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.

Install the latest version of this package by entering the following in R:
install.packages("MAVTgsa")
AuthorChih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen
Date of publication2014-07-02 13:48:35
MaintainerChih-Yi Chien <92354503@nccu.edu.tw>
LicenseGPL-2
Version1.3

View on CRAN

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.