An integrative factor analysis model for drug-pathway association inference

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Documentation for package ‘iFad’ version 3.0

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iFad-package An integrative factor analysis model for drug-pathway association inference
data_simulation Simulation of example dataset for the factor analysis model
gibbs_sampling Gibbs sampling for the inference of the inference of parameters in the sparse factor analysis model
iFad An integrative factor analysis model for drug-pathway association inference
label_chain Updated factor label configuration during the Gibbs sampling
matrixL1 The matrix representing prior belief for matrixZ1
matrixL2 The matrix representing prior belief for matrixZ2
matrixPi1 The bernoulli probability matrix for matrixZ1
matrixPi2 The bernoulli probability matrix for matrixZ2
matrixPr_chain The updated posterior probability for matrixZ1&Z2 during Gibbs sampling
matrixW1 The factor loading matrix representing the gene-pathway association
matrixW2 The factor loading matrix representing the drug-pathway association
matrixW_chain The updated matrixW during the Gibbs sampling
matrixX The factor activity matrix
matrixX_chain The updated matrixX in the Gibbs sampling process
matrixY1 The gene expression dataset
matrixY2 The drug sensitivity matrix
matrixZ1 The binary indicator matrix for matrixW1
matrixZ2 Binary indictor matrix for matrixW2
matrixZ_chain The updated matrixZ in the Gibbs sampling process
mcmc_trace_plot Traceplot of the Gibbs sampling iterations
ROC_plot Calculate the AUC (area under curve) and generate ROC plot
sigma1 Covariance matrix of the noise term for the genes
sigma2 Covariance matrix of the noise term for the drugs
tau_g_chain The updated tau_g in the Gibbs sampling process
Y1_mean The mean value used for the simulation of matrixY1
Y2_mean The mean value used for the simulation of matrixY2
Ymean_compare Compare the infered Y_mean values with the true values