deconvolute: deconvolute given bulks with all supplied algorithms and...

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

deconvolute given bulks with all supplied algorithms and training data

Description

deconvolute given bulks with all supplied algorithms and training data

Usage

deconvolute(
  training.expr,
  training.pheno,
  test.expr,
  test.pheno,
  algorithms,
  verbose = FALSE,
  exclude.from.bulks = NULL,
  exclude.from.signature = NULL,
  max.genes = 500,
  n.bulks = 500,
  bulks = NULL,
  n.repeats = 1,
  subtypes = FALSE,
  cell.type.column = "cell_type",
  patient.column = "patient",
  n.profiles.per.bulk = 1000
)

Arguments

training.expr	matrix containing single-cell expression profiles (training set, one cell per column)
training.pheno	data frame containing phenotype data of the single-cell training set. Has to contain column 'cell.type.column'
test.expr	matrix containing single-cell expression profiles (test set, one cell per column)
test.pheno	data frame containing phenotype data of the single-cell test set. Has to contain column 'cell.type.column'
algorithms	List containing a list for each algorithm. Each sublist contains 1) name 2) function 3) model
verbose	logical, default FALSE
exclude.from.bulks	character vector containing cell types to be excluded from the bulks (if they are not supplied). If not specified, all will be used.
exclude.from.signature	character vector containing cell types to be excluded from the signature matrix. If not specified, all will be used.
max.genes	maximum number of genes that will be included in the signature for each celltype
n.bulks	number of bulks to build if they are not supplied to the function, default 500
bulks	matrix containing expression profiles of bulks in the columns. If not supplied, bulks will be created
n.repeats	integer determining the number of times deconvolution should be repeated for each algorithm, default 1
subtypes	boolean, are simulated subtypes used for deconvolution?
cell.type.column	string, which column of 'training.pheno'/'test.pheno' holds the cell type information? default 'cell_type'
patient.column	string, which column of 'pheno' holds the patient information; optional, default 'patient'
n.profiles.per.bulk	positive numeric, number of samples to be randomly drawn for each simulated bulk; default 1000; only needed when bulks=NULL

Value

list with two entries: 1) results.list: list containing deconvolution results for all algorithms and repetitions as returned by the algorithm functions 2) bulk.props: matrix containing the real proportions / quantities for all cell types in all bulks (cell type x bulk)

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