Plot spatial gradient screening results

Visualize normalized results produced by RunSpatialGradientFeatures(). The plot reads only the stored screening, summary, and model-fit tables; it does not refetch expression from the object's current assay or layer. The model view displays the stored model-matching error (RMSE) for each feature.

Usage

SpatialGradientPlot(
  object,
  result_name = NULL,
  plot_type = c("summary", "line", "model"),
  features = NULL,
  nfeatures = 4,
  palette = "Spectral",
  palcolor = NULL,
  legend.position = "right",
  theme_use = "theme_scop",
  theme_args = list(),
  line_size = 1,
  line_alpha = 0.35,
  line_fit = c("stored", "lm"),
  nrow = NULL,
  ncol = NULL,
  srt = NULL
)

Arguments

object

A Seurat object containing stored gradient results.

result_name

Stored spatial gradient result name. If NULL, the latest stored result is used.

plot_type

Plot type: "summary", "line", or "model".

features

Variables to plot. If NULL, top variables from the stored result are used.

nfeatures

Number of top variables used when features = NULL.

palette, palcolor

Color palette passed to SCOP plotting helpers.

legend.position

Legend position for line, summary, and model plots.

theme_use

Theme name or function.

theme_args

Additional theme arguments.

line_size

Size of fitted gradient lines.

line_alpha

Alpha for raw value points.

line_fit

Gradient line source. "stored" uses the saved screening$estimate values produced by the selected backend. "lm" draws a fresh linear fit from screening$value, which is useful for showing a simple monotonic trend even when the backend stores a smoothed curve.

nrow, ncol

Layout controls for multi-feature plots.

srt

Deprecated alias for object; supply exactly one of the two. It will be removed in scop 1.0.0.

Value

A ggplot or patchwork object.

See also

RunSpatialGradientFeatures()

Examples

data(visium_human_pancreas_sub)
spatial <- visium_human_pancreas_sub
counts <- GetAssayData5(spatial, assay = "Spatial", layer = "counts")
gradient_features <- names(sort(Matrix::rowSums(counts), decreasing = TRUE))[
  seq_len(min(8L, nrow(counts)))
]
# Use a diagonal example axis without permutation testing.
spatial <- RunSpatialGradientFeatures(
  spatial,
  reference = "trajectory",
  backend = "cpp",
  result_name = "example_axis",
  variables = gradient_features,
  start = c(min(spatial$x), min(spatial$y)),
  end = c(max(spatial$x), max(spatial$y)),
  layer = "counts",
  coord.cols = c("x", "y"),
  n_random = 0,
  n_bins = 5,
  min_spots = 3,
  sign_threshold = 1,
  nfeatures = 4,
  verbose = FALSE
)

SpatialGradientPlot(spatial, plot_type = "line")

SpatialGradientPlot(spatial, plot_type = "model")