Annotate invariant T cells (MAIT or iNKT) in single-cell TCR data

The annotateInvariant() function identifies potential mucosal-associated invariant T (MAIT) cells or invariant natural killer T (iNKT) cells from single-cell sequencing datasets based on their characteristic TCR usage. It extracts TCR chain information from the provided single-cell data, checks it against known invariant T-cell receptor criteria for either MAIT or iNKT cells, and returns a score indicating the presence (1) or absence (0) of these invariant cell populations for each individual cell. The function supports data from mouse and human samples, providing a convenient method to annotate specialized T-cell subsets within single-cell analyses.

annotateInvariant(
  input.data,
  type = c("MAIT", "iNKT"),
  species = c("mouse", "human")
)

Arguments

input.data: The product of combineTCR() or combineExpression().
type: Character specifying the type of invariant cells to annotate ('MAIT' or 'iNKT').
species: Character specifying the species ('mouse' or 'human').

Value

A single-cell object or list with the corresponding annotation scores (0 or 1) added.

Examples

#Getting the combined contigs
combined <- combineTCR(contig_list, 
                        samples = c("P17B", "P17L", "P18B", "P18L", 
                                    "P19B","P19L", "P20B", "P20L"))

#Getting a sample of a Seurat object
scRep_example <- get(data("scRep_example"))

#Using combineExpresion()
scRep_example <- combineExpression(combined, scRep_example)

#Using annotateInvariant
annotateInvariant(input.data = scRep_example, type = "MAIT", species = "human")
#> An object of class Seurat 
#> 2000 features across 500 samples within 1 assay 
#> Active assay: RNA (2000 features, 2000 variable features)
#>  2 layers present: counts, data
#>  1 dimensional reduction calculated: umap
annotateInvariant(input.data = scRep_example, type = "iNKT", species = "human")
#> An object of class Seurat 
#> 2000 features across 500 samples within 1 assay 
#> Active assay: RNA (2000 features, 2000 variable features)
#>  2 layers present: counts, data
#>  1 dimensional reduction calculated: umap