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

A string containing "de", indicating that markers should be calculated from ref. For back compatibility, other string values are allowed but will be ignored with a deprecation warning.

Alternatively, if ref is not a list, genes can be either:

A list of lists of character vectors containing DE genes between pairs of labels.
A list of character vectors containing marker genes for each label.

If ref is a list, genes can be a list of length equal to ref. Each element of the list should be one of the two above choices described for non-list ref, containing markers for labels in the corresponding entry of ref.

de.method

String specifying how DE genes should be detected between pairs of labels. Defaults to "classic", which sorts genes by the log-fold changes and takes the top de.n. Other options are "wilcox" and "t", see Details. Ignored if genes is a list of markers/DE genes.

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.

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.

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 [2026-04-26 02:27:49] Rename features for the assay: RNA
panc8_sub <- standard_scop(panc8_sub)
#> ℹ [2026-04-26 02:27:49] Start standard processing workflow...
#> ℹ [2026-04-26 02:27:49] Checking a list of <Seurat>...
#> ! [2026-04-26 02:27:49] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:27:49] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:27:52] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:27:52] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:27:52] Number of available HVF: 2000
#> ℹ [2026-04-26 02:27:53] Finished check
#> ℹ [2026-04-26 02:27:53] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:27:53] Perform pca linear dimension reduction
#> ℹ [2026-04-26 02:27:54] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 02:27:55] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 02:27:55] Reorder clusters...
#> ℹ [2026-04-26 02:27:55] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:27:55] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 02:27:55] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 02:28:01] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 02:28:07] Standard processing workflow completed

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-04-26 02:28:07] Start standard processing workflow...
#> ℹ [2026-04-26 02:28:08] Checking a list of <Seurat>...
#> ! [2026-04-26 02:28:08] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:28:08] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:28:11] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:28:11] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:28:11] Number of available HVF: 2000
#> ℹ [2026-04-26 02:28:11] Finished check
#> ℹ [2026-04-26 02:28:12] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:28:12] Perform pca linear dimension reduction
#> ℹ [2026-04-26 02:28:13] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 02:28:13] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 02:28:13] Reorder clusters...
#> ℹ [2026-04-26 02:28:13] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:28:13] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 02:28:13] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 02:28:19] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 02:28:25] Standard processing workflow completed
pancreas_sub <- RunSingleR(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = "Standardpca_SNN_res.0.6",
  ref_group = "celltype"
)
#> ℹ [2026-04-26 02:28:25] Start SingleR annotation
#> ℹ [2026-04-26 02:29:13] Data type is log-normalized
#> ℹ [2026-04-26 02:29:13] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-04-26 02:29:14] Data type is log-normalized
#> ℹ [2026-04-26 02:29:14] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-04-26 02:29:15] Perform "SingleRCluster"
#> Detected a large SingleCellExperiment as the reference dataset, consider
#> setting 'aggr.ref = TRUE' for speed in trainSingleR(). If you know better, this
#> hint can be disabled with 'hint.sce=FALSE'.
#> ✔ [2026-04-26 02:29:16] SingleR annotation completed
CellDimPlot(
  pancreas_sub,
  group.by = c("singler_annotation", "SubCellType")
)


pancreas_sub <- RunSingleR(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  query_group = NULL,
  ref_group = "celltype"
)
#> ℹ [2026-04-26 02:29:16] Start SingleR annotation
#> ℹ [2026-04-26 02:29:17] Data type is log-normalized
#> ℹ [2026-04-26 02:29:17] Detected `srt_query` data type: "log_normalized_counts"
#> ℹ [2026-04-26 02:29:17] Data type is log-normalized
#> ℹ [2026-04-26 02:29:17] Detected `srt_ref` data type: "log_normalized_counts"
#> ℹ [2026-04-26 02:29:18] Perform "SingleRCell"
#> Detected a large SingleCellExperiment as the reference dataset, consider
#> setting 'aggr.ref = TRUE' for speed in trainSingleR(). If you know better, this
#> hint can be disabled with 'hint.sce=FALSE'.
#> ✔ [2026-04-26 02:29:21] SingleR annotation completed
CellDimPlot(
  pancreas_sub,
  group.by = c("singler_annotation", "SubCellType"),
  label = TRUE
)


CellCorHeatmap(
  pancreas_sub,
  group.by = "singler_annotation",
  assay = "RNA",
  layer = "data",
  method = "spearman"
)
#> Error in CellCorHeatmap(pancreas_sub, group.by = "singler_annotation",     assay = "RNA", layer = "data", method = "spearman"): unused arguments (group.by = "singler_annotation", assay = "RNA", layer = "data", method = "spearman")

ht1 <- CellCorHeatmap(
  srt_query = pancreas_sub,
  srt_ref = pancreas_sub,
  query_group = "SubCellType",
  cluster_rows = TRUE,
  ref_group = "singler_annotation",
  cluster_columns = TRUE,
  width = 2,
  height = 2
)
#> ℹ [2026-04-26 02:29:22] Drop [1] 19 cells with NA in the ref_group
#> ℹ [2026-04-26 02:29:22] Use the HVF to calculate distance metric
#> ℹ [2026-04-26 02:29:22] Use [1] 2000 features to calculate distance.
#> ℹ [2026-04-26 02:29:23] Detected query data type: "log_normalized_counts"
#> ℹ [2026-04-26 02:29:23] Detected reference data type: "log_normalized_counts"
#> ℹ [2026-04-26 02:29:23] Calculate similarity...
#> ℹ [2026-04-26 02:29:23] Use raw method to find neighbors
#> ℹ [2026-04-26 02:29:23] Predict cell type...

ht1$plot

Annotate single cells using SingleR

Usage

Arguments

Value

See also

Examples