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 [2026-05-14 07:37:52] Start standard processing workflow...
#> ℹ [2026-05-14 07:37:52] Checking a list of <Seurat>...
#> ! [2026-05-14 07:37:52] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-05-14 07:37:52] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 07:37:54] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 07:37:55] Use the separate HVF from `srt_list`
#> ℹ [2026-05-14 07:37:55] Number of available HVF: 2000
#> ℹ [2026-05-14 07:37:55] Finished check
#> ℹ [2026-05-14 07:37:55] Perform `Seurat::ScaleData()`
#> ℹ [2026-05-14 07:37:56] Perform pca linear dimension reduction
#> ℹ [2026-05-14 07:37:57] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-05-14 07:37:57] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-05-14 07:37:57] Reorder clusters...
#> ℹ [2026-05-14 07:37:57] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-05-14 07:37:57] Perform umap nonlinear dimension reduction
#> ℹ [2026-05-14 07:37:57] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-05-14 07:38:04] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-05-14 07:38:11] Standard processing 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 [2026-05-14 07:38:11] Rename features for the assay: RNA
panc8_sub <- CheckDataMerge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]
#> ℹ [2026-05-14 07:38:11] Split `srt_merge` into `srt_list` by "tech"
#> ℹ [2026-05-14 07:38:12] Checking a list of <Seurat>...
#> ℹ [2026-05-14 07:38:12] Data 1/5 of the `srt_list` has been log-normalized
#> ℹ [2026-05-14 07:38:12] Perform `Seurat::FindVariableFeatures()` on 1/5 of `srt_list`...
#> ℹ [2026-05-14 07:38:13] Data 2/5 of the `srt_list` has been log-normalized
#> ℹ [2026-05-14 07:38:13] Perform `Seurat::FindVariableFeatures()` on 2/5 of `srt_list`...
#> ℹ [2026-05-14 07:38:14] Data 3/5 of the `srt_list` has been log-normalized
#> ℹ [2026-05-14 07:38:14] Perform `Seurat::FindVariableFeatures()` on 3/5 of `srt_list`...
#> ℹ [2026-05-14 07:38:14] Data 4/5 of the `srt_list` has been log-normalized
#> ℹ [2026-05-14 07:38:14] Perform `Seurat::FindVariableFeatures()` on 4/5 of `srt_list`...
#> ℹ [2026-05-14 07:38:15] Data 5/5 of the `srt_list` has been log-normalized
#> ℹ [2026-05-14 07:38:15] Perform `Seurat::FindVariableFeatures()` on 5/5 of `srt_list`...
#> ℹ [2026-05-14 07:38:15] Use the separate HVF from `srt_list`
#> ℹ [2026-05-14 07:38:16] Number of available HVF: 2000
#> ℹ [2026-05-14 07:38:16] Finished check
#> Warning: Key ‘StandardpcaUMAP2D_’ taken, using ‘standardumap2d_’ instead
#> Warning: Key ‘StandardpcaUMAP3D_’ taken, using ‘standardumap3d_’ instead

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-05-14 07:38:18] Start standard processing workflow...
#> ℹ [2026-05-14 07:38:19] Checking a list of <Seurat>...
#> ! [2026-05-14 07:38:19] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-05-14 07:38:19] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 07:38:21] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-05-14 07:38:22] Use the separate HVF from `srt_list`
#> ℹ [2026-05-14 07:38:22] Number of available HVF: 2000
#> ℹ [2026-05-14 07:38:22] Finished check
#> ℹ [2026-05-14 07:38:22] Perform `Seurat::ScaleData()`
#> ℹ [2026-05-14 07:38:22] Perform pca linear dimension reduction
#> ℹ [2026-05-14 07:38:23] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-05-14 07:38:23] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-05-14 07:38:23] Reorder clusters...
#> ℹ [2026-05-14 07:38:24] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-05-14 07:38:24] Perform umap nonlinear dimension reduction
#> ℹ [2026-05-14 07:38:24] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-05-14 07:38:30] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-05-14 07:38:36] Standard processing workflow completed
pancreas_sub <- RunScmap(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  method = "scmapCluster"
)
#> ℹ [2026-05-14 07:39:08] Data type is log-normalized
#> ℹ [2026-05-14 07:39:08] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-05-14 07:39:10] Data type is log-normalized
#> ℹ [2026-05-14 07:39:10] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-05-14 07:39:12] Perform selectFeatures
#> ℹ [2026-05-14 07:39:12] Perform indexCluster
#> ℹ [2026-05-14 07:39:12] Perform scmapCluster
#> Warning: Features Mt-atp6, Mt-co1, Mt-co2, Mt-co3, Mt-nd1, Mt-nd2, Mt-nd4, Mt-nd4l, Mt-nd5 are not present in the 'SCESet' object and therefore were not set.
CellDimPlot(
  pancreas_sub,
  group.by = "scmap_annotation"
)


pancreas_sub <- RunScmap(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  method = "scmapCell"
)
#> ℹ [2026-05-14 07:39:13] Data type is log-normalized
#> ℹ [2026-05-14 07:39:13] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-05-14 07:39:15] Data type is log-normalized
#> ℹ [2026-05-14 07:39:15] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-05-14 07:39:17] Perform selectFeatures
#> ℹ [2026-05-14 07:39:17] Perform indexCell
#> ℹ [2026-05-14 07:39:18] Perform scmapCell
#> ℹ [2026-05-14 07:39:19] Perform scmapCell2Cluster
CellDimPlot(
  pancreas_sub,
  group.by = "scmap_annotation"
)

Annotate single cells using scmap.

Usage

Arguments

See also

Examples