Run doublet-calling with scds

Usage

db_scds(
  srt,
  assay = "RNA",
  db_rate = ncol(srt)/1000 * 0.01,
  method = c("hybrid", "cxds", "bcds"),
  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.
method: The method to be used for doublet-calling. Options are "hybrid", "cxds", or "bcds".
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 scds::cxds_bcds_hybrid().

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-04-26 02:32:08] Start standard processing workflow...
#> ℹ [2026-04-26 02:32:08] Checking a list of <Seurat>...
#> ! [2026-04-26 02:32:08] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:32:08] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:32:11] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:32:12] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:32:12] Number of available HVF: 2000
#> ℹ [2026-04-26 02:32:12] Finished check
#> ℹ [2026-04-26 02:32:12] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:32:13] Perform pca linear dimension reduction
#> ℹ [2026-04-26 02:32:13] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 02:32:14] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 02:32:14] Reorder clusters...
#> ℹ [2026-04-26 02:32:14] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:32:14] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 02:32:14] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 02:32:20] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 02:32:25] Standard processing workflow completed
pancreas_sub <- db_scds(pancreas_sub, method = "hybrid")
#> ℹ [2026-04-26 02:32:25] Running scds with method "hybrid"
#> ℹ [2026-04-26 02:32:26] Data type is raw counts
CellDimPlot(
  pancreas_sub,
  reduction = "umap",
  group.by = "db.scds_hybrid_class"
)


FeatureDimPlot(
  pancreas_sub,
  reduction = "umap",
  features = "db.scds_hybrid_score"
)