FeatureScatter: Scatter plot of single cell data

In ibseq/scs-analysis: Tools for Single Cell Genomics

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,
  group.by = NULL,
  cols = NULL,
  pt.size = 1,
  shape.by = NULL,
  span = NULL,
  smooth = FALSE,
  combine = TRUE,
  slot = "data",
  plot.cor = TRUE,
  raster = NULL
)

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.
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

Value

A ggplot object

Examples

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

ibseq/scs-analysis documentation built on Feb. 27, 2021, 12:35 a.m.