WGCNA: an R package for weighted correlation network analysis

By Peter Langfelder, Steve Horvath. BMC Bioinformatics, 2008

861 citations recorded by the source corpus.

BackgroundCorrelation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.ResultsThe WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.ConclusionThe WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/39dae53515afb42664369c291ec6d1ce34d778bd

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Citations in the corpus

A method for generating realistic correlation matrices (2011, background): “[26,27,43] 128 define a correlation structure with respect to a particular profile (or hub-observation).”
iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data (2018, methodology): “Gene co-expression networks can also be constructed with the WGCNA package [96].”
Shared and distinct transcriptomic cell types across neocortical areas (2017, positive): “To extract relevant transcriptomic features, we employed dimensionality reduction by weighted gene co-expression network analysis (WGCNA) (Langfelder and Horvath, 2008).”
Integrated Omics: Tools, Advances, and Future Approaches. (2019, methodology): “Some investigators have used the WGCNA approach (Langfelder & Horvath 2008) adopted from transcriptomics/microarray analyses for integrated omics workflows.”
Overlapping community detection in networks: The state-of-the-art and comparative study (2011, background): “iew work that has been done mostly for unweighted networks. However, there are number of applications where a weight bears signiﬁcant information (e.g., the correlation network in biological studies [Langfelder and Horvath 2008]). Algorithms that explicitly take weights into account and allow overla”
Assessment of network module identification across complex diseases (2019, neutral): “OmniPath integrates literature-curated human signaling pathways from 27 different sources, of which 20 provide causal interaction, 7 deliver undirected interactions.”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, methodology): “Given the weights, we can deﬁne biweight midcorrelation of x and y as: A modiﬁed version of biweight midcorrelation is implemented as function bicor in the WGCNA R package [5,20].”