Compute second-generation deconvolution methods

Usage

compute_sc_deconvolution_methods(
  raw_counts,
  normalized = TRUE,
  methods_sc = c("Autogenes", "BayesPrism", "Bisque", "CPM", "MuSic", "SCDC"),
  sc_object,
  sc_metadata,
  cell_annotations,
  samples_ids,
  name_object,
  n_cores = NULL,
  return = FALSE,
  file_name = NULL
)

Arguments

raw_counts: A matrix with raw counts (samples as columns and genes symbols as rows)
normalized: Boolean value to specify if raw_counts need to be normalized (If no raw_counts are available and argument corresponds to already normalized counts this arguments needs to be set to False)
methods_sc: A character vector with the sc-deconvolution methods to run. Default are "Autogenes", "BayesPrism", "Bisque", "CPM", "MuSic", "SCDC"
sc_object: A matrix with the counts from scRNAseq object (genes as rows and cells as columns)
sc_metadata: Dataframe with metadata from the single cell object. The matrix should include the columns cell_label and sample_label.
cell_annotations: A string with the column name with the cell labels (column should be of the same order as in the sc_object)
samples_ids: A string with the column name with the samples labels (column should be of the same order as in the sc_object)
name_object: Signature name to use in the generated single cell signature for deconvolving the bulk RNAseq data
n_cores: Number of cores to use for paralellization. If no number is set, detectCores() - 1 will be set as the number.
return: Whether to save or not the csv file with the deconvolution features.
file_name: File name for the .csv file to save with the deconvolution results.

Value

A matrix of deconvolution features across samples from your bulk counts based on the second generation methods.

References

Sturm, G., Finotello, F., Petitprez, F., Zhang, J. D., Baumbach, J., Fridman, W. H., ..., List, M., Aneichyk, T. (2019). Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology. Bioinformatics, 35(14), i436-i445. https://doi.org/10.1093/bioinformatics/btz363

Benchmarking second-generation methods for cell-type deconvolution of transcriptomic data. Dietrich, Alexander and Merotto, Lorenzo and Pelz, Konstantin and Eder, Bernhard and Zackl, Constantin and Reinisch, Katharina and Edenhofer, Frank and Marini, Federico and Sturm, Gregor and List, Markus and Finotello, Francesca. (2024) https://doi.org/10.1101/2024.06.10.598226