Annotate single cells using scmap

Usage

RunScmap(
  srt_query,
  srt_ref,
  ref_group = NULL,
  query_assay = "RNA",
  ref_assay = "RNA",
  method = "scmapCluster",
  nfeatures = 500,
  threshold = 0.5,
  k = 10,
  verbose = TRUE
)

Arguments

srt_query: An object of class Seurat to be annotated with cell types.
srt_ref: An object of class Seurat storing the reference cells.
ref_group: Column name in the srt_ref metadata that represents the cell grouping.
query_assay: Assay to be used for the query data. Default is the default assay of the srt_query object.
ref_assay: Assay to be used for the reference data. Default is the default assay of the srt_ref object.
method: The method to be used for scmap analysis. Can be any of "scmapCluster" or "scmapCell".
nfeatures: The number of top features to be selected.
threshold: The threshold value on similarity to determine if a cell is assigned to a cluster. This should be a value between 0 and 1.
k: Number of clusters per group for k-means clustering when method is "scmapCell".
verbose: Whether to print the message. Default is TRUE.

Examples

data(panc8_sub)
keep <- panc8_sub$celltype %in% c("ductal", "alpha", "beta")
panc8_sub <- Seurat::NormalizeData(panc8_sub[, keep], verbose = FALSE)
reference <- panc8_sub[, panc8_sub$tech != "fluidigmc1"]
query <- panc8_sub[, panc8_sub$tech == "fluidigmc1"]
query <- RunScmap(
  srt_query = query, srt_ref = reference,
  ref_group = "celltype", method = "scmapCluster", nfeatures = 200,
  verbose = FALSE
)
#> ℹ [2026-10-11 23:47:45] Data type is log-normalized
#> ℹ [2026-10-11 23:47:46] Data type is log-normalized
query <- RunStandardWorkflow(query, verbose = FALSE, linear_reduction_dims = 10)
#> ℹ [2026-10-11 23:47:47] Skip `log1p()` because `layer = data` is not "counts"
CellDimPlot(
  query,
  group.by = "scmap_annotation",
  label = TRUE, legend.position = "bottom"
)

Annotate single cells using scmap

Usage

Arguments

See also

Examples