SCDC does the signature creation in one step, not separated into build_model and deconvolute. Please use the deconvolute method with your single cell and bulk RNA seq data to use SCDC.
build_model_scdc(
single_cell_object,
cell_type_annotations,
batch_ids,
ct_sub = NULL,
ct_varname = "cellType",
sample = "batchId",
ct_cell_size = NULL,
verbose = FALSE
)A matrix or dataframe with the single-cell data. Rows are genes, columns are samples. Row and column names need to be set. This can also be a list of objects, if SCDC_ENSEMBLE should be used.
A Vector of the cell type annotations. Has to be in the same order as the samples in single_cell_object. This can also be a list of vectors, if SCDC_ENSEMBLE should be used.
A vector of the ids of the samples or individuals.
vector. a subset of cell types that are selected to construct basis matrix. NULL means that all are used.
character string specifying the variable name for 'cell types'.
character string specifying the variable name for subject/samples.
default is NULL, which means the "library size" is calculated based on the data. Users can specify a vector of cell size factors corresponding to the ct.sub according to prior knowledge. The vector should be named: names(ct_cell_size input) should not be NULL.
Whether to produce an output on the console.
a list with elements:
basis matrix
sum of cell-type-specific library size
sample variance matrix
basis matrix by mvw
mvw matrix