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Review of Weighted Gene Coexpression Network Analysis

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Book cover Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

We survey key concepts of weighted gene coexpression network analysis (WGCNA), also known as weighted correlation network analysis, and related data analysis strategies. We describe the construction of a weighted gene coexpression network from gene expression data, identification of network modules and integration of external data such as gene ontology information and clinical phenotype data. We review Differential Weighted Gene Coexpression Network Analysis (DWGCNA), a method for comparing and contrasting networks constructed from qualitatively different groups of samples. DWGCNA provides a means for measuring not only differential expression but also differential connectivity. Further, we show how to incorporate genetic marker data with expression data via Integrated Weighted Gene Coexpression Network Analysis (IWGCNA). Lastly, we describe R software implementing WGCNA methods.

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Acknowledgements

We would like to acknowledge the grant support from 1U19AI063603-01, 5P30CA016042-28, P50CA092131, and DK072206. The authors would like to thank UCLA collaborators Jun Dong, Jake Lusis, Tom Drake, Dan Geschwind, Wen Lin, Paul Mischel, Mike Oldham, and Wei Zhao for useful discussions.

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Authors and Affiliations

  1. Human Genetics and Biostatistics, University of California, Los Angeles, USA

    Tova Fuller, Peter Langfelder, Angela Presson & Steve Horvath

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  1. Tova Fuller
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  2. Peter Langfelder
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  3. Angela Presson
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  4. Steve Horvath
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Correspondence to Steve Horvath .

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Editors and Affiliations

  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Fuller, T., Langfelder, P., Presson, A., Horvath, S. (2011). Review of Weighted Gene Coexpression Network Analysis. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_18

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