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
The goal of DGCA is to calculate differential correlations across conditions.
It simplifies the process of seeing whether two correlations are different without having to rely solely on parametric assumptions by leveraging non-parametric permutation tests and adjusting the resulting empirical p-values for multiple corrections using the qvalue R package.
It also has several other options including calculating the average differential correlation between groups of genes, gene ontology enrichment analyses of the results, and differential correlation network identification via integration with MEGENA.
You can install DGCA from github with:
library(DGCA)data(darmanis); data(design_mat)ddcor_res = ddcorAll(inputMat = darmanis, design = design_mat, compare = c("oligodendrocyte", "neuron"))head(ddcor_res, 3)# Gene1 Gene2 oligodendrocyte_cor oligodendrocyte_pVal neuron_cor neuron_pVal# 1 CACYBP NACA -0.070261455 0.67509118 0.9567267 0# 2 CACYBP SSB -0.055290516 0.74162636 0.9578999 0# 3 NDUFB9 SSB -0.009668455 0.95405875 0.9491904 0# zScoreDiff pValDiff empPVals pValDiff_adj Classes# 1 10.256977 1.100991e-24 1.040991e-05 0.6404514 0/+# 2 10.251847 1.161031e-24 1.040991e-05 0.6404514 0/+# 3 9.515191 1.813802e-21 2.265685e-05 0.6404514 0/+
There are three vignettes available in order to help you learn how to use the package:
The second two vignettes can be found in inst/doc.
You can view the manuscript describing DGCA in detail as well as several applications here:
Material for associated simulations and networks created from MEGENA can be found here: