DGCA: Differential Gene Correlation Analysis

Performs differential correlation analysis on input matrices, with multiple conditions specified by a design matrix. Contains functions to filter, process, save, visualize, and interpret differential correlations of identifier-pairs across the entire identifier space, or with respect to a particular set of identifiers (e.g., one). Also contains several functions to perform differential correlation analysis on clusters (i.e., modules) or genes. Finally, it contains functions to generate empirical p-values for the hypothesis tests and adjust them for multiple comparisons. Although the package was built with gene expression data in mind, it is applicable to other types of genomics data as well, in addition to being potentially applicable to data from other fields entirely. It is described more fully in the manuscript introducing it, freely available at <doi:10.1186/s12918-016-0349-1>.

Version: 1.0.1
Depends: R (≥ 3.2)
Imports: WGCNA, matrixStats, methods
Suggests: knitr, impute, gplots, fdrtool, testthat, ggplot2, plotrix, GOstats, HGNChelper, org.Hs.eg.db, AnnotationDbi, abind, MEGENA, Matrix, doMC, igraph, cowplot, stats, utils
Published: 2016-11-17
Author: Bin Zhang [aut], Andrew McKenzie [aut, cre]
Maintainer: Andrew McKenzie <amckenz at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: README
CRAN checks: DGCA results

Downloads:

Reference manual: DGCA.pdf
Vignettes: Basic DGCA Vignette
Package source: DGCA_1.0.1.tar.gz
Windows binaries: r-devel: DGCA_1.0.1.zip, r-release: DGCA_1.0.1.zip, r-oldrel: DGCA_1.0.1.zip
OS X El Capitan binaries: r-release: not available
OS X Mavericks binaries: r-oldrel: DGCA_1.0.1.tgz

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