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inferring cell-type specific gene regulatory network

Source: R/inferCSN.R
inferCSN.Rd

inferring cell-type specific gene regulatory network

Usage

inferCSN(
  object,
  penalty = "L0",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 5,
  subsampling_method = c("sample", "meta_cells", "pseudobulk"),
  subsampling_ratio = 1,
  r_squared_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

# S4 method for class 'matrix'
inferCSN(
  object,
  penalty = "L0",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 5,
  subsampling_method = c("sample", "meta_cells", "pseudobulk"),
  subsampling_ratio = 1,
  r_squared_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

# S4 method for class 'sparseMatrix'
inferCSN(
  object,
  penalty = "L0",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 5,
  subsampling_method = c("sample", "meta_cells", "pseudobulk"),
  subsampling_ratio = 1,
  r_squared_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

# S4 method for class 'data.frame'
inferCSN(
  object,
  penalty = "L0",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 5,
  subsampling_method = c("sample", "meta_cells", "pseudobulk"),
  subsampling_ratio = 1,
  r_squared_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

Arguments

object

The input data for inferCSN.

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.

cross_validation

Whether to use cross-validation. Default is FALSE.

seed

The random seed for cross-validation. Default is 1.

n_folds

The number of folds for cross-validation. Default is 5.

subsampling_method

The method to use for subsampling. Options are "sample", "pseudobulk" or "meta_cells".

subsampling_ratio

The percent of all samples used for fit_srm. Default is 1.

r_squared_threshold

Threshold of \(R^2\) coefficient. Default is 0.

regulators

The regulator genes for which to infer the regulatory network.

targets

The target genes for which to infer the regulatory network.

cores

The number of cores to use for parallelization with foreach::foreach. Default is 1.

verbose

Whether to print progress messages. Default is TRUE.

...

Parameters for other methods.

Value

A data table of regulator-target regulatory relationships. The data table has the three columns: regulator, target, and weight.

Examples

data(example_matrix)
network_table_1 <- inferCSN(
  example_matrix
)
#> ℹ [2025-10-30 09:49:03] Running for <dense matrix>.
#> ◌ [2025-10-30 09:49:03] Checking input parameters...
#> ℹ [2025-10-30 09:49:03] Using `L0` sparse regression model
#> ℹ [2025-10-30 09:49:03] Using 1 core
#> ℹ [2025-10-30 09:49:03] Building results
#> ✔ [2025-10-30 09:49:04] Run done.

network_table_2 <- inferCSN(
  example_matrix,
  cores = 2
)
#> ℹ [2025-10-30 09:49:04] Running for <dense matrix>.
#> ◌ [2025-10-30 09:49:04] Checking input parameters...
#> ℹ [2025-10-30 09:49:04] Using `L0` sparse regression model
#> ℹ [2025-10-30 09:49:04] Using 2 cores
#> ℹ [2025-10-30 09:49:04] Building results
#> ✔ [2025-10-30 09:49:04] Run done.

head(network_table_1)
#>   regulator target     weight
#> 1       g18     g1 -0.9223177
#> 2       g17    g18  0.8770468
#> 3        g4     g3  0.8103230
#> 4       g16    g15  0.7659245
#> 5       g17    g16  0.7558764
#> 6       g12    g11  0.7444053

identical(
  network_table_1,
  network_table_2
)
#> [1] TRUE

inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g4")
)
#> ℹ [2025-10-30 09:49:04] Running for <dense matrix>.
#> ◌ [2025-10-30 09:49:04] Checking input parameters...
#> ℹ [2025-10-30 09:49:04] Using 2 regulators
#> ℹ [2025-10-30 09:49:04] Using 2 targets
#> ℹ [2025-10-30 09:49:04] Using `L0` sparse regression model
#> ℹ [2025-10-30 09:49:04] Using 1 core
#> ℹ [2025-10-30 09:49:04] Building results
#> ✔ [2025-10-30 09:49:04] Run done.
#>   regulator target     weight
#> 1        g2     g3  0.9848781
#> 2        g2     g4  0.9230387
#> 3        g1     g4 -0.3847071
#> 4        g1     g3 -0.1732490
inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g0")
)
#> ℹ [2025-10-30 09:49:04] Running for <dense matrix>.
#> ◌ [2025-10-30 09:49:04] Checking input parameters...
#> ℹ [2025-10-30 09:49:04] Using 2 regulators
#> ! [2025-10-30 09:49:04] 1 out of 2 candidate targets are in the input matrix
#> ℹ [2025-10-30 09:49:04] Using `L0` sparse regression model
#> ℹ [2025-10-30 09:49:04] Using 1 core
#> ℹ [2025-10-30 09:49:04] Building results
#> ✔ [2025-10-30 09:49:04] Run done.
#>   regulator target     weight
#> 1        g2     g3  0.9848781
#> 2        g1     g3 -0.1732490

if (FALSE) { # \dontrun{
data("example_ground_truth")
network_table_07 <- inferCSN(
  example_matrix,
  r_squared_threshold = 0.7
)
calculate_metrics(
  network_table_1,
  example_ground_truth,
  return_plot = TRUE
)
calculate_metrics(
  network_table_07,
  example_ground_truth,
  return_plot = TRUE
)
} # }
if (FALSE) { # \dontrun{
data(example_matrix)
network_table <- inferCSN(example_matrix)
head(network_table)

network_table_sparse_1 <- inferCSN(
  as(example_matrix, "sparseMatrix")
)
head(network_table_sparse_1)

network_table_sparse_2 <- inferCSN(
  as(example_matrix, "sparseMatrix"),
  cores = 2
)
identical(
  network_table,
  network_table_sparse_1
)

identical(
  network_table_sparse_1,
  network_table_sparse_2
)

plot_scatter(
  data.frame(
    network_table$weight,
    network_table_sparse_1$weight
  ),
  legend_position = "none"
)

plot_weight_distribution(
  network_table
) + plot_weight_distribution(
  network_table_sparse_1
)
} # }