Gini methodology-based correlation and clustering analysis of
both microarray and RNA-Seq gene expression data
R (>= 2.14.2), biwt, fBasics, snowfall, grDevices, gplots,
gWidgets, gWidgetsRGtk2, stringr
This package provides functions for calcluating Gini
correlation coefficient (GCC) and performing GCC-based
clustering analysis of gene expression data generated from both
microarray and next-generation sequencing (RNA-Seq)
technologies. Gini correlation outperforms Pearson correlation
and Spearman correlation to identify regulatory relationships
from both microarray and RNA-Seq data. In addtion, GCC has the
capability of more robustness on non-distribution, less
sensitivity to outlier data points and higher comatibility to
RNA-Seq data.

Gini methodology-based correlation and clustering analysis of
microarray and RNA-Seq gene expression data
R (>= 2.15.0), biwt, cairoDevice, fBasics, snowfall,
grDevices, gplots, gWidgets, gWidgetsRGtk2, stringr
This package provides functions for calcluating 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).
It can also been implemented to perform correlation and
clustering analysises on the microarray and RNA-Seq gene
expression data. Additionally, this package can be applied for
further constructing gene co-expression networks (GCNs).

This package provides functions for calculating
associations between two genes with different correlation
methods, including 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). 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).