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. Defaults to the default assay of the `srt_query` object.
ref_assay: A character vector specifying the assay to be used for the reference data. Defaults to the default assay of the `srt_ref` object.
method: The method to be used for scmap analysis. Can be any of "scmapCluster" or "scmapCell". The default value is "scmapCluster".
nfeatures: The number of top features to be selected. The default value 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. The default value is 0.5.
k: Number of clusters per group for k-means clustering when method is "scmapCell".

Examples

data("panc8_sub")

genenames <- make.unique(
  capitalize(rownames(panc8_sub),
    force_tolower = TRUE
  )
)
names(genenames) <- rownames(panc8_sub)
panc8_sub <- RenameFeatures(
  panc8_sub,
  newnames = genenames
)
#> ℹ [2025-07-02 03:04:53] Rename features for the assay: RNA
panc8_sub <- check_srt_merge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]
#> ℹ [2025-07-02 03:04:54]  Spliting srt_merge into srt_list by column tech...
#> ℹ [2025-07-02 03:04:55] Checking srt_list...
#> ℹ [2025-07-02 03:04:55] Data 1/5 of the srt_list has been log-normalized.
#> ℹ [2025-07-02 03:04:55] Perform FindVariableFeatures on the data 1/5 of the srt_list...
#> ℹ [2025-07-02 03:04:55] Data 2/5 of the srt_list has been log-normalized.
#> ℹ [2025-07-02 03:04:55] Perform FindVariableFeatures on the data 2/5 of the srt_list...
#> ℹ [2025-07-02 03:04:56] Data 3/5 of the srt_list has been log-normalized.
#> ℹ [2025-07-02 03:04:56] Perform FindVariableFeatures on the data 3/5 of the srt_list...
#> ℹ [2025-07-02 03:04:56] Data 4/5 of the srt_list has been log-normalized.
#> ℹ [2025-07-02 03:04:56] Perform FindVariableFeatures on the data 4/5 of the srt_list...
#> ℹ [2025-07-02 03:04:57] Data 5/5 of the srt_list has been log-normalized.
#> ℹ [2025-07-02 03:04:57] Perform FindVariableFeatures on the data 5/5 of the srt_list...
#> ℹ [2025-07-02 03:04:57] Use the separate HVF from srt_list...
#> ℹ [2025-07-02 03:04:58] Number of available HVF: 2000
#> ℹ [2025-07-02 03:04:58] Finished checking.

data("pancreas_sub")
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2025-07-02 03:05:01] Start standard_scop
#> ℹ [2025-07-02 03:05:01] Checking srt_list...
#> ℹ [2025-07-02 03:05:03] Data 1/1 of the srt_list has been log-normalized.
#> ℹ [2025-07-02 03:05:03] Perform FindVariableFeatures on the data 1/1 of the srt_list...
#> ℹ [2025-07-02 03:05:04] Use the separate HVF from srt_list...
#> ℹ [2025-07-02 03:05:04] Number of available HVF: 2000
#> ℹ [2025-07-02 03:05:04] Finished checking.
#> ℹ [2025-07-02 03:05:04] Perform ScaleData on the data...
#> ℹ [2025-07-02 03:05:04] Perform linear dimension reduction (pca) on the data...
#> ℹ [2025-07-02 03:05:04] linear_reduction(pca) is already existed. Skip calculation.
#> ℹ [2025-07-02 03:05:05] Perform FindClusters (louvain) on the data...
#> ℹ [2025-07-02 03:05:05] Reorder clusters...
#> ! [2025-07-02 03:05:05] Using 'Seurat::AggregateExpression()' to calculate pseudo-bulk data for 'Assay5'.
#> ℹ [2025-07-02 03:05:05] Perform nonlinear dimension reduction (umap) on the data...
#> ℹ [2025-07-02 03:05:05] Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims:1-50) as input
#> ℹ [2025-07-02 03:05:09] Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims:1-50) as input
#> ✔ [2025-07-02 03:05:14] Run standard_scop done
#> ℹ [2025-07-02 03:05:14] Elapsed time:12.22 secs
pancreas_sub <- RunScmap(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  method = "scmapCluster"
)
#> ℹ [2025-07-02 03:05:14] Detected srt_query data type: log_normalized_counts
#> ℹ [2025-07-02 03:05:16] Detected srt_ref data type: log_normalized_counts
#> ℹ [2025-07-02 03:05:18] Perform selectFeatures on the data...
#> ℹ [2025-07-02 03:05:18] Perform indexCluster on the data...
#> ℹ [2025-07-02 03:05:18] Perform scmapCluster on the data...
#> 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"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.


pancreas_sub <- RunScmap(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  method = "scmapCell"
)
#> ℹ [2025-07-02 03:05:19] Detected srt_query data type: log_normalized_counts
#> ℹ [2025-07-02 03:05:21] Detected srt_ref data type: log_normalized_counts
#> ℹ [2025-07-02 03:05:23] Perform selectFeatures on the data...
#> ℹ [2025-07-02 03:05:23] Perform indexCell on the data...
#> ℹ [2025-07-02 03:05:23] Perform scmapCell on the data...
#> ℹ [2025-07-02 03:05:24] Perform scmapCell2Cluster on the data...
CellDimPlot(
  pancreas_sub,
  group.by = "scmap_annotation"
)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.