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.
||R (≥ 2.13.2), corpcor, foreach, multcomp, randomForest, MASS
||Chih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen
||Chih-Yi Chien <92354503 at nccu.edu.tw>
Please use the canonical form
to link to this page.