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,
  ...
)

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.

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 [2026-04-26 02:05:10] Start standard processing workflow...
#> ℹ [2026-04-26 02:05:11] Checking a list of <Seurat>...
#> ! [2026-04-26 02:05:11] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:05:11] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:05:13] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:05:14] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:05:14] Number of available HVF: 2000
#> ℹ [2026-04-26 02:05:14] Finished check
#> ℹ [2026-04-26 02:05:14] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:05:15] Perform pca linear dimension reduction
#> ℹ [2026-04-26 02:05:15] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 02:05:16] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 02:05:16] Reorder clusters...
#> ℹ [2026-04-26 02:05:16] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:05:16] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 02:05:16] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 02:05:21] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 02:05:27] Standard processing workflow completed
pancreas_sub <- RunDoubletCalling(
  pancreas_sub,
  db_method = "scDblFinder"
)
#> ℹ [2026-04-26 02:05:27] Data type is raw counts
#> ℹ [2026-04-26 02:05:27] Running scDblFinder
#> ℹ [2026-04-26 02:05:27] Data type is raw counts
#> Warning: Layer ‘data’ is empty
#> Warning: Layer ‘scale.data’ is empty
#> Warning: 'normalizeCounts' is deprecated.
#> Use 'scrapper::normalizeCounts' instead.
#> See help("Deprecated")
#> Warning: 'librarySizeFactors' is deprecated.
#> Use 'scrapper::centerSizeFactors' instead.
#> See help("Deprecated")
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