ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literatures or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature.
|Author||Ying Shen, Andrea H. Bild, and W. Evan Johnson|
|Date of publication||None|
|Maintainer||Ying Shen <firstname.lastname@example.org>|
assign.convergence: Check the convergence of the MCMC chain
assign.cv.output: Cross validation output
assign.mcmc: The Gibbs sampling algorithm to approximate the joint...
assign.output: Prediction/validation output for test data
assign.preprocess: Input data preprocessing
assign.summary: Summary of the model parameters estimated by the Gibbs...
assign.wrapper: ASSIGN All-in-one function
geneList1: Pathway signature gene sets
testData1: Gene expression profiling from cancer patients (test dataset)
trainingData1: Gene expression profiling from cell line preturbation...