mcmc.sampler: Function for fitting sBFAC model via MCMC methods.

In syspremed/PhenMAP: sBFAC - a sparse Bayesian FAC model for clustering expression data, finding associated phenotypes and molecular features.

Function for fitting sBFAC model via MCMC methods.

Description

Function for fitting sBFAC model via MCMC methods.

Usage

mcmc.sampler(facfit, Y, X, w0, g0, mcmc, thinstep)

Arguments

facfit	MAP estimates of sBFAC used to initialize MCMC algorithm.
Y	Expression data in samples by features format
X	Design matrix from model.matrix function which defines how covariates are included into sBFAC.
w0	Controls sparseness in features, minimum value should be 0.01 for non-informative priors.
g0	Decreasing this regularize regression coefficients, i.e., g0=1000 provides non-informative prior == Classical estimates
mcmc	Number of MCMC samples. Default is 20000 but can be increased to improve convergence.
thinstep	Number of iterations to thin to remove autocorrelation between samples.

Details

Function for fitting sBFAC model via MCMC methods.

Value

scores.store	This is an array of qxNxmcmc containing scores for the q latent variables on N obaservations over mcmc samples.
loadings.store	This is an array of pxqxmcmc containing loadings for the p genes/features on q latent variables over mcmc samples.
beta.store	This is an array of qxLxmcmc containing regression coefficients of L covariates on q latent variables over mcmc samples.
sigma.store	This is an matrix of pxmcmc containing residual variablity of each feature.
s2.store	This is an matrix of qxmcmc containing residual variablity of each latent variable.

Author(s)

Gift Nyamundanda, Katie Eason, Pawan Poudel and Anguraj Sadanandam.

References

Nyamundanda et al (2015). A next generation tool enables identification of functional cancer subtypes with associated biological phenotypes.

Examples

## help(mcmc.sampler)

syspremed/PhenMAP documentation built on April 2, 2022, 3:12 p.m.