RunSlingshot

RunSlingshot

Usage

RunSlingshot(
  srt,
  group.by,
  reduction = NULL,
  dims = NULL,
  start = NULL,
  end = NULL,
  prefix = NULL,
  reverse = FALSE,
  align_start = FALSE,
  show_plot = TRUE,
  lineage_palette = "Dark2",
  seed = 11,
  ...
)

Arguments

srt: A Seurat object.
group.by: Name of one or more meta.data columns to group (color) cells by.
reduction: Which dimensionality reduction to use. If not specified, will use the reduction returned by DefaultReduction.
dims: The dimensions to use for the Slingshot algorithm. Default is NULL, which uses first two dimensions.
start: The starting group for the Slingshot algorithm. Default is NULL.
end: The ending group for the Slingshot algorithm. Default is NULL.
prefix: The prefix to add to the column names of the resulting pseudotime variable. Default is NULL.
reverse: Logical value indicating whether to reverse the pseudotime variable. Default is FALSE.
align_start: Logical value indicating whether to align the starting pseudotime values at the maximum pseudotime. Default is FALSE.
show_plot: Logical value indicating whether to show the dimensionality plot. Default is TRUE.
lineage_palette: The color palette to use for the lineages in the plot. Default is "Dark2".
seed: Random seed for reproducibility. Default is 11.
...: Additional arguments to be passed to the slingshot::slingshot function.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-04-26 02:29:25] Start standard processing workflow...
#> ℹ [2026-04-26 02:29:25] Checking a list of <Seurat>...
#> ! [2026-04-26 02:29:26] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-04-26 02:29:26] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:29:28] Perform `Seurat::FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-04-26 02:29:29] Use the separate HVF from `srt_list`
#> ℹ [2026-04-26 02:29:29] Number of available HVF: 2000
#> ℹ [2026-04-26 02:29:29] Finished check
#> ℹ [2026-04-26 02:29:29] Perform `Seurat::ScaleData()`
#> ℹ [2026-04-26 02:29:30] Perform pca linear dimension reduction
#> ℹ [2026-04-26 02:29:30] Use stored estimated dimensions 1:20 for Standardpca
#> ℹ [2026-04-26 02:29:31] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-04-26 02:29:31] Reorder clusters...
#> ℹ [2026-04-26 02:29:31] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-04-26 02:29:31] Perform umap nonlinear dimension reduction
#> ℹ [2026-04-26 02:29:31] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ℹ [2026-04-26 02:29:37] Perform umap nonlinear dimension reduction using Standardpca (1:20)
#> ✔ [2026-04-26 02:29:42] Standard processing workflow completed
pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP"
)
#> Warning: Removed 9 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 9 rows containing missing values or values outside the scale range
#> (`geom_path()`).

pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "PCA"
)
#> Warning: Removed 9 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 9 rows containing missing values or values outside the scale range
#> (`geom_path()`).

CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  lineages = paste0("Lineage", 1:2),
  lineages_span = 0.1
)


# 3D lineage
pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "StandardpcaUMAP3D"
)
CellDimPlot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP",
  lineages = paste0("Lineage", 1:2),
  lineages_span = 0.1,
  lineages_trim = c(0.05, 0.95)
)

Usage

Arguments

See also

Examples