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Create cell type signatures from scRNAseq

Source: R/cell_deconvolution.R
create_sc_signatures.Rd

Create cell type signatures from scRNAseq

Usage

create_sc_signatures(
  sc_obj,
  sc_metadata,
  cells_labels,
  sample_labels,
  credentials.mail = NULL,
  credentials.token = NULL,
  bulk_rna = NULL,
  cell_markers = NULL,
  name_signature = NULL,
  methods_sig = c("DWLS", "CIBERSORTx", "MOMF", "BSeqsc")
)

Arguments

sc_obj

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.

cells_labels

A character vector with the cell labels (need to be of the same order as in the sc_object)

sample_labels

A character vector with the samples labels (need to be of the same order as in the sc_object)

credentials.mail

(Optional) Credential email for running CIBERSORTx If not provided, CIBERSORTx method will not be run.

credentials.token

(Optional) Credential token for running CIBERSORTx. If not provided, CIBERSORTx method will not be run.

bulk_rna

A matrix of bulk data. Rows are genes, columns are samples. This is needed for MOMF method, if not given the method will not be run.

cell_markers

Named list with the genes markers names as Symbol per cell types to be used to create the signature using the BSeq-SC method. If NULL, the method will be ignored during the signature creation.

name_signature

A string indicating the signature name. This will be added as a suffix in each method (e.g. CBSX_name_signature, DWLS_name_signature)

methods_sig

A character vector specifying which methods to run. Options are "DWLS", "CIBERSORTx", "MOMF", and "BSeqsc". Default runs all available methods.

Value

A list containing the cell signatures per method. Signatures are directly saved in Results/custom_signatures folder, these will be used to run deconvolution.

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