Switch matrix to network table

matrix_to_table(
  network_matrix,
  regulators = NULL,
  targets = NULL,
  threshold = 0
)

Arguments

network_matrix: The matrix of network weight.
regulators: Character vector of regulator names to filter by.
targets: Character vector of target names to filter by.
threshold: The threshold for filtering weights based on absolute values, defaults to 0.

Value

Network table

Examples

data("example_matrix")
network_table <- inferCSN(example_matrix)
#> ℹ [2025-04-22 07:40:11] Running for <dense matrix>.
#> ℹ [2025-04-22 07:40:11] Checking input parameters.
#> ℹ [2025-04-22 07:40:11] Using L0 sparse regression model.
#> ℹ [2025-04-22 07:40:11] Using 1 core
#> ✔ [2025-04-22 07:40:11] Run done.
network_matrix <- table_to_matrix(network_table)
network_table_new <- matrix_to_table(network_matrix)
head(network_table)
#>   regulator target     weight
#> 1       g18     g1 -0.9223177
#> 2       g17    g18  0.8770468
#> 3        g4     g3  0.8103065
#> 4       g16    g15  0.7659245
#> 5       g17    g16  0.7558764
#> 6       g12    g11  0.7444053
head(network_table_new)
#>   regulator target     weight
#> 1       g18     g1 -0.9223177
#> 2       g17    g18  0.8770468
#> 3        g4     g3  0.8103065
#> 4       g16    g15  0.7659245
#> 5       g17    g16  0.7558764
#> 6       g12    g11  0.7444053
identical(
  network_table,
  network_table_new
)
#> [1] TRUE

matrix_to_table(
  network_matrix,
  threshold = 0.8
)
#>   regulator target     weight
#> 1       g18     g1 -0.9223177
#> 2       g17    g18  0.8770468
#> 3        g4     g3  0.8103065

matrix_to_table(
  network_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g4")
)
#>   regulator target      weight
#> 1        g2     g3  0.54924316
#> 2        g1     g4 -0.03658957
#> 3        g2     g4  0.02103272
#> 4        g1     g3  0.01808795