init.net.mcmc: Random initial network

In anna-pacinkova/intomics_package: Integrative analysis of multi-omics data to infer regulatory networks

Random initial network

Description

init.net.mcmc This function is used to sample random initial network. The edges are sampled only between GE nodes.

Usage

init.net.mcmc(omics, layers_def, B_prior_mat)

Arguments

omics	named list containing the gene expression (possibly copy number variation and methylation data). Each component of the list is a matrix with samples in rows and features in columns.
layers_def	data.frame containing the modality ID, corresponding layer in BN and maximal number of parents from given layer to GE nodes.
B_prior_mat	a biological prior matrix.

Value

List of 2 elements: random adjacency network and empty network

Examples

data(list=c("PK", "TFtarg_mat", "annot", "layers_def", "omics"),
package="IntOMICS")
B <- B_prior_mat(omics = omics, PK = PK, annot = annot, lm_METH = TRUE,
     layers_def = layers_def, r_squared_thres = 0.3, p_val_thres = 0.05,
     TFtargs = TFtarg_mat, TFBS_belief = 0.75, nonGE_belief = 0.5, 
     woPKGE_belief = 0.5)
init.net.mcmc(omics = B$omics, layers_def = layers_def, 
     B_prior_mat = B$B_prior_mat)

anna-pacinkova/intomics_package documentation built on Aug. 13, 2022, 11:38 a.m.