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Annotate single cells using SingleR

Source: R/RunSingleR.R
RunSingleR.Rd

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

Column name in the srt_query metadata that represents the cell grouping.

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.

genes

Marker selection strategy: "de" to detect markers from the reference, or a list of marker genes accepted by SingleR::trainSingleR.

de.method

Differential expression method used to select markers: "wilcox", "t", or "classic". Ignored when genes supplies markers.

sd.thresh

Numeric threshold forwarded to SingleR::SingleR for compatibility with older versions; ignored by current SingleR versions.

de.n

Number of marker genes selected per pair of labels. NULL uses the default for the selected de.method in SingleR::trainSingleR.

aggr.ref

Logical indicating whether to aggregate reference cells into pseudo-bulk samples before classification.

aggr.args

Named list of arguments passed to SingleR::aggregateReference when aggr.ref = TRUE.

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 worker processes to use for parallelization. Default is 1.

verbose

Whether to print the message. Default is TRUE.

Value

An annotate Seurat object. The annotation results are stored in the singler_annotation column of the meta data, and the corresponding scores are stored in the singler_score column.

See also

RunKNNPredict, RunKNNMap

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 <- RunSingleR(
  srt_query = query, srt_ref = reference,
  ref_group = "celltype", genes = "de", cores = 1, verbose = FALSE
)
#> ℹ [2026-11-12 06:04:25] Data type is log-normalized
#> ℹ [2026-11-12 06:04:25] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-11-12 06:04:25] Data type is log-normalized
#> ℹ [2026-11-12 06:04:25] Detected `srt_ref` data type: "log_normalized_counts"
query <- RunStandardWorkflow(query, verbose = FALSE, linear_reduction_dims = 10)
#> ℹ [2026-11-12 06:04:26] Skip `log1p()` because `layer = data` is not "counts"
CellDimPlot(
  query,
  group.by = "singler_annotation",
  label = TRUE, legend.position = "bottom"
)

FeatureStatPlot(
  query,
  stat.by = "singler_score",
  group.by = "singler_annotation"
)