Annotate single cells using SingleR

Annotate single cells using SingleR

Usage

RunSingleR(
  srt_query,
  srt_ref,
  query_group = NULL,
  ref_group = NULL,
  query_assay = "RNA",
  ref_assay = "RNA",
  genes = "de",
  de.method = "wilcox",
  sd.thresh = 1,
  de.n = NULL,
  aggr.ref = FALSE,
  aggr.args = list(),
  quantile = 0.8,
  fine.tune = TRUE,
  tune.thresh = 0.05,
  prune = TRUE,
  cores = 1,
  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.

query_group

A character vector specifying the column name in the srt_query metadata that represents the cell grouping.

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.

genes

"genes" parameter in SingleR::SingleR function.

de.method

"de.method" parameter in SingleR::SingleR function.

sd.thresh

Deprecated and ignored.

de.n

An integer scalar specifying the number of DE genes to use when genes="de". If de.method="classic", defaults to 500 * (2/3) ^ log2(N) where N is the number of unique labels. Otherwise, defaults to 10. Ignored if genes is a list of markers/DE genes.

aggr.ref, aggr.args

Arguments controlling the aggregation of the references prior to annotation, see trainSingleR.

quantile

"quantile" parameter in SingleR::SingleR function.

fine.tune

"fine.tune" parameter in SingleR::SingleR function.

tune.thresh

"tune.thresh" parameter in SingleR::SingleR function.

prune

"prune" parameter in SingleR::SingleR function.

cores

The number of cores to use for parallelization with foreach::foreach. Default is 1.

verbose

Whether to print the message. Default is TRUE.

See also

RunKNNPredict, RunKNNMap

Examples

data(panc8_sub)
# Simply convert genes from human to mouse and preprocess the data
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 08:25:41] Rename features for the assay: RNA
panc8_sub <- CheckDataMerge(
  panc8_sub,
  batch = "tech"
)[["srt_merge"]]
#> ℹ [2026-01-27 08:25:41] Spliting `srt_merge` into `srt_list` by column "tech"...
#> ℹ [2026-01-27 08:25:41] Checking a list of <Seurat>...
#> ! [2026-01-27 08:25:41] Data 1/5 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:25:41] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/5 of the `srt_list`...
#> ℹ [2026-01-27 08:25:43] Perform `Seurat::FindVariableFeatures()` on the data 1/5 of the `srt_list`...
#> ! [2026-01-27 08:25:44] Data 2/5 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:25:44] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 2/5 of the `srt_list`...
#> ℹ [2026-01-27 08:25:45] Perform `Seurat::FindVariableFeatures()` on the data 2/5 of the `srt_list`...
#> ! [2026-01-27 08:25:46] Data 3/5 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:25:46] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 3/5 of the `srt_list`...
#> ℹ [2026-01-27 08:25:47] Perform `Seurat::FindVariableFeatures()` on the data 3/5 of the `srt_list`...
#> ! [2026-01-27 08:25:48] Data 4/5 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:25:48] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 4/5 of the `srt_list`...
#> ℹ [2026-01-27 08:25:49] Perform `Seurat::FindVariableFeatures()` on the data 4/5 of the `srt_list`...
#> ! [2026-01-27 08:25:50] Data 5/5 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:25:50] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 5/5 of the `srt_list`...
#> ℹ [2026-01-27 08:25:52] Perform `Seurat::FindVariableFeatures()` on the data 5/5 of the `srt_list`...
#> ℹ [2026-01-27 08:25:52] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:25:52] Number of available HVF: 2000
#> ℹ [2026-01-27 08:25:54] Finished check

# Annotation
data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 08:25:56] Start standard scop workflow...
#> ℹ [2026-01-27 08:25:57] Checking a list of <Seurat>...
#> ! [2026-01-27 08:25:57] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:25:57] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:25:59] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:26:00] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:26:00] Number of available HVF: 2000
#> ℹ [2026-01-27 08:26:00] Finished check
#> ℹ [2026-01-27 08:26:01] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:26:01] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:26:02] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:26:02] Reorder clusters...
#> ℹ [2026-01-27 08:26:02] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:26:02] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:26:07] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:26:12] Run scop standard workflow completed
pancreas_sub <- RunSingleR(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = "Standardpca_SNN_res.0.6",
  ref_group = "celltype"
)
#> ℹ [2026-01-27 08:26:12] Start SingleR annotation
#> ℹ [2026-01-27 08:33:02] Data type is log-normalized
#> ℹ [2026-01-27 08:33:02] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-01-27 08:33:03] Data type is log-normalized
#> ℹ [2026-01-27 08:33:03] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-01-27 08:33:06] Perform "SingleRCluster"
#> ✔ [2026-01-27 08:33:07] SingleR annotation completed
CellDimPlot(
  pancreas_sub,
  group.by = c("singler_annotation", "CellType")
)


pancreas_sub <- RunSingleR(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = NULL,
  ref_group = "celltype"
)
#> ℹ [2026-01-27 08:33:08] Start SingleR annotation
#> ℹ [2026-01-27 08:33:08] Data type is log-normalized
#> ℹ [2026-01-27 08:33:08] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-01-27 08:33:12] Data type is log-normalized
#> ℹ [2026-01-27 08:33:12] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-01-27 08:33:18] Perform "SingleRCell"
#> ✔ [2026-01-27 08:33:22] SingleR annotation completed
CellDimPlot(
  pancreas_sub,
  group.by = c("singler_annotation", "CellType")
)