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 CRAN with:
You can install the development version of DGCA from github with:
# install.packages("devtools") devtools::install_github("andymckenzie/DGCA")
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:
- DGCA Basic: This will get you going quickly.
- DGCA: This is a more extended version that explains a bit about how the package works and shows several of the options available in the package.
- DGCA Modules: This will show you how to use the package to perform module-based and network-based analyses.
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: