Plot mitochondrial vs. coding counts

plotMitoVsCoding(object, ...)

# S4 method for SingleCellExperiment
plotMitoVsCoding(object,
  interestingGroups = NULL, trendline = FALSE, color = getOption(x =
  "acid.color.discrete", default =
  acidplots::scale_colour_synesthesia_d()), trans = "log2",
  title = "Mito vs. coding")

Arguments

object

Object.

interestingGroups

character. Groups of interest to use for visualization. Corresponds to factors describing the columns of the object.

trendline

logical(1). Include trendline on plot.

color

ScaleDiscrete. Desired ggplot2 color scale. Must supply discrete values. When set NULL, the default ggplot2 color palette will be used. If manual color definitions are desired, we recommend using ggplot2::scale_color_manual().

To set the discrete color palette globally, use:

options(acid.color.discrete = ggplot2::scale_color_viridis_d())
trans

character(1). Name of the axis scale transformation to apply.

For more information:

help(topic = "scale_x_continuous", package = "ggplot2")
title

character(1). Title.

...

Additional arguments.

Value

ggplot.

Note

Updated 2019-07-24.

Examples

data(SingleCellExperiment, package = "acidtest") ## SingleCellExperiment ==== object <- SingleCellExperiment object <- calculateMetrics(object)
#> Calculating 100 cellular barcode metrics.
#> 497 coding features detected.
#> 0 mitochondrial features detected.
if (!anyNA(object$nMito)) { plotMitoVsCoding(object) }