Plot the disambiguated counts per cell vs. features (i.e. genes or transcripts) detected.

plotCountsVsFeatures(object, ...)

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

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.

Details

"Counts" refer to universal molecular identifier (UMI) counts for droplet-based scRNA-seq data.

Note

Updated 2019-08-08.

See also

Examples

data(SingleCellExperiment, package = "acidtest") ## SingleCellExperiment ==== object <- SingleCellExperiment object <- calculateMetrics(object)
#> Calculating 100 cellular barcode metrics.
#> 497 coding features detected.
#> 0 mitochondrial features detected.
plotCountsVsFeatures(object)