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, shuffle = FALSE, seed = 1, group.by = NULL, split.by = NULL, cols = NULL, pt.size = 1, shape.by = NULL, span = NULL, smooth = FALSE, combine = TRUE, slot = "data", plot.cor = TRUE, ncol = NULL, raster = NULL, raster.dpi = c(512, 512), jitter = FALSE )
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.
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)
Sets the seed if randomly shuffling the order of points.
Name of one or more metadata columns to group (color) cells by (for example, orig.ident); pass 'ident' to group by identity class
A factor in object metadata to split the feature plot by, pass 'ident' to split by cell identity'
Colors to use for identity class plotting.
Size of the points on the plot
Ignored for now
Spline span in loess function call, if
NULL, no spline added
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
Number of columns if plotting multiple plots
Convert points to raster format, default is
which will automatically use raster if the number of points plotted is greater than
Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512).
Jitter for easier visualization of crowded points (default is FALSE)
A ggplot object
data("pbmc_small") FeatureScatter(object = pbmc_small, feature1 = 'CD9', feature2 = 'CD3E')