plot_mean_variance {scFeatureFilter} | R Documentation |
Use the output of calculate_cvs
or bin_scdata
and plot a feature
mean expression x coefficient of variation scatter plot. Mean expression is represented as
log10(mean + 1)
. Each dot represents a feature.
Means and coefficient of variations were obtained across single cells.
Optionally, colours each dot according to the defined bins of features.
Optionally, adds a density2d geom.
plot_mean_variance(df, density = TRUE, colourByBin = TRUE, density_color = "blue", ...)
df |
A |
density |
A boolean. Should a |
colourByBin |
A boolean. Should feature be coloured by bin? Need a |
density_color |
Colour of the density2d curves. |
... |
Further arguments are passed to |
A ggplot2 plot.
library(magrittr) scData_hESC %>% calculate_cvs %>% plot_mean_variance(colourByBin = FALSE) scData_hESC %>% calculate_cvs %>% define_top_genes(window_size = 100) %>% bin_scdata(window_size = 1000) %>% plot_mean_variance