Cell-type deconvolution of tumor bulk RNA-seq with one function

Runs one published deconvolution or cell-abundance method on a gene-by-sample table. The input layout is the one rsemverse::rsem_read() returns: the first column holds gene symbols and every other column is one sample. The method runs in a separate R process. External methods use the original package at the version recorded in the registry (see bulkdeconv_install()).

Usage

bulkdeconv(data, expr, species, method, ...)

Arguments

data: A data frame whose first column is the gene symbol (character) and whose other columns are samples (numeric). A numeric matrix with gene symbols as row names is also accepted. Gene symbols must be unique and every sample must contain at least one nonzero value.
expr: "count" or "tpm": the scale of the values in data. Each method accepts one scale (see expr_accepted in bulkdeconv_methods()): "count" for seqimmucc, "tpm" for the others. The wrong one is refused with a message that says so.
species: "human" or "mouse".
method: A method ID; see bulkdeconv_methods().
...: Method-specific options, named: preset (xcell2), cancer_type (consensus_tme; required for timer), tissue (immucellai2, ssmd), n_permutations and seed (base). Unknown or invalid options are errors.

Value

An object of class bulkdeconv_result, a list with

score: numeric matrix, samples in rows, features in columns;
metadata: method, species, expr, options, what kind of value the method returns (output_class) and how it can be compared (comparability), licence, package versions, runtime;
qc: gene and sample counts, signature coverage when the method exposes its genes, and the notes and warnings raised.

Details

Use bulkdeconv_methods() to check the input scale, minimum sample count and output class. Scores from different methods can have different units. Cohort-dependent scores are interpreted within the run.

All reference data the methods need are installed with the package.

Examples

if (FALSE) { # \dontrun{
# df: first column gene symbols, other columns samples (TPM)
res <- bulkdeconv(df, expr = "tpm", species = "human", method = "quantiseq")
res$score
} # }