state quantitative electroencephalography reveals increased neurophysiologic connectivity in depression. PLoS ONE 2012; 7(2): e32508.

Emilsson V, Thorleifsson G, Zhang B, et al. Genetics of gene expression and its effect on disease. Nature 2008; 452(7186): 423-428.

Oldham MC, Konopka G, Iwamoto K, et al. Functional organization of the transcriptome in human brain. Nat Neurosci 2008; 11(11):1271-1282.

Mozhui K, Lu L, Armstrong WE, et al. Sex-specific modulation of gene expression networks in murine hypothalamus. Front Neurosci 2012; 6: 63.

Horvath S, Zhang YF, Langfelder P, et al. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol 2012; 13(10): R97.

Mason MJ, Fan GP, Plath K, et al. Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells. BMC Genomics 2009; 10: 327.

Xue ZG, Huang K, Cai CC, et al. Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing. Nature 2013; 500(7464): 593-597.

Calabrese G, Bennett BJ, Orozco L, et al. Systems genetic analysis of osteoblast-lineage cells. PLoS Genet 2012; 8(12): e1003150.

Clarke C, Doolan P, Barron N, et al. Large scale microarray profiling and coexpression network analysis of CHO cells identifies transcriptional modules associated with growth and productivity. J Biotechnol 2011; 155(3): 350-359.

DiLeo MV, Strahan GD, den Bakker M, et al. Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome. PLoS ONE 2011; 6(10): e26683.

Fortney K, Xie W, Kotlyar M, et al. NetwoRx:Connecting drugs to networks and phenotypes in Saccharomyces cerevisiae. Nucleic Acids Res 2013; 41(D1): D720-D727.

Iskar M, Zeller G, Blattmann P, et al. Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding. Mol Syst Biol 2013; 9(1): 662.

Delahaye-Duriez A, Srivastava P, Shkura K, et al. Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery. Genome Bi ol 2016; 17: 245.

Miller JA, Horvath S, Geschwind DH. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proc Natl Acad Sci USA 2010; 107(28): 12698-12703.

Oldham MC, Horvath S, Geschwind DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc Natl Acad Sci USA 2006; 103(47): 17973-17978.

Filteau M, Pavey SA, St-Cyr J, et al. Gene coexpression networks reveal key drivers of phenotypic divergence in lake whitefish. Mol Biol Evol 2013; 30(6): 1384-1396.

Hu GJ, Hovav R, Grover CE, et al. Evolutionary conservation and divergence of gene coexpression networks in gossypium (cotton) seeds. Genome Biol Evol 2016; 8(12): 3765-3783.

L ó pez-Kleine L, Leal L, L ó pez C. Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data. Brief Funct Genomics 2013; 12(5): 457-467.

Stanley D, Watson-Haigh NS, Cowled CJ, et al. Genetic architecture of gene expression in the chicken. BMC Genomics 2013; 14: 13.

Childs KL, Davidson RM, Buell CR. Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS ONE 2011; 6(7): e22196.

Walley JW, Sartor RC, Shen ZX, et al. Integration of omic networks in a developmental atlas of maize. Science 2016; 353(6301): 814-818.

Schadt EE, Bjrkegren JL. NEW: network-enabled wisdom in biology, medicine, and health care. Sci Transl Med 2012; 4(115): 115 rv1.

Weiss JN, Karma A, MacLellan WR, et al. "Good enough solutions" and the genetics of complex diseases. Circul Res 2012; 111(4): 493-504.

Iancu OD, Kawane S, Bottomly D, et al. Utilizing RNA-Seq data for de novo coexpression network inference. Bioinformatics 2012; 28(12): 1592-1597.

Yepes S, L ó pez R, Andrade RE, et al. Co-expressed miRNAs in gastric adenocarcinoma. Genomics 2016;108(2): 93-101.

Liu W, Li L, Li WD. Gene co-expression analysis identifies common modules related to prognosis and drug resistance in cancer cell lines. Int J Cancer 2014; 135(12): 2795-2803.

Poulin JF, Tasic B, Hjerling-Leffler J, et al. Disentangling neural cell diversity using single-cell transcriptomics. Nat Neurosci 2016; 19(9):1131-1141.

Yan SM, Wu G. Network analysis of fine particulate matter (PM2.5) emissions in China. Sci Rep 2016; 6: 33227.

(287 Abstract Views, 278 PDF Downloads)


  • There are currently no refbacks.

Copyright (c) 2018 Guillermo Guillen-Gamez, Héctor Migallón

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Recent Articles | About | Author Guidlines | Submit

Copyright ©Whioce Publishing Pte Ltd. All rights reserved.