inferCSN {inferCSN} | R Documentation |
Inferring Cell-Specific Gene Regulatory Network
Description
Inferring Cell-Specific Gene Regulatory Network
Usage
inferCSN(
object,
penalty = "L0",
algorithm = "CD",
cross_validation = FALSE,
seed = 1,
n_folds = 10,
percent_samples = 1,
r_threshold = 0,
regulators = NULL,
targets = NULL,
regulators_num = NULL,
cores = 1,
verbose = FALSE,
...
)
## S4 method for signature 'matrix'
inferCSN(
object,
penalty = "L0",
algorithm = "CD",
cross_validation = FALSE,
seed = 1,
n_folds = 10,
percent_samples = 1,
r_threshold = 0,
regulators = NULL,
targets = NULL,
regulators_num = NULL,
cores = 1,
verbose = FALSE,
...
)
## S4 method for signature 'data.frame'
inferCSN(
object,
penalty = "L0",
algorithm = "CD",
cross_validation = FALSE,
seed = 1,
n_folds = 10,
percent_samples = 1,
r_threshold = 0,
regulators = NULL,
targets = NULL,
regulators_num = NULL,
cores = 1,
verbose = FALSE,
...
)
Arguments
object |
The input data for |
penalty |
The type of regularization.
This can take either one of the following choices: |
algorithm |
The type of algorithm used to minimize the objective function.
Currently |
cross_validation |
Check whether cross validation is used. |
seed |
The seed used in randomly shuffling the data for cross-validation. |
n_folds |
The number of folds for cross-validation. |
percent_samples |
The percent of all samples used for |
r_threshold |
Threshold of |
regulators |
A character vector with the regulators to consider for CSN inference. |
targets |
A character vector with the targets to consider for CSN inference. |
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 |
Number of CPU cores used. Setting to parallelize the computation with |
verbose |
Print detailed information. |
... |
Parameters for other methods. |
Value
A data table of gene-gene regulatory relationship
Examples
data("example_matrix")
network_table <- inferCSN(example_matrix, verbose = TRUE)
head(network_table)
network_table <- inferCSN(example_matrix, cores = 2)
head(network_table)