Construct network for single target gene

Usage

single_network(
  matrix,
  regulators,
  target,
  pseudotime = NULL,
  max_support_size = NULL,
  lag_fraction = 0.05,
  lag_steps = NULL,
  cores = 1,
  verbose = TRUE,
  method = c("greedy_l0", "L0", "L0L1", "L0L2"),
  ...
)

Arguments

matrix: An expression matrix.
regulators: Candidate regulator genes.
target: The target gene.
pseudotime: Optional pseudotime vector or branch matrix passed to [inferCSN()].
max_support_size: Optional support-size cap passed to [inferCSN()].
lag_fraction: Fractional state lag passed to [inferCSN()].
lag_steps: Optional integer state lag passed to [inferCSN()].
cores: Number of inference workers.
verbose: Whether to report progress.
method: greedy_l0 (default), or L0Learn with the L0, L0L1, or L0L2 penalty.
...: Arguments passed to the method.

Value

A data frame with regulator, target, and weight columns. Greedy-L0 returns selected edges. L0Learn retains the original per-regulator coefficients, including zeros; [inferCSN()] removes zero weights from the complete network.

Examples

data(example_matrix)
head(
  single_network(
    example_matrix,
    regulators = colnames(example_matrix),
    target = "g1"
  )
)
#> ℹ [2026-10-07 03:30:43] Inferring network for <matrix/array>...
#> ✔ [2026-10-07 03:30:43] Inferring network done
#> ℹ [2026-10-07 03:30:43] Network information:
#> ℹ                         Edges Regulators Targets
#> ℹ                       1     2          2       1
#>   regulator target weight
#> 1        g6     g1   0.75
#> 2        g5     g1  -0.25
single_network(
  example_matrix,
  regulators = c("g1", "g2", "g3"),
  target = "g1"
)
#> ℹ [2026-10-07 03:30:43] Inferring network for <matrix/array>...
#> ✔ [2026-10-07 03:30:43] Inferring network done
#> ℹ [2026-10-07 03:30:43] Network information:
#> ℹ                         Edges Regulators Targets
#> ℹ                       1     2          2       1
#>   regulator target weight
#> 1        g2     g1  -0.75
#> 2        g3     g1  -0.25