Three methods to identify differentially expressed gene sets,
ordinary least square test, Multivariate Analysis Of Variance
test with n contrasts and Random forest.
R (>= 2.13.2), corpcor, foreach, multcomp, randomForest, MASS
This package is a gene set analysis function for one-sided test (OLS), two-sided test (multivariate analysis of variance).
If the experimental conditions are equal to 2, the p-value for Hotelling's t^2 test is calculated.
If the experimental conditions are great than 2, the p-value for Wilks' Lambda is determined and post-hoc test is reported too.
Three multiple comparison procedures, Dunnett, Tukey, and sequential pairwise comparison, are implemented.
The program computes the p-values and FDR (false discovery rate) q-values for all gene sets.
The p-values for individual genes in a significant gene set are also listed.
MAVTgsa generates two visualization output: a p-value plot of gene sets (GSA plot) and a GST-plot of the empirical distribution function of the ranked test statistics of a given gene set.
A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.

AC Del Re & William T. Hoyt University of Wisconsin-Madison
This is an integrated meta-analysis package for conducting
a research synthesis with correlations. The unique feature of
this package is that there are user-friendly functions for
completing all statistical steps involved in a meta-analysis.
It uses recommended procedures as described in Cooper, Hedges,
& Valentine's Handbook of Research Synthesis and Meta-Analysis
(2009).

This is an integrated meta-analysis package for conducting
a research synthesis with correlational data. One of the unique
features of this package is in its integration of user-friendly
functions to complete all statistical steps involved in a meta-
analysis with correlations. It uses recommended procedures as
described in The Handbook of Research Synthesis and
Meta-Analysis (Cooper, Hedges, & Valentine, 2009).