It is Primarily based on the R programming language. It aims to provide access to a wide range of powerful statistical and graphical methods for the analysis of genomic data. Analysis packages are available for: pre-processing Affymetrix and cDNA array data;
Statistical discrimination metric and permutation analysis to identify clusters of genes or individual genes that best differentiate experimental groups
filter and preprocess data in a variety of ways; Self-Organizing Map; unsupervised classification by weighted voting (WV) and k-nearest neighbors (KNN) algorithms, gene selection and permutation test methods
Performs sample classification from gene expression data, Estimates prediction error via cross-validation, Provides a list of significant genes whose expression characterizes each diagnostic class
Correlates gene expression data to a wide variety of clinical parameters including treatment, diagnosis categories, survival time and time trends; Provides estimate of False Discovery Rate for multiple testing
