Creates a scatter plot of two features (typically gene 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.
GenePlot(object, gene1, gene2, cell.ids = NULL, col.use = NULL,
pch.use = 16, cex.use = 1.5, use.imputed = FALSE, use.scaled = FALSE,
use.raw = FALSE, do.hover = FALSE, data.hover = "ident",
do.identify = FALSE, dark.theme = FALSE, do.spline = FALSE,
spline.span = 0.75, ...)Seurat object
First feature to plot. Typically gene 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.
Colors to use for identity class plotting.
Pch argument for plotting
Cex argument for plotting
Use imputed values for gene expression (Default is FALSE)
Use scaled data
Use raw data
Enable hovering over points to view information
Data to add to the hover, pass a character vector of features to add. Defaults to cell name and ident. Pass 'NULL' to clear extra information.
Opens a locator session to identify clusters of cells.
Use a dark theme for the plot
Add a spline (currently hardwired to df=4, to be improved)
spline span in loess function call
Additional arguments to be passed to plot.
No return, only graphical output
# NOT RUN {
GenePlot(object = pbmc_small, gene1 = 'CD9', gene2 = 'CD3E')
# }
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