Run doublet-calling with scds
Arguments
- object
A
Seuratobject.- assay
Assay used for doublet calling.
- db_rate
Expected doublet rate.
- method
The method to be used for doublet-calling. Options are
"hybrid","cxds", or"bcds".- data_type
Optional CheckDataType result, used internally to avoid rescanning the count matrix.
- ...
Additional arguments passed to the selected
scdsmethod.- verbose
Whether to print messages.
- srt
Deprecated alias for
object; supply exactly one of the two. It will be removed in scop 1.0.0.
Examples
data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-10-11 23:57:18] Start standard processing workflow...
#> ℹ [2026-10-11 23:57:18] Checking a list of <Seurat>...
#> ! [2026-10-11 23:57:18] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-10-11 23:57:18] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:57:18] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-10-11 23:57:18] Use the separate HVF from `srt_list`
#> ℹ [2026-10-11 23:57:18] Number of available HVF: 2000
#> ℹ [2026-10-11 23:57:18] Finished check
#> ℹ [2026-10-11 23:57:18] Perform `ScaleData()`
#> ℹ [2026-10-11 23:57:18] Perform pca linear dimension reduction
#> ℹ [2026-10-11 23:57:19] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-10-11 23:57:19] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-10-11 23:57:19] Reorder clusters...
#> ℹ [2026-10-11 23:57:19] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-10-11 23:57:19] Perform umap nonlinear dimension reduction
#> ✔ [2026-10-11 23:57:27] Standard processing workflow completed
pancreas_sub <- Runscds(pancreas_sub, method = "hybrid")
#> ℹ [2026-10-11 23:57:27] Running scds with method "hybrid"
#> ℹ [2026-10-11 23:57:28] 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"
)