bn_tabu_gen: Uses tabu search algorithm to learn the structure of...

In GmicR: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC)

Description

Uses tabu search algorithm to learn the structure of discretized data.

Usage

bn_tabu_gen(
  Auto_WGCNA_OUTPUT,
  whitelist = NULL,
  blacklist = NULL,
  score = "bde",
  tabu = 50,
  iss = 10,
  cluster = NULL,
  debug = TRUE,
  bootstraps_replicates = 500
)

Arguments

Auto_WGCNA_OUTPUT	an R object generated by Auto_WGCNA and discretized using the Data_Prep function.
whitelist	a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs to be included in the graph.
blacklist	a data frame with two columns (optionally labeled "from" and "to"), containing a set of arcs not to be included in the graph.
score	character string indicating the score used for structure learning. If "bde" (default), prior is set to "uniform". If bds is used, the prior is set to "marginal".
tabu	a positive integer number, the length of the tabu list used in the tabu function.
iss	the imaginary sample size, used by the Bayesian Dirichlet scores (bde and bds) It is also known as “equivalent sample size”. The default value is equal to 10.
cluster	an optional cluster object from package parallel.
debug	a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.
bootstraps_replicates	an integer for the number of bootstraps_replicates used for structure learning. Default value is 500

Value

The learned bayesian network

See Also

arc.strength

hc

score

Examples

GMIC_Builder_disc_dir<-system.file("extdata", "GMIC_Builder_disc.Rdata", 
package = "GmicR", mustWork = TRUE)
load(GMIC_Builder_disc_dir)

no_cores<-1
cl<-parallel::makeCluster(no_cores)

GMIC_net<-bn_tabu_gen(GMIC_Builder_disc,
cluster = cl,
bootstraps_replicates = 50, score = "bds")
parallel::stopCluster(cl)

GmicR documentation built on Nov. 8, 2020, 7:07 p.m.