ModelGenerator: Generates an ensemble of neural network models.

In SignacX: Cell Type Identification and Discovery from Single Cell Gene Expression Data

Description

ModelGenerator generates an ensemble of neural network models each trained to classify cellular phenotypes using the reference data set.

Usage

ModelGenerator(
  R,
  N = 1,
  num.cores = 1,
  verbose = TRUE,
  hidden = 1,
  set.seed = TRUE,
  seed = "42"
)

Arguments

R	Reference data set returned by GetTrainingData_HPCA
N	Number of neural networks to train. Default is 1.
num.cores	Number of cores to use for parallel computing. Default is 1.
verbose	if TRUE, code will report outputs. Default is TRUE.
hidden	Number of hidden layers in the neural network. Default is 1.
set.seed	If TRUE, seed is set to ensure reproducibility of these results. Default is TRUE.
seed	if set.seed is TRUE, the seed can be set. Default is 42.

Value

A list, each containing N neural network models

See Also

[SignacFast()] for a function that uses the models generated by this function.

Examples

## Not run: 
# download training data set from GitHub
Ref = GetTrainingData_HPCA()

# train a stack of 1,800 neural network models
Models = ModelGenerator(R = Ref, N = 100, num.cores = 4)

# save models
save(Models, file = "models.rda")

## End(Not run)

SignacX documentation built on Nov. 18, 2021, 5:07 p.m.