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## FacPad: Bayesian Sparse Factor Analysis model for the inference of pathways responsive to drug treatment

**F**acPad is the inference of ** Pa**thways responsive to

**rug treatment via Bayesian sparse**

*d***tor modeling.**

*Fac*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 L_{g,k}=1 representing that the g-th probeset is mapped to the k-th pathway and L_{g,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.

**Dataset:**

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 **D**atabase for **A**nnotation, **V**isualization and** I**ntegrated **D**iscovery (**DAVID**).

**Software:**

The bayesian sparse factor model is implemented as the R package “FacPad” on CRAN.