Check input parameters

check_parameters(
  matrix,
  penalty,
  algorithm,
  cross_validation,
  seed,
  n_folds,
  percent_samples,
  r_threshold,
  regulators,
  targets,
  regulators_num,
  verbose,
  cores,
  ...
)

Arguments

matrix: An expression matrix, cells by genes
penalty: The type of regularization. This can take either one of the following choices: L0, L0L1 and L0L2. For high-dimensional and sparse data, such as single-cell sequencing data, L0L2 is more effective.
algorithm: The type of algorithm used to minimize the objective function. Currently CD and CDPSI are supported. The CDPSI algorithm may yield better results, but it also increases running time.
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 sparse_regression. Default set to 1.
r_threshold: Threshold of \(R^2\) or correlation coefficient.
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.
verbose: Logical value. Whether to print detailed information.
cores: Number of CPU cores used. Setting to parallelize the computation with foreach.
...: Parameters for other methods.

Value

Not return value, called for check input parameters