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: |
1.62 |
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), robust |
LinkingTo: |
Rcpp |
Suggests: |
org.Hs.eg.db, org.Mm.eg.db, infotheo, entropy, minet |
Published: |
2018-02-12 |
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 |