Deconvolute bulk proteome data using BayesDeBulk — deconvolute

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.

Value

A matrix containing cell type proportions with samples as rows and cell types as columns.

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)
} # }