Run VECTOR developmental direction inference

Usage

RunVECTOR(
  object,
  reduction = NULL,
  pca.reduction = "pca",
  dims = 1:2,
  pca.dims = 1:30,
  grid.n = 30,
  arrow.p = 0.9,
  arrow.ol = 1.5,
  score.name = "VECTOR_Score",
  tool_name = "VECTOR",
  verbose = TRUE
)

Arguments

object: A Seurat object or expression matrix with genes in rows and cells in columns.
reduction: Embedding reduction used for the direction field.
pca.reduction: PCA-like reduction used to score local polarization.
dims: Dimensions from reduction.
pca.dims: Dimensions from pca.reduction.
grid.n: Number of bins per embedding axis.
arrow.p: Distance decay parameter used by the original VECTOR arrow weighting. Larger values keep more distant grid centers influential.
arrow.ol: Arrow vector length multiplier relative to grid spacing.
score.name: Metadata column for the VECTOR score.
tool_name: Name used in srt@tools.
verbose: Whether to print progress messages.

Value

A modified Seurat object.

References

Zhang F, Li X, Tian W. Unsupervised inference of developmental directions for single cells using VECTOR. Cell Reports, 2020. doi:10.1016/j.celrep.2020.108069.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-07-28 03:33:45] Start standard processing workflow...
#> ℹ [2026-07-28 03:33:45] Checking a list of <Seurat>...
#> ! [2026-07-28 03:33:45] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-07-28 03:33:45] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-07-28 03:33:45] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-07-28 03:33:45] Use the separate HVF from `srt_list`
#> ℹ [2026-07-28 03:33:46] Number of available HVF: 2000
#> ℹ [2026-07-28 03:33:46] Finished check
#> ℹ [2026-07-28 03:33:46] Perform `ScaleData()`
#> ℹ [2026-07-28 03:33:46] Perform pca linear dimension reduction
#> ℹ [2026-07-28 03:33:46] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-07-28 03:33:47] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-07-28 03:33:47] Reorder clusters...
#> ℹ [2026-07-28 03:33:47] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-07-28 03:33:47] Perform umap nonlinear dimension reduction
#> ✔ [2026-07-28 03:33:52] Standard processing workflow completed
pancreas_sub <- RunVECTOR(
  pancreas_sub,
  reduction = "umap",
  pca.reduction = "pca",
  verbose = FALSE
)
FeatureDimPlot(pancreas_sub, features = "VECTOR_Score")


VECTORPlot(pancreas_sub, plot_type = "grid")


VECTORPlot(
  pancreas_sub,
  plot_type = "raw",
  group.by = "SubCellType",
  background = "none"
)