Feature values on a 3D reduction

Usage

FeatureDimPlot3D(
  srt,
  features,
  reduction = NULL,
  dims = c(1, 2, 3),
  axis_labs = NULL,
  split.by = NULL,
  layer = "data",
  assay = NULL,
  calculate_coexp = FALSE,
  pt.size = 1.5,
  cells.highlight = NULL,
  cols.highlight = "black",
  shape.highlight = "circle-open",
  sizes.highlight = 2,
  width = NULL,
  height = NULL,
  save = NULL,
  force = FALSE,
  verbose = TRUE
)

Arguments

srt: A Seurat object.
features: Features to plot: a character vector or a named list of assay gene names or numeric metadata columns.
reduction: Dimensionality reduction to use. NULL uses DefaultReduction.
dims: Length-2 vector of dimensions to plot.
axis_labs: Length-3 axis labels.
split.by: Metadata column to split the analysis or plot by.
layer: Assay layer to use.
assay: Assay to use. NULL uses the default assay.
calculate_coexp: Plot the geometric mean of the features.
pt.size: Point size and transparency. pt.size = NULL scales with sqrt(n) (minimum 0.3). Rasterized points keep at least a two-pixel radius at raster.dpi = c(512, 512) and scale with raster.dpi.
cells.highlight: Cells to highlight and their appearance. TRUE highlights all cells.
cols.highlight: Cells to highlight and their appearance. TRUE highlights all cells.
shape.highlight: Shape of the cell to highlight. See scattergl-marker-symbol
sizes.highlight: Cells to highlight and their appearance. TRUE highlights all cells.
width: Width in pixels, defaults to automatic sizing.
height: Height in pixels, defaults to automatic sizing.
save: The name of the file to save the plot to. Must end in ".html".
force: Draw even when more than 100 features are requested.
verbose: Whether to print messages.

Examples

data(pancreas_sub)
pancreas_sub <- RunStandardWorkflow(pancreas_sub)
#> ℹ [2026-08-30 04:27:28] Start standard processing workflow...
#> ℹ [2026-08-30 04:27:28] Checking a list of <Seurat>...
#> ! [2026-08-30 04:27:28] Data 1/1 of the `srt_list` is "unknown"
#> Warning: Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-08-30 04:27:28] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:27:28] Perform `FindVariableFeatures()` on 1/1 of `srt_list`...
#> ℹ [2026-08-30 04:27:28] Use the separate HVF from `srt_list`
#> ℹ [2026-08-30 04:27:28] Number of available HVF: 2000
#> ℹ [2026-08-30 04:27:28] Finished check
#> ℹ [2026-08-30 04:27:28] Perform `ScaleData()`
#> ℹ [2026-08-30 04:27:28] Perform pca linear dimension reduction
#> ℹ [2026-08-30 04:27:29] Use stored estimated dimensions 1:23 for Standardpca
#> ℹ [2026-08-30 04:27:29] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-08-30 04:27:29] Reorder clusters...
#> ℹ [2026-08-30 04:27:29] Skip `log1p()` because `layer = data` is not "counts"
#> ℹ [2026-08-30 04:27:29] Perform umap nonlinear dimension reduction
#> ✔ [2026-08-30 04:27:36] Standard processing workflow completed
FeatureDimPlot3D(
  pancreas_sub,
  features = c("Ghrl", "Ins1", "Gcg", "Ins2"),
  reduction = "StandardpcaUMAP3D"
)

Feature values on a 3D reduction

Usage

Arguments

See also

Examples