sampling_phase: Sampling phase

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

Sampling phase

Description

sampling_phase Now we apply 2 MCMC simulations and check the RMS value. After the burn-in period, we discard the values from the first half of this phase.

Usage

sampling_phase(
  second.adapt.phase_net,
  omics,
  layers_def,
  prob_mbr,
  thin,
  minseglen,
  burn_in,
  annot
)

Arguments

second.adapt.phase_net	list output of the second.adapt.phase function.
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.
prob_mbr	numeric vector probability of the MBR step.
thin	numeric vector thinning frequency of the resulting MCMC simulation.
minseglen	numeric vector minimal number of iterations with the c_rms value below the c_rms threshold.
burn_in	numeric vector the minimal length of burn-in period of the MCMC simulation.
annot	named list containing the associated methylation probes of given gene.

Value

List of 2 elements: sampling phase result; RMS used to evaluate MCMC convergence

Examples

data(list=c("PK", "TFtarg_mat", "annot", "layers_def", "omics"),
    package="IntOMICS")
OMICS_mod_res <- OMICS_module(omics = omics, PK = PK, annot = annot, 
    layers_def = layers_def, TFtargs = TFtarg_mat, r_squared_thres = 0.3, 
    lm_METH = TRUE)
first.adapt.phase_net <- first_adapt_phase(omics = OMICS_mod_res$omics, 
    B_prior_mat = OMICS_mod_res$B_prior_mat, prob_mbr = 0.07, len = 5,  
    energy_all_configs_node = OMICS_mod_res$pf_UB_BGe_pre$energy_all_configs_node,
    layers_def = OMICS_mod_res$layers_def, annot = OMICS_mod_res$annot,
    BGe_score_all_configs_node = OMICS_mod_res$pf_UB_BGe_pre$BGe_score_all_configs_node, 
    parent_set_combinations =
    OMICS_mod_res$pf_UB_BGe_pre$parents_set_combinations)
transient.phase_net <- transient_phase(prob_mbr = 0.07, 
    first.adapt.phase_net = first.adapt.phase_net, 
    omics = OMICS_mod_res$omics, B_prior_mat = OMICS_mod_res$B_prior_mat, 
    layers_def = OMICS_mod_res$layers_def, annot = OMICS_mod_res$annot,
    energy_all_configs_node = OMICS_mod_res$pf_UB_BGe_pre$energy_all_configs_node,
    BGe_score_all_configs_node = OMICS_mod_res$pf_UB_BGe_pre$BGe_score_all_configs_node, 
    parent_set_combinations = OMICS_mod_res$pf_UB_BGe_pre$parents_set_combinations) 
second.adapt.phase_net <- second_adapt_phase(prob_mbr = 0.07, 
    transient.phase_net = transient.phase_net, woPKGE_belief = 0.5, 
    omics = OMICS_mod_res$omics, B_prior_mat = OMICS_mod_res$B_prior_mat, 
    layers_def = OMICS_mod_res$layers_def, annot = OMICS_mod_res$annot,
    energy_all_configs_node =
    OMICS_mod_res$pf_UB_BGe_pre$energy_all_configs_node,
    BGe_score_all_configs_node = OMICS_mod_res$pf_UB_BGe_pre$BGe_score_all_configs_node, 
    parent_set_combinations = OMICS_mod_res$pf_UB_BGe_pre$parents_set_combinations) 
sampling_phase(second.adapt.phase_net = second.adapt.phase_net, 
    omics = OMICS_mod_res$omics, layers_def = OMICS_mod_res$layers_def, 
    prob_mbr = 0.07, thin = 500, minseglen = 50000,
    burn_in = 100000, annot = OMICS_mod_res$annot)

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