Run spatial neighborhood statistics — RunSpatialNeighborhood • scop

Build a scop-style spatial neighborhood result bundle and optionally dispatch to a supported backend for colocalization or local-effect statistics.

Usage

RunSpatialNeighborhood(
  srt,
  group.by,
  method = "spicyR",
  assay = NULL,
  layer = "data",
  coord.cols = c("col", "row"),
  image = NULL,
  sample.by = NULL,
  split.by = NULL,
  subject.by = NULL,
  radius = NULL,
  k = NULL,
  features = NULL,
  from = NULL,
  to = NULL,
  tool_name = "SpatialNeighborhood",
  store_results = TRUE,
  verbose = TRUE,
  ...
)

Arguments

srt: A Seurat object.
group.by: Metadata column containing spatial cell or spot labels.
method: Neighborhood backend. Currently only "spicyR" is supported.
assay: Assay used when features are requested.
layer: Assay layer used when features are requested.
coord.cols: Metadata coordinate columns used when no Seurat image coordinates are available.
image: Name of the Seurat spatial image. If NULL, the first image is used when present.
sample.by: Metadata column identifying images or samples. If NULL, all spots are treated as one sample.
split.by: Optional metadata column identifying conditions for differential neighborhood statistics.
subject.by: Optional metadata column identifying subjects for backends that support paired or repeated designs.
radius: Optional spatial radius used for scop-native neighborhood summaries.
k: Optional number of nearest neighbors used for scop-native neighborhood summaries. When both radius and k are NULL, k = 6 is used.
features: Optional features to extract into the backend input table.
from, to: Optional cell or spot label filters.
tool_name: Name used to store results in srt@tools.
store_results: Whether to store results in srt@tools.
verbose: Whether to print the message. Default is TRUE.
...: Additional arguments passed to the selected backend.

Value

A Seurat object with results stored in srt@tools[[tool_name]].

Examples

data(visium_human_pancreas_sub)
spatial <- visium_human_pancreas_sub
spatial <- RunSpatialNeighborhood(
  spatial,
  group.by = "coda_label",
  coord.cols = c("x", "y"),
  k = 4,
  verbose = FALSE
)

SpatialNeighborhoodPlot(spatial, plot_type = "heatmap")

SpatialNeighborhoodPlot(spatial, plot_type = "network", top_n = 12)
#> Error in calcCurveGrob(x, x$debug): end points must not be identical
SpatialNeighborhoodPlot(spatial, plot_type = "stat", top_n = 12)

SpatialNeighborhoodPlot(
  spatial,
  plot_type = "spatial",
  overlay_image = FALSE,
  coord.cols = c("x", "y")
)