FacPad is the inference of * Pa*thways
responsive to

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):

Figure. Illustration of the bayesian sparse factor model used for the analysis of treatment response data.

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:http://www.broadinstitute.org/cmap/cel_file_chunks.jsp

Pathways associated with the probesets were derived using the functional annotation function of the Database for Annotation, Visualization and Integrated Discovery (DAVID):

http://david.abcc.ncifcrf.gov/summary.jsp

Software

The bayesian sparse factor model is implemented as the R package "FacPad" on CRAN:

http://cran.open-source-solution.org/web/packages/FacPad/