FacPad-package: Sparse factor modeling for the inference of drug-responsive...

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

Description

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.

Details

Package:	FacPad
Type:	Package
Version:	2.0
Date:	2014-03-25
License:	GPL (>= 2)
LazyLoad:	yes

install.packages("FacPad")

Author(s)

Haisu Ma<[e-mail]>

Examples

data(matrixY)
data(matrixL)
result<-gibbs_sampling(matrixY,matrixL,max_iter=30,
thin=10,file_name="test_30iter.RData")

result2<-gibbs2(matrixY,matrixL,eta0=0.2,eta1=0.2,
max_iter=50,thin=10,file_name="test_v2_50iter.RData")

FacPad documentation built on May 2, 2019, 3:46 p.m.