Weighted Gene Coexpression Network Analysis: State of the Art

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Authors

Wei Zhao · St. Jude Children's Research Hospital

Peter Langfelder · University of California, Los Angeles

Tova F Fuller · University of California, Los Angeles

Jun Dong · Amgen (United States)

Li Ai · Tris Pharma (United States)

Steve Hovarth · University of California, Los Angeles

Abstract

Weighted gene coexpression network analysis (WGCNA) has been applied to many important studies since its introduction in 2005. WGCNA can be used as a data exploratory tool or as a gene screening method; WGCNA can also be used as a tool to generate testable hypothesis for validation in independent data sets. In this article, we review key concepts of WGCNA and some of its applications in gene expression analysis of oncology, brain function, and protein interaction data.

Topics & Keywords

Bioinformatics and Genomic Networks Gene expression and cancer classification Gene Regulatory Network Analysis

Publication Details

Published in: Journal of Biopharmaceutical Statistics

Volume 20, Issue 2, pp. 281-300

DOI: 10.1080/10543400903572753

Field-Weighted Citation Impact: 4.20

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Weighted Gene Coexpression Network Analysis: State of the Art

Authors

Abstract

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Publication Details