runiClusterBayes: Run the iClusterBayes method

In GaoLabXDU/CEPICS: CEPICS: a comparison and evaluation platform for integration methods in cancer subtyping

Description

Run the iClusterBayes method

Usage

runiClusterBayes(dl, type = c("gaussian", "gaussian", "gaussian",
  "gaussian", "gaussian", "gaussian"), kMax = 4, n.burnin = 1000,
  n.draw = 1200, prior.gamma = rep(0.1, 6), sdev = 0.5,
  beta.var.scale = 1, thin = 1, pp.cutoff = 0.5, cores = 1)

Arguments

dl	The datalist needs to be processed.
type	Data type corresponding to dl1-6, which can be gaussian, binomial, or poisson.
kMax	The maximum number of clusters.
n.burnin	Please see "iClusterPlus" package for more details.
n.draw	Please see "iClusterPlus" package for more details.
prior.gamma	Please see "iClusterPlus" package for more details.
sdev	Please see "iClusterPlus" package for more details.
beta.var.scale	Please see "iClusterPlus" package for more details.
thin	Please see "iClusterPlus" package for more details.
pp.cutoff	Please see "iClusterPlus" package for more details.
cores	An integer value means the number of cores for parallel computing.

Value

2 to kMax clustering results and sample similarity matrix.

References

Mo,Q. et al. (2017) A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics, 19, 71-86.

Examples

data(COAD_Methy, COAD_miRNA, COAD_mRNA)
datalist <- list(COAD_Methy, COAD_miRNA, COAD_mRNA)
res <- runiClusterBayes(datalist, type = c("gaussian","gaussian","gaussian"),kMax=5,n.burnin=1000,
     n.draw=1200,prior.gamma=rep(0.1,6),sdev=0.5,beta.var.scale=1,thin=1,pp.cutoff=0.5)

GaoLabXDU/CEPICS documentation built on June 9, 2020, 2:31 a.m.