huge.graph: Learn Huge Graph (With Random Gene Selection + Cells...

In rlebron-bioinfo/gnlearn: Genetic Network Learning

Description

This function allows you to learn a directed graph from a high-dimensional dataset.

Usage

huge.graph(
  df,
  algorithm = boot.pc,
  n.genes = 15,
  R = 200,
  threshold = 0.5,
  iter.R = 4,
  iter.m = NULL,
  to = c("igraph", "adjacency", "edges", "graph", "bnlearn"),
  cluster = parallel::detectCores(),
  seed = sample(1:10^6, 1),
  ...
)

Arguments

df	Dataset.
algorithm	Algorithm to be used (any of the gnlearn 'boot.x' algorithms, such as boot.pc or boot.hc). Default: boot.pc
n.genes	Number of random genes per iteration. Default: 15
R	Number of iterations. Defaults: 200
threshold	Minimum strength required for a coefficient to be included in the average adjacency matrix (optional). Default: 0.5
iter.R	Number of bootstrap replicates. Default: 200
iter.m	Size of training set. Default: nrow(df)/2
to	Output format ('adjacency', 'edges', 'graph', 'igraph', or 'bnlearn').
cluster	A cluster object from package parallel or the number of cores to be used (optional). Default: parallel::detectCores()
seed	Seed used for random selection. Default: NULL
...	Other arguments for the specified algorithm.

Examples

obj <- huge.graph(df, algorithm=boot.tabu)
obj <- huge.graph(df, algorithm=boot.lingam)
obj <- huge.graph(df, algorithm=boot.iamb, n.genes=20, R=100, threshold=0.9, iter.R=10)
avg.g <- obj$average
g.rep <- obj$replicates

rlebron-bioinfo/gnlearn documentation built on July 25, 2020, 12:38 p.m.