3D-Dimensional reduction plot for gene expression visualization.

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
)

Arguments

srt: A Seurat object.
features: A character vector or a named list of features to plot. Features can be gene names in Assay or names of numeric columns in meta.data.
reduction: Which dimensionality reduction to use. If not specified, will use the reduction returned by DefaultReduction.
dims: Dimensions to plot, must be a two-length numeric vector specifying x- and y-dimensions
axis_labs: A character vector of length 3 indicating the labels for the axes.
split.by: Name of a column in meta.data column to split plot by. Default is NULL.
layer: Which layer to use. Default is data.
assay: Which assay to use. If NULL, the default assay of the Seurat object will be used.
calculate_coexp: Whether to calculate the co-expression value (geometric mean) of the features.
pt.size: The size of the points in the plot.
cells.highlight: A logical or character vector specifying the cells to highlight in the plot. If TRUE, all cells are highlighted. If FALSE, no cells are highlighted. Default is NULL.
cols.highlight: Color used to highlight the cells.
shape.highlight: Shape of the cell to highlight. See scattergl-marker-symbol
sizes.highlight: Size of highlighted cell points.
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: Whether to force drawing regardless of the number of features greater than 100. Default is FALSE.

Examples

data(pancreas_sub)
pancreas_sub <- standard_scop(pancreas_sub)
#> ℹ [2026-01-27 07:46:22] Start standard scop workflow...
#> ℹ [2026-01-27 07:46:22] Checking a list of <Seurat>...
#> ! [2026-01-27 07:46:22] Data 1/1 of the `srt_list` is "unknown"
#> ℹ [2026-01-27 07:46:22] Perform `NormalizeData()` with `normalization.method = 'LogNormalize'` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 07:46:24] Perform `Seurat::FindVariableFeatures()` on the data 1/1 of the `srt_list`...
#> ℹ [2026-01-27 07:46:25] Use the separate HVF from srt_list
#> ℹ [2026-01-27 07:46:25] Number of available HVF: 2000
#> ℹ [2026-01-27 07:46:25] Finished check
#> ℹ [2026-01-27 07:46:25] Perform `Seurat::ScaleData()`
#> ℹ [2026-01-27 07:46:26] Perform pca linear dimension reduction
#> ℹ [2026-01-27 07:46:27] Perform `Seurat::FindClusters()` with `cluster_algorithm = 'louvain'` and `cluster_resolution = 0.6`
#> ℹ [2026-01-27 07:46:27] Reorder clusters...
#> ℹ [2026-01-27 07:46:27] Perform umap nonlinear dimension reduction
#> ℹ [2026-01-27 07:46:27] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ℹ [2026-01-27 07:46:30] Non-linear dimensionality reduction (umap) using (Standardpca) dims (1-50) as input
#> ✔ [2026-01-27 07:46:34] Run scop standard workflow completed
FeatureDimPlot3D(
  pancreas_sub,
  features = c("Ghrl", "Ins1", "Gcg", "Ins2"),
  reduction = "StandardpcaUMAP3D"
)

3D-Dimensional reduction plot for gene expression visualization.

Usage

Arguments

See also

Examples