KStest), scale (function RKStest) or
correlation structure (function GSNCAtest) between two conditions.
It also offers a graphical visualization tool for correlation networks to
examine the change in the net correlation structure of a gene set between two
conditions based on the minimum spanning trees
(function plotMST2.pathway).
The methods available in this package were proposed in Rahmatallah et. al. 2014
and Friedman and Rafsky 1979. The performance of different methods available
in this package was thoroughly tested using simulated data and microarray
datasets in Rahmatallah et. al. 2012 and Rahmatallah et. al. 2014. These
methods (except RKStest) can also be applied to RNA-Seq count
data given that proper normalization is used. Proper normalization must take
into account both the within-sample differences (mainly gene length) and
between-samples differences (library size or sequencing depth).
A recent work examining the performance of the tests in package GSAR using
simulated and real RNA-Seq dataset was submitted for publication and will be
cited in future versions of this package.Rahmatallah Y., Emmert-Streib F. and Glazko G. (2012) Gene set analysis for self-contained tests: complex null and specific alternative hypotheses. Bioinformatics 28, 3073--3080.
Friedman J. and Rafsky L. (1979) Multivariate generalization of the Wald-Wolfowitz and Smirnov two-sample tests. Ann. Stat. 7, 697--717.
igraph.