run_least_squares: deconvolute given bulk with DTD using single-cell data...

In MarianSchoen/DMC: Comparison of different Deconvolution Models in several scenarios

deconvolute given bulk with DTD using single-cell data without loss function learning

Description

deconvolute given bulk with DTD using single-cell data without loss function learning

Usage

run_least_squares(
  exprs,
  pheno,
  bulks,
  exclude.from.signature = NULL,
  max.genes = 500,
  cell.type.column = "cell_type",
  patient.column = NULL,
  scale.cpm = FALSE,
  model = NULL,
  model_exclude = NULL
)

Arguments

exprs	non negative numeric matrix containing single cell profiles as columns and features as rows
pheno	data.frame, with 'nrow(pheno)' must equal 'ncol(exprs)'. Has to contain single cell labels in a column named 'cell_type'
bulks	matrix containing bulk expression profiles as columns
exclude.from.signature	vector of strings of cell types not to be included in the signature matrix
max.genes	numeric, maximum number of genes that will be included in the signature for each celltype, default 500
cell.type.column	string, which column of 'pheno' holds the cell type information? default "cell_type"
patient.column	string, which column of 'pheno' holds the patient information; optional, default NULL
scale.cpm	boolean, scale single-cell profiles to CPM? default FALSE
model	pre-trained model for LSQ deconvolution as returned by this wrapper, default NULL
model_exclude	character vector, cell type(s) to exclude from the supplied pre-trained model, default NULL
verbose	boolean, default FALSE

Value

list with three entries: 1) est.props - matrix containing for each bulk the estimated fractions of the cell types contained
2) sig.matrix - effective signature matrix used by the algorithm (features x cell types)
3) model - list containing reference.X (signature matrix) and g (weight vector, all weights 1)

MarianSchoen/DMC documentation built on Aug. 2, 2022, 3:05 p.m.