andymckenzie/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>.

Getting started

Package details

AuthorBin Zhang [aut], Andrew McKenzie [aut, cre]
MaintainerAndrew McKenzie <[email protected]>
LicenseGPL-3
Version1.0.1
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("andymckenzie/DGCA")
andymckenzie/DGCA documentation built on May 10, 2017, 10:09 p.m.