plotPhenoData {scater} | R Documentation |
plotPhenoData
, plotColData
and plotCellData
are
synonymous.
plotPhenoData(object, aesth = aes_string(x = "log10(total_counts)", y = "total_features"), ...) plotColData(...) plotCellData(...)
object |
an |
aesth |
aesthetics function call to pass to ggplot. This function expects at least x and y variables to be supplied. The default is to plot total_features against log10(total_counts). |
... |
arguments passed to |
Plot phenotype data from a SingleCellExperiment object. If one variable is supplied then a density plot will be returned. If both variables are continuous (numeric) then a scatter plot will be returned. If one variable is discrete and one continuous then a violin plot with jittered points overlaid will be returned. If both variables are discrete then a jitter plot will be produced. The object returned is a ggplot object, so further layers and plotting options (titles, facets, themes etc) can be added.
a ggplot plot object
data("sc_example_counts") data("sc_example_cell_info") example_sce <- SingleCellExperiment( assays = list(counts = sc_example_counts), colData = sc_example_cell_info) example_sce <- calculateQCMetrics(example_sce) plotPhenoData(example_sce, aesth = aes_string(x = "log10(total_counts)", y = "total_features", colour = "Mutation_Status")) plotColData(example_sce, aesth = aes_string(x = "log10(total_counts)", y = "total_features", colour = "Mutation_Status")) plotCellData(example_sce, aesth = aes_string(x = "log10(total_counts)", y = "total_features", colour = "Mutation_Status"))