Run semla radial distance analysis

Use semla::RadialDistance() to calculate distances from selected spatial regions and write the returned columns to Seurat metadata. SCOP provides no dedicated plot for this result; retrieve its schema record with GetSpatialResult() and inspect the recorded metadata columns.

Usage

RunSemlaRadialDistance(
  srt,
  column_name,
  selected_groups = NULL,
  column_suffix = NULL,
  image_type = "tissue_lowres",
  verbose = TRUE,
  ...
)

Arguments

srt

A Seurat object with spatial image data.

column_name

Metadata column containing region labels.

selected_groups

Region labels used by semla. If NULL, semla uses all labels in column_name.

column_suffix

Optional suffix for metadata columns returned by semla.

image_type

Image scale used by semla::UpdateSeuratForSemla() when the object does not already contain a Staffli object.

verbose

Whether to print the message. Default is TRUE.

...

Additional arguments passed to semla.

Value

A Seurat object.

Examples

data(visium_human_pancreas_sub)
spatial <- visium_human_pancreas_sub
spatial$region <- ifelse(
  spatial$y > stats::median(spatial$y),
  "upper",
  "lower"
)
upper_center <- c(
  stats::median(spatial$x[spatial$region == "upper"]),
  stats::median(spatial$y[spatial$region == "upper"])
)
spatial$upper_distance <- sqrt(
  (spatial$x - upper_center[1])^2 + (spatial$y - upper_center[2])^2
)

SpatialSpotPlot(
  spatial,
  group.by = "upper_distance",
  overlay_image = FALSE,
  coord.cols = c("x", "y")
)


spatial <- RunSemlaRadialDistance(
  spatial,
  column_name = "region",
  selected_groups = "upper",
  column_suffix = "upper_distance",
  verbose = FALSE
)
#> ℹ Found VisiumV2 object(s).
#> 
#> ── Collecting data from @images slot 
#> ! Array coordinates are not available for non-VisiumHD datasets. Please consider using semla's own functions to load the data. See '?ReadVisiumData()'