Welcome to the homepage of FacPad!
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):
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/