Perform an immune cell deconvolution on a dataset.

deconvolute(
  gene_expression,
  method = deconvolution_methods,
  indications = NULL,
  tumor = TRUE,
  arrays = FALSE,
  column = "gene_symbol",
  rmgenes = NULL,
  scale_mrna = TRUE,
  expected_cell_types = NULL,
  ...
)

Arguments

gene_expression: A gene expression matrix or a Biobase ExpressionSet. Either: A numeric matrix or data.frame with HGNC gene symbols as rownames and sample identifiers as colnames. Or: A Biobase ExpressionSet with HGNC symbols in an fData column (see column parameter) In both cases, data must be on non-log scale.
method: a string specifying the method. Supported methods are xcell, ...
indications: a character vector with one indication per sample for TIMER. Argument is ignored for all other methods.
tumor: use a signature matrix/procedure optimized for tumor samples, if supported by the method. Currently affects EPIC and quanTIseq.
arrays: Runs methods in a mode optimized for microarray data. Currently affects quanTIseq and CIBERSORT.
column: Only relevant if gene_expression is an ExpressionSet. Defines in which column of fData the HGNC symbol can be found.
rmgenes: a character vector of gene symbols. Exclude these genes from the analysis. Use this to exclude e.g. noisy genes.
scale_mrna: logical. If FALSE, disable correction for mRNA content of different cell types. This is supported by methods that compute an absolute score (EPIC and quanTIseq)
expected_cell_types: Limit the analysis to the cell types given in this list. If the cell types present in the sample are known a priori, setting this can improve results for xCell (see https://github.com/grst/immunedeconv/issues/1).
...: arguments passed to the respective method

Value

data.frame with cell_type as first column and a column with the calculated cell fractions for each sample.

Examples

# Not run: deconvolute(gene_expression_matrix, "xcell")