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Set the cell plot node size scale

cell_node_scale_size.Rd

Sets the output sizes for a column mapped with cell_plot(size = ...). Numeric and integer columns are scaled continuously. Character and factor columns are treated as categorical levels.

Usage

cell_node_scale_size(object, sizes = c(2, 6), limits = NULL)

Arguments

object

A cell_plot recipe.

sizes

Non-negative relative sizes. Supply two ordered values for a continuous or interpolated categorical range, or a named vector of per-level values when the mapped column is categorical.

limits

Optional scale limits for a ranged mapping. Supply two ordered numeric values. Named categorical sizes cannot be combined with limits.

Value

A modified cell_plot recipe.

Details

A length-2 sizes vector is an output range. build_cell_plot() normalizes continuous values, and interpolated categorical levels, to that interval. Rows with missing values in a mapped size column are dropped at build time with a warning. A named sizes vector assigns one relative size to each category, for example sizes = c(a = 1, b = 2, c = 3). Named values must cover every observed level and are not interpolated. limits belong to the ranged scale and cannot be combined with named sizes.

Sizes are backend-neutral relative units. Each renderer converts the values to native units: ggplot2 uses millimetres, Plotly and rgl use marker diameters in pixels, and base R uses cex. The contract guarantees consistent ordering and relative range, not pixel-perfect physical equality.

This modifier requires a size mapping and is optional; the default output range is c(2, 6). One node size shared by every point is set with cell_plot(size = <number>) instead. Because cell_node_depth() scales one node size by depth, it cannot be combined with this modifier.

For projected 3D data, ggplot2 applies perspective sizing from the depth mapping only when no size column is mapped. A size mapping always wins. Plotly and rgl ignore depth.

See also

cell_plot(), cell_node_depth()

Other cell-plot-modifiers: cell_annotation(), cell_grid(), cell_illuminate(), cell_node_depth(), cell_node_scale_alpha(), cell_node_scale_color(), cell_theme()

Examples

se <- ReadPNA_Seurat(minimal_pna_pxl_file())
#> ✔ Created a <Seurat> object with 5 cells and 158 targeted surface proteins
se <- LoadCellGraphs(se, cells = colnames(se)[4], verbose = FALSE) |>
  ComputeLayout(layout_method = "spectral")
#> ℹ Computing layouts for 1 graphs

cell_graph <- CellGraphs(se)[[4]]

layout_data <- FetchLayoutData(cell_graph, vars = "CD82", layout_method = "spectral_3d") |>
  # Downsample to speed up tests
  dplyr::slice_sample(n = 5000)

cell_plot(layout_data, size = CD82) |>
  cell_node_scale_size(sizes = c(0.1, 3))


# One size per category
layout_data |>
  dplyr::mutate(group = dplyr::if_else(CD82 > median(CD82), "high", "low")) |>
  cell_plot(size = group) |>
  cell_node_scale_size(sizes = c(low = 1, high = 4))