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.


Reference manual

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1.3 by Chih-Yi Chien, 7 years ago

Browse source code at

Authors: Chih-Yi Chien , Chen-An Tsai , Ching-Wei Chang , and James J. Chen

Documentation:   PDF Manual  

GPL-2 license

Depends on corpcor, foreach, multcomp, randomForest, MASS

See at CRAN