gibbs2: A Collapsed Gibbs Sampling Algorithm for the Inference of...

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

Description

In each iteration, the algorithm iteratively updates each entry in the binary matrix Z, loading matrix W and factor activity matrix X, as well as other model parameters.

Usage

gibbs2(matrixY, matrixL,eta0,eta1,alpha_tau = 1, 
beta_tau = 0.01, tau_sig = 1, max_iter = 10000, 
thin = 10, alpha_sigma = 0.7, beta_sigma = 0.3, file_name)

Arguments

matrixY	The input treatment response matrix. It has dimension G by J,where G is the number of probesets and J is the number of different treatments. The (g,j)-th entry represents the ratio of the expression of the g-th probeset after and before the j-th treatment.
matrixL	The binary probeset-pathway association matrix.It has dimension G by K. If the (g,k)-th entry has value 1, it indicates that the g-th probeset is involved in the k-th pathway; and the (g,k)-th entry takes value 0 if there is no association relationship.
eta0	The bernoulli probability of entries in matrix Z taking a non-zero value given that the corresponding entry in matrix L is zero
eta1	The bernoulli probability of entries in matrix Z taking the value zero given that the corresponding entry in matrix L is one
alpha_tau	The alpha parameter of Gamma distribution used for the simulation of noise, default value=1
beta_tau	The beta parameter of Gamma distribution used for the simulation of noise, default value=0.01
tau_sig	Pre-defined precision of each entry in the factor loadings matrixW, default value=0
max_iter	The number of iterations of the collaped Gibbs sampling algorithm, default=10000
thin	The number of iteration cycle for the record of Gibbs samples. For the convenience of storage, the result of the Gibbs sampling will be kept every other "thin" iterations to alliviate the auto-correlation problem between adjacent interations of the Gibbs sampling process
alpha_sigma	the alpha parameter for the Gamma prior for matrixW
beta_sigma	The beta parameter for the Gamma prior for matrixW
file_name	name of the file saving the result

Value

The algorithm will store the inferred binary indicator matrix Z, loading matrix W and factor activity matrix X (as well as tau_g if not pre-difined) in each thinned iteration and write them into .RData file with name defined by the user.

Author(s)

Haisu Ma<[e-mail]>

Examples

data(matrixY)
data(matrixL)
result<-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.