MAVTgsa: Three methods to identify differentially expressed gene sets, ordinary least square test, Multivariate Analysis Of Variance test with n contrasts and Random forest

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.

Version: 1.3
Depends: R (≥ 2.13.2), corpcor, foreach, multcomp, randomForest, MASS
Published: 2014-07-02
Author: Chih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen
Maintainer: Chih-Yi Chien <92354503 at>
License: GPL-2
NeedsCompilation: no
CRAN checks: MAVTgsa results


Reference manual: MAVTgsa.pdf
Package source: MAVTgsa_1.3.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: MAVTgsa_1.3.tgz, r-oldrel: MAVTgsa_1.3.tgz
OS X Mavericks binaries: r-release: MAVTgsa_1.3.tgz
Old sources: MAVTgsa archive