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.
|Author||Chih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen|
|Date of publication||2014-07-02 13:48:35|
|Maintainer||Chih-Yi Chien <firstname.lastname@example.org>|
data: Example data for MAVTn
design.matrix: Design matrix
GS: Example data for MAVTn
GSTplot: GST plot
Hott2: Hottelling's T square
ma.estimate: Estimate of the coefficients
MAVTgsa-package: OLS and Multivariate Analysis of Variance test for gene set...
MAVTn: OLS, Hottelling's T2 and MANOVA with n contrasts
MAVTp: Random Forests-based procedure
minp: P-values adjustment in permutation
Tols: Ordinary Least Square test
Wilksn: Wilk's Lambda for n-group multiple comparisons