build_model_scaden: Builds Scaden Model from scRNA data
In PelzKo/immunedeconv2: Second generation methods for immune cell deconvolution

build_model_scaden

R Documentation

Builds Scaden Model from scRNA data

Description

The model is saved in a defined directory.

Usage

build_model_scaden(
  single_cell_object,
  cell_type_annotations,
  bulk_gene_expression,
  model_path = NULL,
  temp_dir = NULL,
  batch_size = 128,
  learning_rate = 1e-04,
  steps = 5000,
  var_cutoff = NULL,
  cells = 100,
  samples = 1000,
  dataset_name = "scaden",
  verbose = FALSE
)

Arguments

`single_cell_object`	A matrix with the single-cell data. Rows are genes, columns are samples. Row and column names need to be set.
`cell_type_annotations`	A vector of the cell type annotations. Has to be in the same order as the samples in single_cell_object.
`bulk_gene_expression`	A matrix of bulk data. Rows are genes, columns are samples. Row and column names need to be set.
`model_path`	Path where model directory should be created (optional).
`temp_dir`	The temporary directory to use for the computations (optional)
`batch_size`	Training of model: Batch size to use for training (default: 128).
`learning_rate`	Training of model: Learning rate used for training (default: 1E-4).
`steps`	Training of model: Number of training steps (default: 5000).
`var_cutoff`	Training data processing: Filter out genes with a variance less than the specified cutoff. A low cutoff is recommended,this should only remove genes that are obviously uninformative. (default NULL).
`cells`	Bulk simulation: Number of cells per sample (default: 100).
`samples`	Bulk simulation: Number of samples to simulate (default: 1000).
`dataset_name`	Bulk simulation: Name of simulated dataset (default scaden).
`verbose`	Whether to produce an output on the console (default: false).