WGCNA: Weighted Correlation Network Analysis

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. 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: 1.42
Depends: R (≥ 2.14), dynamicTreeCut (≥ 1.62), fastcluster
Imports: stats, grDevices, utils, matrixStats (≥ 0.8.1), Hmisc, impute, splines, foreach, doParallel, reshape, preprocessCore, survival, parallel
Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, infotheo, entropy, minet
Published: 2014-12-04
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://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA/
NeedsCompilation: yes
Citation: WGCNA citation info
Materials: ChangeLog
CRAN checks: WGCNA results


Reference manual: WGCNA.pdf
Package source: WGCNA_1.42.tar.gz
Windows binaries: r-devel: WGCNA_1.42.zip, r-release: WGCNA_1.42.zip, r-oldrel: WGCNA_1.42.zip
OS X Snow Leopard binaries: r-release: WGCNA_1.42.tgz, r-oldrel: WGCNA_1.42.tgz
OS X Mavericks binaries: r-release: WGCNA_1.42.tgz
Old sources: WGCNA archive

Reverse dependencies:

Reverse depends: GOGANPA
Reverse imports: nettools
Reverse suggests: GOGANPA