Run doublet-calling with scds

Usage

db_scds(
  srt,
  assay = "RNA",
  db_rate = ncol(srt)/1000 * 0.01,
  method = c("hybrid", "cxds", "bcds"),
  ...
)

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".
...: Additional arguments to be passed to scds::cxds_bcds_hybrid().

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-02-11 04:21:03] Start standard scop workflow...
#> ℹ [2026-02-11 04:21:04] Checking a list of <Seurat>...
#> ! [2026-02-11 04:21:04] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-02-11 04:21:04] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-02-11 04:21:06] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-02-11 04:21:06] Use the separate HVF from srt_list
#> ℹ [2026-02-11 04:21:07] Number of available HVF: 2000
#> ℹ [2026-02-11 04:21:07] Finished check
#> ℹ [2026-02-11 04:21:07] Perform `Seurat::ScaleData()`
#> ℹ [2026-02-11 04:21:07] Perform pca linear dimension reduction
#> ℹ [2026-02-11 04:21:08] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-02-11 04:21:08] Reorder clusters...
#> ℹ [2026-02-11 04:21:08] Perform umap nonlinear dimension reduction
#> ℹ [2026-02-11 04:21:08] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-02-11 04:21:13] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-02-11 04:21:17] Run scop standard workflow completed
pancreas_sub <- db_scds(pancreas_sub, method = "hybrid")
#> ℹ [2026-02-11 04:21:18] Data type is raw counts
#> Registered S3 method overwritten by 'pROC':
#>   method   from            
#>   plot.roc spatstat.explore
CellDimPlot(
  pancreas_sub,
  reduction = "umap",
  group.by = "db.scds_hybrid_class"
)


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