Deconvolute bulk proteome data using BayesDeBulk
Source:R/deconvolute_bayesdebulk.R
deconvolute_bayesdebulk.Rd
Deconvolutes bulk proteome data using the BayesDeBulk algorithm to estimate cell type proportions in mixed samples based on a signature matrix.
Usage
deconvolute_bayesdebulk(
data,
signature,
n_iter = 1000,
burn_in = 100,
marker_selection = "limma",
...
)
Arguments
- data
A numeric matrix of bulk proteome data with gene identifiers as row names and samples as columns.
- signature
A numeric matrix containing signature marker values with gene identifiers as row names and cell types as columns.
- n_iter
Number of iterations for the MCMC sampling; default is 1000.
- burn_in
Number of burn-in iterations to discard; default is 100.
- marker_selection
The method to use for marker selection: "limma" (default), "simple", or a pre-computed marker matrix.
- ...
Additional arguments passed to marker selection functions.
Details
This function calculates signature markers using the specified marker selection method and runs the BayesDeBulk deconvolution algorithm. The marker selection process identifies genes that are uniquely expressed in specific cell types, which are then used for the deconvolution.
Examples
if (FALSE) { # \dontrun{
# Load example data and signature matrix
data_file <- system.file("extdata", "mixed_samples_matrix.rds", package = "proteoDeconv")
mixed_samples <- readRDS(data_file)
signature_file <- system.file("extdata", "cd8t_mono_signature_matrix.rds", package = "proteoDeconv")
signature_matrix <- readRDS(signature_file)
# Run deconvolution
result <- deconvolute_bayesdebulk(mixed_samples, signature_matrix)
} # }