DiffCorr: Analyzing and Visualizing Differential Correlation Networks in Biological Data

A method for identifying pattern changes between 2 experimental conditions in correlation networks (e.g., gene co-expression networks), which builds on a commonly used association measure, such as Pearson's correlation coefficient. This package includes functions to calculate correlation matrices for high-dimensional dataset and to test differential correlation, which means the changes in the correlation relationship among variables (e.g., genes and metabolites) between 2 experimental conditions.

Install the latest version of this package by entering the following in R:
AuthorAtsushi Fukushima, Kozo Nishida
Date of publication2015-04-02 00:07:09
MaintainerAtsushi Fukushima <atsushi.fukushima@riken.jp>
LicenseGPL (> 3)

View on CRAN


AraMetLeaves Man page
AraMetRoots Man page
cluster.molecule Man page
comp.2.cc.fdr Man page
compcorr Man page
cor2.test Man page
cor.dist Man page
DiffCorr Man page
DiffCorr-package Man page
generate_g Man page
get.eigen.molecule Man page
get.eigen.molecule.graph Man page
get.lfdr Man page
get.min.max Man page
plotClusterMolecules Man page
plotDiffCorrGroup Man page
scalingMethods Man page
uncent.cor2dist Man page
uncent.cordist Man page
write.modules Man page

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

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