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ASSIGN (version 1.8.0)

Adaptive Signature Selection and InteGratioN (ASSIGN)

Description

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.

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Version

Version

1.8.0

License

MIT

Maintainer

Ying Shen

Last Published

February 15th, 2017

Functions in ASSIGN (1.8.0)

assign.cv.output

Cross validation output
trainingData1

Gene expression profiling from cell line preturbation experiments (training dataset)
assign.output

Prediction/validation output for test data
assign.convergence

Check the convergence of the MCMC chain
assign.mcmc

The Gibbs sampling algorithm to approximate the joint distribution of the model parameters
assign.preprocess

Input data preprocessing
assign.wrapper

ASSIGN All-in-one function
assign.summary

Summary of the model parameters estimated by the Gibbs sampling algorithm
geneList1

Pathway signature gene sets
testData1

Gene expression profiling from cancer patients (test dataset)