3D-Dimensional reduction plot for cell classification visualization.

Plotting cell points on a reduced 3D space and coloring according to the groups of the cells.

Usage

CellDimPlot3D(
  srt,
  group.by,
  reduction = NULL,
  dims = c(1, 2, 3),
  axis_labs = NULL,
  palette = "Paired",
  palcolor = NULL,
  bg_color = "grey80",
  pt.size = 1.5,
  cells.highlight = NULL,
  cols.highlight = "black",
  shape.highlight = "circle-open",
  sizes.highlight = 2,
  lineages = NULL,
  lineages_palette = "Dark2",
  span = 0.75,
  width = NULL,
  height = NULL,
  save = NULL,
  force = FALSE
)

Arguments

srt

A Seurat object.

group.by

Name of one or more meta.data columns to group (color) cells by (for example, orig.ident).

reduction

Which dimensionality reduction to use. If not specified, will use the reduction returned by DefaultReduction.

dims

Dimensions to plot, must be a three-length numeric vector specifying x-, y- and z-dimensions

axis_labs

A character vector of length 3 indicating the labels for the axes.

palette

Name of a color palette name collected in SCP. Default is "Paired".

palcolor

Custom colors used to create a color palette.

bg_color

Color value for background(NA) points.

pt.size

Point size.

cells.highlight

A vector of cell names to highlight.

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.

lineages

Lineages/pseudotime to add to the plot. If specified, curves will be fitted using loess method.

lineages_palette

Color palette used for lineages.

span

A numeric value specifying the span of the loess smoother for lineages line.

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 maximum levels in any cell group is greater than 100.

See also

CellDimPlot FeatureDimPlot3D

Examples

data("pancreas_sub")
pancreas_sub <- Standard_SCP(pancreas_sub)
#> [2023-11-21 07:10:31.757817] Start Standard_SCP
#> [2023-11-21 07:10:31.75878] Checking srtList... ...
#> Data 1/1 of the srtList is raw_counts. Perform NormalizeData(LogNormalize) on the data ...
#> Perform FindVariableFeatures on the data 1/1 of the srtList...
#> Use the separate HVF from srtList...
#> Number of available HVF: 2000
#> [2023-11-21 07:10:32.453668] Finished checking.
#> [2023-11-21 07:10:32.453853] Perform ScaleData on the data...
#> [2023-11-21 07:10:32.526244] Perform linear dimension reduction (pca) on the data...
#> Warning: The following arguments are not used: force.recalc
#> Warning: The following arguments are not used: force.recalc
#> [2023-11-21 07:10:33.084557] Perform FindClusters (louvain) on the data...
#> [2023-11-21 07:10:33.157226] Reorder clusters...
#> [2023-11-21 07:10:33.222341] Perform nonlinear dimension reduction (umap) on the data...
#> Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims:1-13) as input
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Non-linear dimensionality reduction(umap) using Reduction(Standardpca, dims:1-13) as input
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by ‘spam’
#> [2023-11-21 07:10:39.768675] Standard_SCP done
#> Elapsed time: 8.01 secs 
CellDimPlot3D(pancreas_sub, group.by = "SubCellType", reduction = "StandardpcaUMAP3D")


pancreas_sub <- RunSlingshot(pancreas_sub, group.by = "SubCellType", reduction = "StandardpcaUMAP3D")
CellDimPlot3D(pancreas_sub, group.by = "SubCellType", reduction = "StandardpcaUMAP3D", lineages = "Lineage1")