Plot a single feature overlaid on a dimensionality reduction.

One point per cell, colored by expression/abundance of feature (a gene, antibody, or any other value FeaturePlot() can fetch). Unlike passing a multi-color ramp straight to FeaturePlot(cols = ), the ramp is applied afterward with scale_color_gradientn() so the color scale stays continuous - see the "cols" note below.

Usage

plot_dr_feature(
  x,
  feature,
  assay = NULL,
  reduction = "umap",
  cells = NULL,
  color_scheme = NULL,
  pt_size = NULL
)

Arguments

x

Seurat object.

feature

Feature name (gene, antibody, or metadata column) to plot.

assay

(optional) Assay to fetch feature from. NULL uses DefaultAssay(x).

reduction

Dimensionality reduction to plot on (e.g. "umap", "tsne", "pca").

cells

(optional) Cells to plot, in the given order (controls z-order of overlapping points). Defaults to a random shuffle of all cells so no single cell/group dominates due to plot order.

color_scheme

(optional) Vector of colors for the low-to-high expression gradient.

pt_size

(optional) Point size. Defaults to get_dr_point_size(x).

Value

A ggplot object.

Seurat's FeaturePlot(cols = ) quirk

As of Seurat 5, passing a cols vector of anything other than exactly 2 colors makes FeaturePlot() bin the continuous values into 2 groups before coloring (cut(x, breaks = 2)), which turns a smooth expression gradient into a flat low/high split. Seurat 4 only did this when cols had exactly 2 colors, so a long custom ramp used to pass through untouched. This function sidesteps the quirk (and the version difference) entirely by never passing a multi-color cols to FeaturePlot() - the ramp is layered on afterward instead.