deconvolute | R Documentation |
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,
...
)
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 |
method |
a string specifying the method.
Supported methods are |
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 |
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 |
data.frame
with cell_type
as first column and a column with the
calculated cell fractions for each sample.
# Not run: deconvolute(gene_expression_matrix, "xcell")
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