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
)

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: A character vector specifying the column name in the srt_ref metadata that represents the cell grouping.
query_assay: A character vector specifying the assay to be used for the query data. Default is the default assay of the srt_query object.
ref_assay: A character vector specifying the 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". Default is "scmapCluster".
nfeatures: The number of top features to be selected. Default is 500.
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. Default is 0.5.
k: Number of clusters per group for k-means clustering when method is "scmapCell". Default is 10.

Examples

data(panc8_sub)
panc8_sub <- standard_scop(panc8_sub)
#> ℹ [2026-01-27 08:23:35] Start standard scop workflow...
#> ℹ [2026-01-27 08:23:35] Checking a list of <Seurat>...
#> ! [2026-01-27 08:23:36] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:23:36] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:23:38] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:23:39] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:23:39] Number of available HVF: 2000
#> ℹ [2026-01-27 08:23:39] Finished check
#> ℹ [2026-01-27 08:23:39] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:23:40] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:23:41] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:23:41] Reorder clusters...
#> ℹ [2026-01-27 08:23:41] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:23:41] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:23:46] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:23:51] Run scop standard workflow completed

genenames <- make.unique(
  thisutils::capitalize(
    rownames(panc8_sub),
    force_tolower = TRUE
  )
)
names(genenames) <- rownames(panc8_sub)
panc8_sub <- RenameFeatures(
  panc8_sub,
  newnames = genenames
)
#> ℹ [2026-01-27 08:23:51] Rename features for the assay: RNA
panc8_sub <- CheckDataMerge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]
#> ℹ [2026-01-27 08:23:51] Spliting `srt_merge` into `srt_list` by column "tech"...
#> ℹ [2026-01-27 08:23:52] Checking a list of <Seurat>...
#> ℹ [2026-01-27 08:23:53] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-01-27 08:23:53] Perform `Seurat::FindVariableFeatures()` on the data 1/5 of the `srt_list`...
#> ℹ [2026-01-27 08:23:53] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-01-27 08:23:53] Perform `Seurat::FindVariableFeatures()` on the data 2/5 of the `srt_list`...
#> ℹ [2026-01-27 08:23:54] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-01-27 08:23:54] Perform `Seurat::FindVariableFeatures()` on the data 3/5 of the `srt_list`...
#> ℹ [2026-01-27 08:23:54] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-01-27 08:23:54] Perform `Seurat::FindVariableFeatures()` on the data 4/5 of the `srt_list`...
#> ℹ [2026-01-27 08:23:55] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-01-27 08:23:55] Perform `Seurat::FindVariableFeatures()` on the data 5/5 of the `srt_list`...
#> ℹ [2026-01-27 08:23:55] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:23:56] Number of available HVF: 2000
#> ℹ [2026-01-27 08:23:56] Finished check

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 08:23:58] Start standard scop workflow...
#> ℹ [2026-01-27 08:23:59] Checking a list of <Seurat>...
#> ! [2026-01-27 08:23:59] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:23:59] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:24:01] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:24:02] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:24:02] Number of available HVF: 2000
#> ℹ [2026-01-27 08:24:02] Finished check
#> ℹ [2026-01-27 08:24:02] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:24:03] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:24:04] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:24:04] Reorder clusters...
#> ℹ [2026-01-27 08:24:04] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:24:04] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:24:09] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:24:13] Run scop standard workflow completed
pancreas_sub <- RunScmap(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  method = "scmapCluster"
)
#> ℹ [2026-01-27 08:24:43] Data type is log-normalized
#> ℹ [2026-01-27 08:24:43] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-01-27 08:24:44] Data type is log-normalized
#> ℹ [2026-01-27 08:24:44] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-01-27 08:24:47] Perform selectFeatures
#> ℹ [2026-01-27 08:24:47] Perform indexCluster
#> ℹ [2026-01-27 08:24:47] Perform scmapCluster
CellDimPlot(
  pancreas_sub,
  group.by = "scmap_annotation"
)


pancreas_sub <- RunScmap(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  method = "scmapCell"
)
#> ℹ [2026-01-27 08:24:48] Data type is log-normalized
#> ℹ [2026-01-27 08:24:48] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-01-27 08:24:50] Data type is log-normalized
#> ℹ [2026-01-27 08:24:50] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-01-27 08:24:52] Perform selectFeatures
#> ℹ [2026-01-27 08:24:52] Perform indexCell
#> ℹ [2026-01-27 08:24:54] Perform scmapCell
#> ℹ [2026-01-27 08:24:55] Perform scmapCell2Cluster
CellDimPlot(
  pancreas_sub,
  group.by = "scmap_annotation"
)

Annotate single cells using scmap.

Usage

Arguments

See also

Examples