Run doublet-calling for single cell RNA-seq data.

Usage

RunDoubletCalling(
  srt,
  assay = "RNA",
  db_rate = ncol(srt)/1000 * 0.01,
  db_method = "scDblFinder",
  data_type = NULL,
  ...,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
assay: The name of the assay to be used for doublet-calling. Default is "RNA".
db_rate: The expected doublet rate. Default is calculated as ncol(srt) / 1000 * 0.01.
db_method: Method used for doublet-calling. Can be one of "scDblFinder", "Scrublet", "DoubletDetection", "scds_cxds", "scds_bcds", "scds_hybrid".
data_type: Optional precomputed result from CheckDataType for the input assay. Primarily used internally to avoid repeated scans of the same count matrix across nested QC calls.
...: Additional arguments to be passed to the corresponding doublet-calling method.
verbose: Whether to print the message. Default is TRUE.

Value

Returns a Seurat object with the doublet prediction results and prediction scores stored in the meta.data.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-07-02 09:37:22] Start standard processing workflow...
#> ℹ [2026-07-02 09:37:23] Checking a list of <Seurat>...
#> ! [2026-07-02 09:37:23] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-07-02 09:37:23] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-07-02 09:37:23] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-02 09:37:23] Use the separate HVF from `srt_list`
#> ℹ [2026-07-02 09:37:23] Number of available HVF: 2000
#> ℹ [2026-07-02 09:37:23] Finished check
#> ℹ [2026-07-02 09:37:23] Perform `ScaleData()`
#> ℹ [2026-07-02 09:37:23] Perform pca linear dimension reduction
#> ℹ [2026-07-02 09:37:24] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-07-02 09:37:24] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-02 09:37:24] Reorder clusters...
#> ℹ [2026-07-02 09:37:24] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-02 09:37:24] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-02 09:37:31] Standard processing workflow completed
pancreas_sub <- RunDoubletCalling(
  pancreas_sub,
  db_method = "scDblFinder"
)
#> ℹ [2026-07-02 09:37:31] Data type is raw counts
#> ℹ [2026-07-02 09:37:31] Running scDblFinder
#> ℹ [2026-07-02 09:37:32] Data type is raw counts
#> Warning: Layer ‘data’ is empty
#> Warning: Layer ‘scale.data’ is empty
CellDimPlot(
  pancreas_sub,
  reduction = "umap",
  group.by = "db.scDblFinder_class"
)
#> Error in DefaultReduction(srt, pattern = reduction): Unable to find any reductions

FeatureDimPlot(
  pancreas_sub,
  reduction = "umap",
  features = "db.scDblFinder_score"
)
#> Error in DefaultReduction(srt, pattern = reduction): Unable to find any reductions