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Deconvolute protein data using multiple algorithms

Source: R/deconvolute.R
deconvolute.Rd

This function provides a unified interface for deconvoluting protein or gene expression data using various algorithms, making it easy to switch between different deconvolution methods.

Usage

deconvolute(algorithm, data, signature = NULL, ...)

Arguments

algorithm

A string specifying the deconvolution algorithm to use. Options are "cibersortx", "cibersort", "epic", and "bayesdebulk".

data

A numeric matrix containing protein or gene expression data with genes/proteins as row names and samples as columns.

signature

A numeric matrix containing the reference signature with genes/proteins as row names and cell types as columns. Required by all supported algorithms.

...

Additional arguments passed to the specific deconvolution functions.

Value

A numeric matrix with samples as rows and cell types as columns, representing the estimated proportion of each cell type in each sample.

Details

The function supports multiple deconvolution algorithms:

  • cibersortx: Uses Docker-based CIBERSORTx, which requires Docker installation and credentials (see deconvolute_cibersortx).

  • cibersort: Uses the R implementation of CIBERSORT, which requires sourcing the original CIBERSORT.R script (see deconvolute_cibersort).

  • epic: Uses the EPIC algorithm which performs constrained least squares regression (see deconvolute_epic).

  • bayesdebulk: Uses the BayesDeBulk Bayesian deconvolution algorithm (see deconvolute_bayesdebulk).

See also

deconvolute_cibersortx for the CIBERSORTx Docker implementation deconvolute_cibersort for the CIBERSORT R implementation deconvolute_epic for the EPIC algorithm deconvolute_bayesdebulk for the BayesDeBulk algorithm

Examples

if (FALSE) { # \dontrun{
# Load example data
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)

# Using EPIC algorithm
epic_results <- deconvolute(
  algorithm = "epic",
  data = mixed_samples,
  signature = signature_matrix,
  with_other_cells = TRUE
)

# Using BayesDeBulk algorithm
bayes_results <- deconvolute(
  algorithm = "bayesdebulk",
  data = mixed_samples,
  signature = signature_matrix,
  n_iter = 1000,
  burn_in = 100
)
} # }