rsgcc: Gini methodology-based correlation and clustering analysis of microarray and RNA-Seq gene expression data

Brief introduction: This package provides functions for calculating associations between two genes with five correlation methods(e.g., the Gini correlation coefficient [GCC], the Pearson's product moment correlation coefficient [PCC], the Kendall tau rank correlation coefficient [KCC], the Spearman's rank correlation coefficient [SCC] and the Tukey's biweight correlation coefficient [BiWt], and three non-correlation methods (e.g., mutual information [MI] and the maximal information-based nonparametric exploration [MINE], and the euclidean distance [ED]). It can also been implemented to perform the correlation and clustering analysis of transcriptomic data profiled by microarray and RNA-Seq technologies. Additionally, this package can be further applied to construct gene co-expression networks (GCNs).

Installation

Note: This is a variant of original R package rsgcc(https://cran.r-project.org/web/packages/rsgcc/index.html) which does not provide GUI interface because the installation of dependency package "gWidgetsRGtk2" is not convenient for server users.

install.packages("devtools")
library(devtools)
install_github("cma2015/rsgcc")

For users who would like to use GUI interface (Windows system)

# install from cran
install.packages("rsgcc")

For users who would like to use GUI interface (ubuntu)

sudo apt-get install libgtk2.0-dev

install.packages("RGtk2")
install.packages("gWidgetsRGtk2")
install.packages("rsgcc")

For users who would like to use GUI interface (MAC OS X) Step 1: install R package RGtk2 Step 2:

install.packages("rsgcc")

Quickly start

library(rsgcc)
# calculate Gini correlation of two genes
data(rsgcc) #A gene expression matrix which contains 100 genes and 13 samples.
res <- cor.pair(idxvec = c(1,3), GEMatrix = rnaseq, rowORcol = "row", cormethod = "GCC")
res

Note: For more details about this package, please refer to https://github.com/cma2015/rsgcc/blob/master/rsgcc.pdf

Citation

Ma, Chuang, and Xiangfeng Wang. "Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis." Plant physiology (2012): pp-112.