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inferring cell-type specific gene regulatory network

Source: R/inferCSN.R
inferCSN.Rd

Fits greedy-L0 models for static or pseudotime-ordered expression data.

Usage

inferCSN(
  object,
  pseudotime = NULL,
  regulators = NULL,
  targets = NULL,
  max_support_size = NULL,
  lag_fraction = 0.05,
  lag_steps = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

# S4 method for class 'matrix'
inferCSN(
  object,
  pseudotime = NULL,
  regulators = NULL,
  targets = NULL,
  max_support_size = NULL,
  lag_fraction = 0.05,
  lag_steps = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

# S4 method for class 'sparseMatrix'
inferCSN(
  object,
  pseudotime = NULL,
  regulators = NULL,
  targets = NULL,
  max_support_size = NULL,
  lag_fraction = 0.05,
  lag_steps = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

Arguments

object

Numeric expression matrix with cells in rows and genes in columns.

pseudotime

Optional pseudotime vector or branch matrix.

regulators, targets

Optional gene subsets.

max_support_size

Optional support-size limit.

lag_fraction

Fractional lag used when lag_steps is NULL.

lag_steps

Optional integer lag.

cores

Number of inference workers.

verbose

Whether to report progress.

...

Additional method arguments.

Value

A data frame containing exactly regulator, target, and weight.

Details

Signed ordinal weights group descending deletion evidence against each group's maximum within 1e-12 * (1 + abs(maximum)). This fixed numerical rule leaves support, fitted coefficients and raw deletion evidence unchanged.

Examples

data(example_matrix)
data(example_meta_data)
network_table <- inferCSN(
  example_matrix,
  pseudotime = example_meta_data$pseudotime
)
#> ℹ [2026-09-08 16:58:03] Inferring network for <matrix/array>...
#> ◌ [2026-09-08 16:58:03] Checking parameters...
#> ✔ [2026-09-08 16:58:03] Inferring network done
#> ℹ [2026-09-08 16:58:03] Network information:
#> ℹ                         Edges Regulators Targets
#> ℹ                       1    11          6       6
head(network_table)
#>   regulator target     weight
#> 1        g5     g1 -0.9545455
#> 2        g4     g5  0.8636364
#> 3        g3     g4  0.7727273
#> 4        g1     g2 -0.6818182
#> 5        g1     g6  0.5909091
#> 6        g6     g3  0.5000000

inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g4")
)
#> ℹ [2026-09-08 16:58:03] Inferring network for <matrix/array>...
#> ◌ [2026-09-08 16:58:03] Checking parameters...
#> ✔ [2026-09-08 16:58:03] Inferring network done
#> ℹ [2026-09-08 16:58:03] Network information:
#> ℹ                         Edges Regulators Targets
#> ℹ                       1     4          2       2
#>   regulator target weight
#> 1        g2     g3 -0.875
#> 2        g1     g4 -0.625
#> 3        g2     g4 -0.375
#> 4        g1     g3 -0.125