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Annotate single cells using native SciBet

Source: R/RunSciBet.R
RunSciBet.Rd

Run a native scop implementation of the core SciBet classifier using a labeled reference Seurat object.

Usage

RunSciBet(
  srt_query,
  srt_ref,
  ref_group,
  query_group = NULL,
  query_assay = NULL,
  ref_assay = NULL,
  query_layer = "counts",
  ref_layer = "counts",
  features = NULL,
  nfeatures = 1000,
  additional_features_per_class = 0,
  input_transform = c("auto", "none", "expm1"),
  prefix = "scibet",
  store_model = TRUE,
  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.

ref_group

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

query_group

A character vector specifying the column name in the srt_query 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.

query_layer, ref_layer

Assay layers used for query and reference.

features

Candidate features used by SciBet. If NULL, common genes between query and reference are used.

nfeatures

Number of entropy-test features selected from features.

additional_features_per_class

Additional high-expression features selected per reference class.

input_transform

How to transform extracted values before SciBet's internal log1p step. "auto" applies expm1 to "data" layers and no transform otherwise.

prefix

Prefix for metadata columns.

store_model

Whether to store the SciBet core and probabilities in srt_query@tools.

verbose

Whether to print the message. Default is TRUE.

Value

A Seurat object with SciBet annotations in metadata and results in srt_query@tools[["SciBet"]].

Examples

data(panc8_sub)
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 07:37:29] Rename features for the assay: RNA

data(pancreas_sub)
pancreas_sub <- RunSciBet(
  srt_query = pancreas_sub,
  srt_ref = panc8_sub,
  ref_group = "celltype",
  nfeatures = 200
)
#> ℹ [2026-05-14 07:37:30] Run native SciBet with 12928 candidate features and 13 reference classes
#> ℹ [2026-05-14 07:37:32] SciBet annotations stored in metadata column "scibet_annotation"
pancreas_sub <- standard_scop(pancreas_sub, verbose = FALSE)
#> ℹ [2026-05-14 07:37:37] Skip `log1p()` because `layer = data` is not "counts"
CellDimPlot(
  pancreas_sub,
  group.by = c("SubCellType", "scibet_annotation"),
  xlab = "UMAP_1",
  ylab = "UMAP_2"
)


ht <- CellCorHeatmap(
  srt_query = pancreas_sub,
  srt_ref = pancreas_sub,
  query_group = "scibet_annotation",
  ref_group = "SubCellType",
  width = 3,
  height = 3
)
#> ℹ [2026-05-14 07:37:50] Use the HVF to calculate distance metric
#> ℹ [2026-05-14 07:37:50] Use [1] 2000 features to calculate distance.
#> ℹ [2026-05-14 07:37:50] Detected query data type: "log_normalized_counts"
#> ℹ [2026-05-14 07:37:50] Detected reference data type: "log_normalized_counts"
#> ℹ [2026-05-14 07:37:50] Calculate similarity...
#> ℹ [2026-05-14 07:37:50] Use raw method to find neighbors
#> ℹ [2026-05-14 07:37:50] Predict cell type...

ht$plot