Weighted Gene Coexpression Network Analysis: State of the Art

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 da...

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Veröffentlicht in:	Journal of biopharmaceutical statistics 2010-03, Vol.20 (2), p.281-300
Hauptverfasser:	Zhao, Wei, Langfelder, Peter, Fuller, Tova, Dong, Jun, Li, Ai, Hovarth, Steve
Format:	Artikel
Sprache:	eng
Schlagworte:	Animals Bayes Theorem Biostatistics Brain Brain - metabolism Cancer Data Interpretation, Statistical Gene expression Gene Expression Profiling - statistics & numerical data Gene Expression Regulation, Neoplastic Gene Regulatory Networks Genetic testing Humans Models, Statistical Network Oligonucleotide Array Sequence Analysis - statistics & numerical data Oncology Pan troglodytes Protein Interaction Mapping - statistics & numerical data Proteins RNA, Messenger - metabolism Species Specificity Studies WGCNA
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Beschreibung
Zusammenfassung:	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.
ISSN:	1054-3406 1520-5711
DOI:	10.1080/10543400903572753