run_dtd | R Documentation |
deconvolute given bulk with DTD using single-cell data
run_dtd( exprs, pheno, bulks, cell.type.column = "cell_type", exclude.from.signature = NULL, max.genes = 1000, verbose = FALSE, patient.column = NULL, scale.cpm = FALSE, model = NULL, model_exclude = NULL )
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 |
cell.type.column |
string, which column of 'pheno' holds the cell type information? default "cell_type" |
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 1000 |
verbose |
boolean, default FALSE |
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 DTD 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 |
list with four 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 - the trained DTD model
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