Colors single cells on a dimensional reduction plot according to a 'feature' (i.e. gene expression, PC scores, number of genes detected, etc.)

  dims = c(1, 2),
  cells = NULL,
  cols = if (blend) {
     c("lightgrey", "#ff0000", "#00ff00")
 } else {
    c("lightgrey", "blue")
  pt.size = NULL,
  alpha = 1,
  order = FALSE,
  min.cutoff = NA,
  max.cutoff = NA,
  reduction = NULL, = NULL,
  keep.scale = "feature", = NULL,
  slot = "data",
  blend = FALSE,
  blend.threshold = 0.5,
  label = FALSE,
  label.size = 4,
  label.color = "black",
  repel = FALSE,
  ncol = NULL,
  coord.fixed = FALSE,
  by.col = TRUE,
  sort.cell = deprecated(),
  interactive = FALSE,
  combine = TRUE,
  raster = NULL,
  raster.dpi = c(512, 512)



Seurat object


Vector of features to plot. Features can come from:

  • An Assay feature (e.g. a gene name - "MS4A1")

  • A column name from (e.g. mitochondrial percentage - "percent.mito")

  • A column name from a DimReduc object corresponding to the cell embedding values (e.g. the PC 1 scores - "PC_1")


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


Vector of cells to plot (default is all cells)


The two colors to form the gradient over. Provide as string vector with the first color corresponding to low values, the second to high. Also accepts a Brewer color scale or vector of colors. Note: this will bin the data into number of colors provided. When blend is TRUE, takes anywhere from 1-3 colors:

1 color:

Treated as color for double-negatives, will use default colors 2 and 3 for per-feature expression

2 colors:

Treated as colors for per-feature expression, will use default color 1 for double-negatives

3+ colors:

First color used for double-negatives, colors 2 and 3 used for per-feature expression, all others ignored


Adjust point size for plotting


Alpha value for plotting (default is 1)


Boolean determining whether to plot cells in order of expression. Can be useful if cells expressing given feature are getting buried.

min.cutoff, max.cutoff

Vector of minimum and maximum cutoff values for each feature, may specify quantile in the form of 'q##' where '##' is the quantile (eg, 'q1', 'q10')


Which dimensionality reduction to use. If not specified, first searches for umap, then tsne, then pca

A factor in object metadata to split the plot by, pass 'ident' to split by cell identity'


How to handle the color scale across multiple plots. Options are:

  • “feature” (default; by row/feature scaling): The plots for each individual feature are scaled to the maximum expression of the feature across the conditions provided to

  • “all” (universal scaling): The plots for all features and conditions are scaled to the maximum expression value for the feature with the highest overall expression

  • all (no scaling): Each individual plot is scaled to the maximum expression value of the feature in the condition provided to Be aware setting NULL will result in color scales that are not comparable between plots

If NULL, all points are circles (default). You can specify any cell attribute (that can be pulled with FetchData) allowing for both different colors and different shapes on cells. Only applicable if raster = FALSE.


Which slot to pull expression data from?


Scale and blend expression values to visualize coexpression of two features


The color cutoff from weak signal to strong signal; ranges from 0 to 1.


Whether to label the clusters


Sets size of labels


Sets the color of the label text


Repel labels


Number of columns to combine multiple feature plots to, ignored if is not NULL


Plot cartesian coordinates with fixed aspect ratio


If splitting by a factor, plot the splits per column with the features as rows; ignored if blend = TRUE


Redundant with order. This argument is being deprecated. Please use order instead.


Launch an interactive FeaturePlot


Combine plots into a single patchworked ggplot object. If FALSE, return a list of ggplot objects


Convert points to raster format, default is NULL which automatically rasterizes if plotting more than 100,000 cells


Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512).


A patchworked ggplot object if combine = TRUE; otherwise, a list of ggplot objects


For the old do.hover and do.identify functionality, please see HoverLocator and CellSelector, respectively.


FeaturePlot(object = pbmc_small, features = 'PC_1')