build_model_scdc: Signature creation with SCDC.

In PelzKo/immunedeconv2: Second generation methods for immune cell deconvolution

Signature creation with SCDC.

Description

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.

Usage

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
)

Arguments

single_cell_object	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.
cell_type_annotations	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.
batch_ids	A vector of the ids of the samples or individuals.
ct_sub	vector. a subset of cell types that are selected to construct basis matrix. NULL means that all are used.
ct_varname	character string specifying the variable name for 'cell types'.
sample	character string specifying the variable name for subject/samples.
ct_cell_size	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.
verbose	Whether to produce an output on the console.

Value

a list with elements:

• basis matrix

• sum of cell-type-specific library size

• sample variance matrix

• basis matrix by mvw

• mvw matrix

PelzKo/immunedeconv2 documentation built on Feb. 12, 2025, 4:16 p.m.