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.
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 )
First feature to plot. Typically feature expression but can also be metrics, PC scores, etc. - anything that can be retreived with FetchData
Second feature to plot.
Cells to include on the scatter plot.
Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class
Colors to use for identity class plotting.
Size of the points on the plot
Ignored for now
Spline span in loess function call, if
Smooth the graph (similar to smoothScatter)
Combine plots into a single
Slot to pull data from, should be one of 'counts', 'data', or 'scale.data'
Display correlation in plot title
Convert points to raster format, default is
A ggplot object
data("pbmc_small") FeatureScatter(object = pbmc_small, feature1 = 'CD9', feature2 = 'CD3E')