Run spatial variable feature detection — RunSpatialVariableFeatures • scop

Score genes by spot-level spatial autocorrelation. The native "moran" and "geary" methods use a lightweight coordinate KNN graph. "SPARKX" and "nnSVG" use optional external backends when their packages are installed.

Usage

RunSpatialVariableFeatures(
  srt,
  assay = NULL,
  layer = "data",
  features = NULL,
  method = c("moran", "geary", "SPARKX", "nnSVG"),
  image = NULL,
  coord.cols = c("x", "y"),
  k = 6,
  nfeatures = 2000,
  min_spots = 5,
  nperm = 0,
  set_variable_features = TRUE,
  store_results = TRUE,
  verbose = TRUE,
  seed = 11,
  ...
)

Arguments

srt: A Seurat object.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used. When the object also contains ChromatinAssay, the default assay and additional ChromatinAssay will be preprocessed sequentially.
layer: Assay layer used for expression values.
features: Features to score. If NULL, current variable features are used; if no variable features are present, all assay features are used.
method: Spatial variable feature detection method.
image: Name of the Seurat spatial image used by the spatial workflow. If NULL, the first image is used when present.
coord.cols: Metadata coordinate columns used by the spatial workflow when no image is available.
k: Number of nearest spatial neighbors per spot.
nfeatures: Number of top spatial features stored in srt@misc[["SpatialVariableFeatures"]].
min_spots: Minimum number of spots with non-zero expression required for a feature to be tested.
nperm: Number of label permutations used for empirical p values. The default 0 skips p-value calculation.
set_variable_features: Whether to set the top spatial features as variable features for assay.
store_results: Whether to store the full result in srt@tools.
verbose: Whether to print the message. Default is TRUE.
seed: Random seed used for permutation tests.
...: Additional arguments passed to external backends.

Value

A Seurat object with spatial variable feature results stored in srt@tools[["SpatialVariableFeatures"]] and top feature names stored in srt@misc[["SpatialVariableFeatures"]].

Examples

data(visium_human_pancreas_sub)
spatial <- Seurat::NormalizeData(
  visium_human_pancreas_sub,
  assay = "Spatial",
  verbose = FALSE
)
spatial <- Seurat::FindVariableFeatures(
  spatial,
  assay = "Spatial",
  nfeatures = 100,
  verbose = FALSE
)

SpatialSpotPlot(
  spatial,
  features = Seurat::VariableFeatures(spatial, assay = "Spatial")[1:2]
)


spatial <- RunSpatialVariableFeatures(
  spatial,
  assay = "Spatial",
  nfeatures = 50
)
#> ◌ [2026-07-02 10:09:05] Running spatial variable feature detection
#> ✔ [2026-07-02 10:09:05] Stored 50 spatial variable features
SpatialVariableFeaturePlot(spatial, plot_type = "combined", nfeatures = 2)