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DiffCorr

An R package to analyze and visualize differential correlations in biological networks.

Large-scale "omics" data can be used to infer underlying cellular regulatory networks in organisms, enabling us to better understand the molecular basis of disease and important traits. Correlation approaches, such as a hierarchical cluster analysis, have been widely used to analyze omics data. In addition to the changes in the mean levels of molecules in the omics data, it is important to know about the changes in the correlation relationship among molecules between 2 experimental conditions. The development of a tool to identify differential correlation patterns in omics data in an efficient and unbiased manner is therefore desirable.

We developed the DiffCorr package, a simple method for identifying pattern changes between 2 experimental conditions in correlation networks, which builds on a commonly used association measure, such as Pearson's correlation coefficient. DiffCorr calculates correlation matrices for each dataset, identifies the first principal component-based "eigen-molecules" in the correlation networks, and tests differential correlation between the 2 groups based on Fisher's z-test.

DiffCorr can explore differential correlations between 2 conditions in the context of post-genomics data types, namely transcriptomics and metabolomics. DiffCorr is simple to use in calculating differential correlations and is suitable for the first step towards inferring causal relationships and detecting biomarker candidates.

Installation

install.packages("devtools")
install.packages(c("igraph", "fdrtool"))

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(c("pcaMethods", "multtest"))

library(devtools)
install_github("afukushima/DiffCorr")

Documents

For short tutorial, please see here.

See also support page of the DiffCorr book here.

Updates

version 0.4.4 (Sep 23, 2024)

  • add tests and argument 'save' of comp.2.cc.fdr().

CHANGE: comp.2.cc.fdr() returns data frame.

version 0.4.3 (Sep 22, 2024)

  • add vignette

version 0.4.2 (Aug 23, 2023)

  • create DiffCorr-package.R

version 0.4.1 (Sep 4, 2015)

  • A metabolite data set from Arabidopsis leaves and roots by GC-TOF/MS

License

The DiffCorr package is free software; a copy of the GNU General Public License, version 3, is available at https://www.R-project.org/Licenses/GPL-3

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Version

Install

install.packages('DiffCorr')

Monthly Downloads

360

Version

0.4.4

License

GPL (> 3)

Last Published

September 30th, 2024

Functions in DiffCorr (0.4.4)

write.modules

Writing modules into a text file
AraMetRoots

A metabolite data set from Arabidopsis roots by GC-TOF/MS
compcorr

Compare two correlation coefficients
cor.dist

Additional distance functions correlation distance (1-r)
generate_g

Generating graph from data matrix
comp.2.cc.fdr

Export differential correlations between two conditions
cor2.test

Correlation Test
get.eigen.molecule

Get eigen molecule
cluster.molecule

Hierarchical clustering of molecules
plotDiffCorrGroup

Plot DiffCorr group
scalingMethods

scalingMethods
uncent.cor2dist

Additional distance functions correlation distance (uncentered)
plotClusterMolecules

Plot cluster molecules
get.min.max

Get minimum and maximum
get.lfdr

Getting local false discovery rate (lfdr)
uncent.cordist

Calculating all pairwise distances using correlation distance
get.eigen.molecule.graph

Getting graph from eigengene module list
AraMetLeaves

A metabolite data set from Arabidopsis leaves by GC-TOF/MS
DiffCorr-package

Differential correlations in omics datasets