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FacPad: Bayesian Sparse Factor Analysis model for the inference of pathways responsive to drug treatment
FacPad is the inference of Pathways responsive to drug treatment via Bayesian sparse Factor modeling.
It requires two matrices as input datasets. The first matrix Y is the gene expression ratios before and after drug treatment. It has dimension G×J, where G is the number of probesets measured by the specific microarray platform and J is the number of different treatments (usually, a treatment is combination of certain drug, concentration, and treatment time). The second matrix L is the binary pathway structure matrix of dimension G×K, where K is the number of pathways associated with the probesets, with Lg,k=1 representing that the g-th probeset is mapped to the k-th pathway and Lg,k=0 otherwise. FacPad models each pathway as a latent factor which is the weighted combination of its associated probesets, and decomposes the matrix Y into loading matrix W (G×K) and factor activity matrix X (K×J):
The sparse structure of loading matrix W is determined by prior binary matrix L.
Microarray expression data before and after drug treatment was downloaded from the Connectivity Map database
Pathways associated with the probesets were derived using the functional annotation function of the Database for Annotation, Visualization and Integrated Discovery (DAVID).
The bayesian sparse factor model is implemented as the R package “FacPad” on CRAN.