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iFad (version 2.0)

An integrative factor analysis model for drug-pathway association inference

Description

This package implements a bayesian sparse factor model for the joint analysis of paired datasets, one is the gene expression dataset and the other is the drug sensitivity profiles, measured across the same panel of samples, e.g., cell lines. Prior knowledge about gene-pathway associations can be easily incorporated in the model to aid the inference of drug-pathway associations.

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Version

Install

install.packages('iFad')

Monthly Downloads

4

Version

2.0

License

GPL (>= 2)

Maintainer

Haisu Ma

Last Published

October 29th, 2012

Functions in iFad (2.0)

data_simulation

Simulation of example dataset for the factor analysis model
matrixL2

The matrix representing prior belief for matrixZ2
matrixPi1

The bernoulli probability matrix for matrixZ1
ROC_plot

Calculate the AUC (area under curve) and generate ROC plot
Ymean_compare

Compare the infered Y_mean values with the true values
Y2_mean

The mean value used for the simulation of matrixY2
label_chain

Updated factor label configuration during the Gibbs sampling
iFad-package

An integrative factor analysis model for drug-pathway association inference
sigma2

Covariance matrix of the noise term for the drugs
gibbs_sampling

Gibbs sampling for the inference of the inference of parameters in the sparse factor analysis model
mcmc_trace_plot

Traceplot of the Gibbs sampling iterations
matrixX

The factor activity matrix
matrixW1

The factor loading matrix representing the gene-pathway association
matrixPr_chain

The updated posterior probability for matrixZ1&Z2 during Gibbs sampling
Y1_mean

The mean value used for the simulation of matrixY1
matrixW_chain

The updated matrixW during the Gibbs sampling
matrixZ2

Binary indictor matrix for matrixW2
matrixY1

The gene expression dataset
tau_g_chain

The updated tau_g in the Gibbs sampling process
matrixX_chain

The updated matrixX in the Gibbs sampling process
matrixZ_chain

The updated matrixZ in the Gibbs sampling process
matrixPi2

The bernoulli probability matrix for matrixZ2
matrixW2

The factor loading matrix representing the drug-pathway association
matrixZ1

The binary indicator matrix for matrixW1
matrixL1

The matrix representing prior belief for matrixZ1
sigma1

Covariance matrix of the noise term for the genes
matrixY2

The drug sensitivity matrix