FeatureScatter_scCustom {scCustomize} | R Documentation |
Modified version of FeatureScatter
Description
Create customized FeatureScatter plots with scCustomize defaults.
Usage
FeatureScatter_scCustom(
seurat_object,
feature1 = NULL,
feature2 = NULL,
colors_use = NULL,
pt.size = NULL,
group.by = NULL,
split.by = NULL,
split_seurat = FALSE,
shuffle = TRUE,
aspect_ratio = NULL,
title_size = 15,
plot.cor = TRUE,
num_columns = NULL,
raster = NULL,
raster.dpi = c(512, 512),
ggplot_default_colors = FALSE,
color_seed = 123,
...
)
Arguments
seurat_object |
Seurat object name. |
feature1 |
First feature to plot. |
feature2 |
Second feature to plot. |
colors_use |
color for the points on plot. |
pt.size |
Adjust point size for plotting. |
group.by |
Name of one or more metadata columns to group (color) cells by (for example, orig.ident). Default is active ident. |
split.by |
Feature to split plots by (i.e. "orig.ident"). |
split_seurat |
logical. Whether or not to display split plots like Seurat (shared y axis) or as individual plots in layout. Default is FALSE. |
shuffle |
logical, whether to randomly shuffle the order of points. This can be useful for crowded plots if points of interest are being buried. Default is TRUE. |
aspect_ratio |
Control the aspect ratio (y:x axes ratio length). Must be numeric value; Default is NULL. |
title_size |
size for plot title labels. Does NOT apply if |
plot.cor |
Display correlation in plot subtitle (or title if |
num_columns |
number of columns in final layout plot. |
raster |
Convert points to raster format. Default is NULL which will rasterize by default if greater than 200,000 cells. |
raster.dpi |
Pixel resolution for rasterized plots, passed to geom_scattermore(). Default is c(512, 512). |
ggplot_default_colors |
logical. If |
color_seed |
random seed for the "varibow" palette shuffle if |
... |
Extra parameters passed to |
Value
A ggplot object
Examples
library(Seurat)
pbmc_small$sample_id <- sample(c("sample1", "sample2"), size = ncol(pbmc_small), replace = TRUE)
FeatureScatter_scCustom(seurat_object = pbmc_small, feature1 = "nCount_RNA",
feature2 = "nFeature_RNA", split.by = "sample_id")