sample_size_benchmark: training set size benchmark: deconvolute given bulks using...

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

training set size benchmark: deconvolute given bulks using training sets of different sizes to train deconvolution models

Description

training set size benchmark: deconvolute given bulks using training sets of different sizes to train deconvolution models

Usage

sample_size_benchmark(
  training.exprs,
  training.pheno,
  test.exprs,
  test.pheno,
  algorithms,
  bulk.data,
  n.repeats,
  exclude.from.signature = NULL,
  step.size = 0.05,
  verbose = FALSE,
  cell.type.column = "cell_type",
  patient.column = "patient"
)

Arguments

training.exprs	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"
test.exprs	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 and 3) model
bulk.data	list with two entries: 1) bulks - matrix containing expression data of the bulks (one bulk per column) 2) props - matrix containing the true fractions of cell types within the bulks (cell type x bulk)
n.repeats	integer determining the number of times deconvolution should be repeated for each algorithm
exclude.from.signature	character vector containing cell types to be excluded from the signature matrix. If not specified, all will be used.
step.size	numerical 0 < step.size < 1; fraction of samples by which size of training set is increased each step; default 0.05
verbose	logical, default FALSE
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 "patient"

Value

list containing deconvolution results for all algorithms for each training set size

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