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

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

Usage

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

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".

...

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 [2026-01-27 08:08:22] Start standard scop workflow...
#> ℹ [2026-01-27 08:08:23] Checking a list of <Seurat>...
#> ! [2026-01-27 08:08:23] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 08:08:23] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:08:25] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 08:08:25] Use the separate HVF from srt_list
#> ℹ [2026-01-27 08:08:26] Number of available HVF: 2000
#> ℹ [2026-01-27 08:08:26] Finished check
#> ℹ [2026-01-27 08:08:26] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 08:08:26] Perform pca linear dimension reduction
#> ℹ [2026-01-27 08:08:27] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 08:08:27] Reorder clusters...
#> ℹ [2026-01-27 08:08:27] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 08:08:27] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 08:08:32] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 08:08:37] Run scop standard workflow completed
pancreas_sub <- RunDoubletCalling(
  pancreas_sub,
  db_method = "scDblFinder"
)
#> ℹ [2026-01-27 08:08:37] Data type is raw counts
#> ℹ [2026-01-27 08:08:37] Data type is raw counts
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