inferCSN: Inferring Cell-Specific Gene Regulatory Network

In inferCSN: Inferring Cell-Specific Gene Regulatory Network

Inferring Cell-Specific Gene Regulatory Network

Description

Inferring Cell-Specific Gene Regulatory Network

Usage

inferCSN(
  matrix,
  penalty = "L0",
  algorithm = "CD",
  crossValidation = FALSE,
  seed = 1,
  nFolds = 10,
  kFolds = NULL,
  rThreshold = 0,
  regulators = NULL,
  targets = NULL,
  maxSuppSize = NULL,
  verbose = FALSE,
  cores = 1
)

## S4 method for signature 'data.frame'
inferCSN(
  matrix,
  penalty = "L0",
  algorithm = "CD",
  crossValidation = FALSE,
  seed = 1,
  nFolds = 10,
  kFolds = NULL,
  rThreshold = 0,
  regulators = NULL,
  targets = NULL,
  maxSuppSize = NULL,
  verbose = FALSE,
  cores = 1
)

## S4 method for signature 'matrix'
inferCSN(
  matrix,
  penalty = "L0",
  algorithm = "CD",
  crossValidation = FALSE,
  seed = 1,
  nFolds = 10,
  kFolds = NULL,
  rThreshold = 0,
  regulators = NULL,
  targets = NULL,
  maxSuppSize = NULL,
  verbose = FALSE,
  cores = 1
)

Arguments

matrix	An expression matrix, cells by genes
penalty	The type of regularization. This can take either one of the following choices: "L0" 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.
crossValidation	Check whether cross validation is used.
seed	The seed used in randomly shuffling the data for cross-validation.
nFolds	The number of folds for cross-validation.
kFolds	The number of folds for sample split.
rThreshold	rThreshold.
regulators	Regulator genes.
targets	Target genes.
maxSuppSize	The number of non-zore coef, 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	Print detailed information.
cores	CPU cores.

Value

A data table of gene-gene regulatory relationship

Examples

library(inferCSN)
data("exampleMatrix")
weightDT <- inferCSN(exampleMatrix, verbose = TRUE)
head(weightDT)

weightDT <- inferCSN(exampleMatrix, verbose = TRUE, cores = 2)
head(weightDT)

inferCSN documentation built on Nov. 2, 2023, 6:27 p.m.