**Welcome to the homepage of FacPad!**

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 **D**atabase for **A**nnotation, **V**isualization
and** I**ntegrated **D**iscovery (**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/__