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

This method tries to explain the gene-wise treatment response ratios in terms of the latent pathways. It uses bayesian sparse factor modeling to infer the loadings (weights) of each pathway on its associated probesets as well as the latent factor activity levels for each treatment.

Author
Haisu Ma
Date of publication
2014-03-28 00:31:36
Maintainer
Haisu Ma <haisu.ma.pku.2008@gmail.com>
License
GPL (>= 2)
Version
3.0

View on CRAN

Man pages

FacPad-package
Sparse factor modeling for the inference of drug-responsive...
gibbs2
A Collapsed Gibbs Sampling Algorithm for the Inference of...
gibbs_sampling
A Collapsed Gibbs Sampling Algorithm for the Inference of...
matrixL
Pathway structure matrix L
matrixY
The treatment response matrix

Files in this package

FacPad
FacPad/NAMESPACE
FacPad/data
FacPad/data/matrixL.rda
FacPad/data/matrixY.rda
FacPad/R
FacPad/R/gibbs_sampling.R
FacPad/R/gibbs2.R
FacPad/MD5
FacPad/DESCRIPTION
FacPad/man
FacPad/man/FacPad-package.Rd
FacPad/man/matrixL.Rd
FacPad/man/matrixY.Rd
FacPad/man/gibbs2.Rd
FacPad/man/gibbs_sampling.Rd