inferring Cell-Specific gene regulatory Network

Source: R/inferCSN.R

inferring Cell-Specific gene regulatory Network

inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  subsampling = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  regulators_num = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

# S4 method for class 'matrix'
inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  subsampling = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  regulators_num = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

# S4 method for class 'sparseMatrix'
inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  subsampling = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  regulators_num = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

# S4 method for class 'data.frame'
inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  subsampling = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  regulators_num = 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.

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.

cross_validation

Logical value, default is FALSE, whether to use cross-validation.

seed

The random seed for cross-validation, default is 1.

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.

regulators

The regulator genes for which to infer the regulatory network.

targets

The target genes for which to infer the regulatory network.

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.

cores

The number of cores to use for parallelization with foreach, default is 1.

verbose

Logical value, default is TRUE, whether to print progress messages.

...

Parameters for other methods.

Value

A data table of regulator-target regulatory relationships

Examples

data("example_matrix")
network_table_1 <- inferCSN(
  example_matrix
)
#> ✔ Running for <dense matrix>.
#> ✔ Checking input parameters.
#> ✔ Using L0 sparse regression model.
#> ✔ Using 1 core.
#> ✔ Run done.
head(network_table_1)
#>   regulator target     weight
#> 1       g17    g18  0.5443805
#> 2       g18     g1 -0.5390453
#> 3       g16    g15  0.4818454
#> 4       g17    g16  0.4599054
#> 5       g15    g14  0.4516497
#> 6       g14    g13  0.4483912

network_table_2 <- inferCSN(
  example_matrix,
  cores = 2
)
#> ✔ Running for <dense matrix>.
#> ✔ Checking input parameters.
#> ✔ Using L0 sparse regression model.
#> ✔ Using 2 cores.
#> ✔ Run done.

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

inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g4")
)
#> ✔ Running for <dense matrix>.
#> ✔ Checking input parameters.
#> ✔ Using 2 regulator(s).
#> ✔ Using 2 target(s).
#> ✔ Using L0 sparse regression model.
#> ✔ Using 1 core.
#> ✔ Run done.
#>   regulator target     weight
#> 1        g2     g3  0.8504059
#> 2        g2     g4  0.7058242
#> 3        g1     g4 -0.2941758
#> 4        g1     g3 -0.1495941
inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g0")
)
#> ✔ Running for <dense matrix>.
#> ✔ Checking input parameters.
#> ✔ Using 2 regulator(s).
#> ! Warning: 1 out of 2 candidate targets are in the input matrix.
#> ✔ Using L0 sparse regression model.
#> ✔ Using 1 core.
#> ✔ Run done.
#>   regulator target     weight
#> 1        g2     g3  0.8504059
#> 2        g1     g3 -0.1495941
inferCSN(
  example_matrix,
  regulators = c("g1", "g0"),
  targets = c("g2", "g3")
)
#> ✔ Running for <dense matrix>.
#> ✔ Checking input parameters.
#> ! Warning: 1 out of 2 candidate regulators are in the input matrix.
#> ✔ Using 2 target(s).
#> ✔ Using L0 sparse regression model.
#> ✔ Using 1 core.
#> ✔ Run done.
#>   regulator target     weight
#> 1        g1     g2 0.08715829
#> 2        g1     g3 0.02584892
inferCSN(
  example_matrix,
  regulators = c("g1"),
  targets = c("g2")
)
#> ✔ Running for <dense matrix>.
#> ✔ Checking input parameters.
#> ✔ Using 1 regulator(s).
#> ✔ Using 1 target(s).
#> ✔ Using L0 sparse regression model.
#> ✔ Using 1 core.
#> ✔ Run done.
#>   regulator target     weight
#> 1        g1     g2 0.08715829
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
)
} # }