rsgcc-package

rsgcc


	This package provides functions for calculating the Gini, the Pearson, the Spearman, the Kendall and Tukey's Biweight correlations, Compared to the other mentioned correlation methods, the GCC may perform better to detect regulatory relationships from gene expression data. In addition, the GCC also has some other advantageous merits, such as independent of distribution forms, more capable of detecting non-linear relationships, more tolerant to outliers and less dependence on sample size. For  more information about these correlation methods, please refer to (Ma and Wang, 2012). This package also provides an graphical user interface (GUI) to perform clustering analysis of microarray and RNA-Seq data in a coherent step-by-step manner.


 package

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).

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

Description

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Details

References