Seurat (version 4.3.0.1)

FeatureScatter: Scatter plot of single cell data

Description

Creates a scatter plot of two features (typically feature expression), across a set of single cells. Cells are colored by their identity class. Pearson correlation between the two features is displayed above the plot.

Usage

FeatureScatter(
  object,
  feature1,
  feature2,
  cells = NULL,
  shuffle = FALSE,
  seed = 1,
  group.by = NULL,
  cols = NULL,
  pt.size = 1,
  shape.by = NULL,
  span = NULL,
  smooth = FALSE,
  combine = TRUE,
  slot = "data",
  plot.cor = TRUE,
  raster = NULL,
  raster.dpi = c(512, 512),
  jitter = FALSE
)

Value

A ggplot object

Arguments

object

Seurat object

feature1

First feature to plot. Typically feature expression but can also be metrics, PC scores, etc. - anything that can be retreived with FetchData

feature2

Second feature to plot.

cells

Cells to include on the scatter plot.

shuffle

Whether to randomly shuffle the order of points. This can be useful for crowded plots if points of interest are being buried. (default is FALSE)

seed

Sets the seed if randomly shuffling the order of points.

group.by

Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class

cols

Colors to use for identity class plotting.

pt.size

Size of the points on the plot

shape.by

Ignored for now

span

Spline span in loess function call, if NULL, no spline added

smooth

Smooth the graph (similar to smoothScatter)

combine

Combine plots into a single patchworked

slot

Slot to pull data from, should be one of 'counts', 'data', or 'scale.data'

plot.cor

Display correlation in plot title

raster

Convert points to raster format, default is NULL which will automatically use raster if the number of points plotted is greater than 100,000

raster.dpi

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

jitter

Jitter for easier visualization of crowded points (default is FALSE)

Examples

data("pbmc_small")
FeatureScatter(object = pbmc_small, feature1 = 'CD9', feature2 = 'CD3E')