Find the default reduction name in a Seurat object

Usage

DefaultReduction(srt, pattern = NULL, min_dim = 2, max_distance = 0.1)

Arguments

srt: A Seurat object.
pattern: Character string containing a regular expression to search for.
min_dim: Minimum dimension threshold.
max_distance: Maximum distance allowed for a match.

Value

Default reduction name.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-03-20 08:18:42] Start standard scop workflow...
#> ℹ [2026-03-20 08:18:43] Checking a list of <Seurat>...
#> ! [2026-03-20 08:18:43] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-03-20 08:18:43] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-03-20 08:18:45] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-03-20 08:18:45] Use the separate HVF from `srt_list`
#> ℹ [2026-03-20 08:18:45] Number of available HVF: 2000
#> ℹ [2026-03-20 08:18:46] Finished check
#> ℹ [2026-03-20 08:18:46] Perform `Seurat::ScaleData()`
#> ℹ [2026-03-20 08:18:46] Perform pca linear dimension reduction
#> ℹ [2026-03-20 08:18:47] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-03-20 08:18:47] Reorder clusters...
#> ℹ [2026-03-20 08:18:47] Perform umap nonlinear dimension reduction
#> ℹ [2026-03-20 08:18:47] Perform umap nonlinear dimension reduction using Standardpca (1:50)
#> ℹ [2026-03-20 08:18:50] Perform umap nonlinear dimension reduction using Standardpca (1:50)
#> ✔ [2026-03-20 08:18:53] Run scop standard workflow completed
names(pancreas_sub@reductions)
#> [1] "Standardpca"       "StandardpcaUMAP2D" "StandardpcaUMAP3D"
#> [4] "StandardUMAP2D"    "StandardUMAP3D"   

DefaultReduction(pancreas_sub)
#> [1] "StandardUMAP2D"

DefaultReduction(pancreas_sub, pattern = "pca")
#> [1] "Standardpca"

DefaultReduction(pancreas_sub, pattern = "umap")
#> [1] "StandardUMAP2D"