Applies the CIBERSORT algorithm to deconvolute bulk proteome data into constituent cell types using a signature matrix. The function requires the original CIBERSORT.R script to be sourced.
Usage
deconvolute_cibersort(
data,
signature,
QN = FALSE,
absolute = FALSE,
abs_method = "sig.score",
...
)Arguments
- data
A numeric matrix containing mixture data with genes as row names and samples as columns.
- signature
A numeric matrix containing signature data with genes as row names and cell types as columns.
- QN
Logical indicating whether quantile normalization is performed (default FALSE).
- absolute
Logical indicating whether an absolute score is computed (default FALSE).
- abs_method
Method for absolute scoring if absolute is TRUE (default "sig.score").
- ...
Additional arguments passed to the CIBERSORT function.
Value
A numeric matrix with samples as rows and cell types as columns, representing the estimated proportion of each cell type in each sample. The returned matrix excludes CIBERSORT's diagnostic columns (RMSE, P-value, Correlation).
Details
This function requires the original CIBERSORT.R script from the CIBERSORT website (https://cibersortx.stanford.edu/) to be sourced before use. It writes temporary files for the mixture data and signature matrix, calls the CIBERSORT function, and processes the results.
See also
deconvolute for a unified interface to multiple
deconvolution methods.
Examples
if (FALSE) { # \dontrun{
# First source the CIBERSORT.R script
source("/path/to/CIBERSORT.R")
# 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_cibersort(
data = mixed_samples,
signature = signature_matrix,
QN = FALSE,
absolute = FALSE
)
} # }