WGCNA: Weighted Correlation Network Analysis

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

Version: 1.69
Depends: R (≥ 3.0), dynamicTreeCut (≥ 1.62), fastcluster
Imports: stats, grDevices, utils, matrixStats (≥ 0.8.1), Hmisc, impute, splines, foreach, doParallel, preprocessCore, survival, parallel, GO.db, AnnotationDbi, Rcpp (≥ 0.11.0)
LinkingTo: Rcpp
Suggests: org.Hs.eg.db, org.Mm.eg.db, infotheo, entropy, minet
Published: 2020-02-28
Author: Peter Langfelder and Steve Horvath with contributions by Chaochao Cai, Jun Dong, Jeremy Miller, Lin Song, Andy Yip, and Bin Zhang
Maintainer: Peter Langfelder <Peter.Langfelder at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/
NeedsCompilation: yes
Citation: WGCNA citation info
Materials: ChangeLog
CRAN checks: WGCNA results

Downloads:

Reference manual: WGCNA.pdf
Package source: WGCNA_1.69.tar.gz
Windows binaries: r-devel: WGCNA_1.69.zip, r-release: WGCNA_1.69.zip, r-oldrel: WGCNA_1.69.zip
macOS binaries: r-release: WGCNA_1.69.tgz, r-oldrel: not available
Old sources: WGCNA archive

Reverse dependencies:

Reverse depends: diffcoexp, GOGANPA
Reverse imports: ADAPTS, CEMiTool, DCD, DGCA, DiPALM, eclust, epihet, fastLiquidAssociation, FREEtree, GmicR, GWENA, MCbiclust, miRSM, MODA, MRPC, netboost, Patterns, Pigengene, proBatch, RegEnrich, seq2pathway, spatialHeatmap, SPsimSeq, TIN
Reverse suggests: BioCor, cola, DDPNA, fuzzyforest, GOGANPA, gsean, scde, scGPS

Linking:

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