BN_module: #' BN module

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

#' BN module

Description

BN_module Performs automatically tuned MCMC sampling from posterior distribution together with conventional MCMC sampling using empirical biological prior matrix to sample network structures from posterior distribution.

Usage

BN_module(burn_in, thin, OMICS_mod_res, minseglen, len = 5, prob_mbr = 0.07)

Arguments

burn_in	numeric vector the minimal length of burn-in period of the MCMC simulation.
thin	numeric vector thinning frequency of the resulting MCMC simulation.
OMICS_mod_res	list output from the OMICS_module function.
minseglen	numeric vector minimal number of iterations with the c_rms value below the c_rms threshold.
len	numeric vector initial width of the sampling interval for hyperparameter beta.
prob_mbr	numeric vector probability of the MBR step.

Value

Large List of 3 elements: empirical biological matrix, sampling phase result and hyperparameter beta tuning trace

Examples

data(list=c("PK", "TFtarg_mat", "annot", "gene_annot", "layers_def", 
    "omics"), package="IntOMICS")
OMICS_mod_res <- OMICS_module(omics = omics, PK = PK, 
    layers_def = layers_def, TFtargs = TFtarg_mat,
    annot = annot, r_squared_thres = 0.3, 
    lm_METH = TRUE)
BN_mod_res <- BN_module(burn_in = 100000, thin = 500, 
    OMICS_mod_res = OMICS_mod_res, minseglen = 50000, 
    len = 5, prob_mbr = 0.07)

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