View source: R/sparse_regression.R
single_network | R Documentation |
Construct network for single target gene
single_network(
matrix,
regulators,
target,
cross_validation = FALSE,
seed = 1,
penalty = "L0",
algorithm = "CD",
regulators_num = (ncol(matrix) - 1),
n_folds = 10,
percent_samples = 1,
r_threshold = 0,
verbose = FALSE,
...
)
matrix |
An expression matrix. |
regulators |
The regulator genes for which to infer the regulatory network. |
target |
The target gene. |
cross_validation |
Logical value, default is |
seed |
The random seed for cross-validation, default is |
penalty |
The type of regularization, default is |
algorithm |
The type of algorithm used to minimize the objective function, default is |
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. |
n_folds |
The number of folds for cross-validation, default is |
percent_samples |
The percent of all samples used for |
r_threshold |
Threshold of |
verbose |
Logical value, default is |
... |
Parameters for other methods. |
The weight data table of sub-network
data("example_matrix")
head(
single_network(
example_matrix,
regulators = colnames(example_matrix),
target = "g1"
)
)
single_network(
example_matrix,
regulators = "g1",
target = "g2"
)
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