Construct network for single target gene

single_network(
  matrix,
  regulators,
  target,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  algorithm = "CD",
  regulators_num = (ncol(matrix) - 1),
  n_folds = 10,
  subsampling = 1,
  r_threshold = 0,
  verbose = FALSE,
  ...
)

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.
algorithm: The type of algorithm used to minimize the objective function, default is CD. Currently CD and CDPSI are supported. The CDPSI algorithm may yield better results, but it also increases running time.
regulators_num: The number of non-zore coefficients, this value will affect the final performance. The maximum support size at which to terminate the regularization path. Recommend setting this to a small fraction of min(n,p) (e.g. 0.05 * min(n,p)) as L0 regularization typically selects a small portion of non-zeros.
n_folds: The number of folds for cross-validation, default is 10.
subsampling: The percent of all samples used for sparse_regression, default is 1.
r_threshold: Threshold of \(R^2\) or correlation coefficient, default is 0.
verbose: Logical value, default is TRUE, whether to print progress messages.
...: Parameters for other methods.

Value

The weight data table of sub-network

Examples

data("example_matrix")
head(
  single_network(
    example_matrix,
    regulators = colnames(example_matrix),
    target = "g1"
  )
)
#>   regulator target       weight
#> 1       g10     g1  0.005805182
#> 2       g11     g1  0.006012177
#> 3       g12     g1  0.010277177
#> 4       g13     g1 -0.001451393
#> 5       g14     g1 -0.012476081
#> 6       g15     g1  0.016008143

single_network(
  example_matrix,
  regulators = "g1",
  target = "g2"
)
#>   regulator target     weight
#> 1        g1     g2 0.08715829