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
Name of the
sc_metadatacolumn with the cell type labels. The labels become the cell type names of the signatures, so they must follow the multideconv nomenclature (seeget_cell_type_nomenclature()and the README); otherwise those cell types are discarded later bycompute.deconvolution.analysis().- sample_labels
Name of the
sc_metadatacolumn with the sample labels.- 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, used in the file names (e.g.
DWLS-<name_signature>-scRNAseq.txt). It must not contain_(replaced by-), which separates method, signature and cell type in the deconvolution column names.- methods_sig
A character vector specifying which methods to run. Options are "DWLS", "CIBERSORTx" (or "CBSX"), "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 (an existing file with the same name is overwritten), 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
