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.
|Depends:||R (≥ 2.13.2), corpcor, foreach, multcomp, randomForest, MASS|
|Author:||Chih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen|
|Maintainer:||Chih-Yi Chien <92354503 at nccu.edu.tw>|
|CRAN checks:||MAVTgsa results|
|Windows binaries:||r-devel: MAVTgsa_1.3.zip, r-release: MAVTgsa_1.3.zip, r-oldrel: MAVTgsa_1.3.zip|
|OS X Mavericks binaries:||r-release: MAVTgsa_1.3.tgz, r-oldrel: MAVTgsa_1.3.tgz|
|Old sources:||MAVTgsa archive|
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