immunedeconv (version 1.1.1)

deconvolute: Perform an immune cell deconvolution on a dataset.

Description

Perform an immune cell deconvolution on a dataset.

Usage

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 releveant 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 anlysis 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 {
# Not run: deconvolute(gene_expression_matrix, "xcell")

# }