run_deconrnaseq: deconvolute given bulks with DeconRNASeq using single cell...

View source: R/wrapper_deconRNASeq.R

run_deconrnaseqR Documentation

deconvolute given bulks with DeconRNASeq using single cell data

Description

deconvolute given bulks with DeconRNASeq using single cell data

Usage

run_deconrnaseq(
  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

model for DeconRNASeq 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

Value

list with two 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)


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