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,
  ...,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
group.by: Metadata column(s) used to color cells.
reduction: Reduction to plot. NULL uses 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.
end: The ending group for the Slingshot algorithm.
prefix: The prefix to add to the column names of the resulting pseudotime variable.
reverse: Whether to reverse the pseudotime variable.
align_start: Whether to align the starting pseudotime values at the maximum pseudotime.
show_plot: Whether to show the dimensionality plot.
lineage_palette: The color palette to use for the lineages in the plot.
seed: Random seed.
...: Passed to the slingshot::slingshot function.
verbose: Whether to print messages.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 05:37:57] Start standard processing workflow...
#> ℹ [2026-08-30 05:37:57] Checking a list of <Seurat>...
#> ! [2026-08-30 05:37:57] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 05:37:57] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:37:57] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 05:37:57] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 05:37:57] Number of available HVF: 2000
#> ℹ [2026-08-30 05:37:57] Finished check
#> ℹ [2026-08-30 05:37:57] Perform `ScaleData()`
#> ℹ [2026-08-30 05:37:57] Perform pca linear dimension reduction
#> ℹ [2026-08-30 05:37:58] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 05:37:58] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 05:37:58] Reorder clusters...
#> ℹ [2026-08-30 05:37:58] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 05:37:58] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 05:38:07] Standard processing workflow completed
pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "UMAP"
)
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_path()`).

pancreas_sub <- RunSlingshot(
  pancreas_sub,
  group.by = "SubCellType",
  reduction = "PCA"
)
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 4 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"
)
#> Warning: Removed 7 rows containing missing values or values outside the scale range
#> (`geom_path()`).
#> Warning: Removed 7 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,
  lineages_trim = c(0.05, 0.95)
)

Usage

Arguments

See also

Examples