single_network: Construct network for single target gene

In inferCSN: Inferring Cell-Specific Gene Regulatory Network

Construct network for single target gene

Description

Construct network for single target gene

Usage

single_network(
  matrix,
  regulators,
  target,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  r_squared_threshold = 0,
  n_folds = 5,
  verbose = TRUE,
  ...
)

Arguments

matrix	An expression matrix.
regulators	The regulator genes for which to infer the regulatory network.
target	The target gene.
cross_validation	Logical value, default is FALSE, whether to use cross-validation.
seed	The random seed for cross-validation, default is 1.
penalty	The type of regularization, default is L0. This can take either one of the following choices: L0, L0L1, and L0L2. For high-dimensional and sparse data, L0L2 is more effective.
r_squared_threshold	Threshold of R^2 coefficient, default is 0.
n_folds	The number of folds for cross-validation, default is 5.
verbose	Logical value, default is TRUE, whether to print progress messages.
...	Parameters for other methods.

Value

A data frame of the single target gene network. The data frame has three columns:

regulator	The regulator genes.
target	The target gene.
weight	The weight of the regulator gene on the target gene.

Examples

data("example_matrix")
head(
  single_network(
    example_matrix,
    regulators = colnames(example_matrix),
    target = "g1"
  )
)
head(
  single_network(
    example_matrix,
    regulators = colnames(example_matrix),
    target = "g1",
    cross_validation = TRUE
  )
)

single_network(
  example_matrix,
  regulators = c("g1", "g2", "g3"),
  target = "g1"
)
single_network(
  example_matrix,
  regulators = c("g1", "g2"),
  target = "g1"
)

inferCSN documentation built on April 13, 2025, 5:11 p.m.