Plot the disambiguated counts per cell.

plotCountsPerCell(object, ...)

# S4 method for SingleCellExperiment
plotCountsPerCell(object,
  geom = c("histogram", "ecdf", "violin", "ridgeline", "boxplot"),
  interestingGroups = NULL, min = 0L, max = Inf, point = c("none",
  "inflection", "knee"), trans = "log10", color = getOption(x =
  "acid.color.discrete", default = acidplots::scale_color_synesthesia_d()),
  fill = getOption(x = "acid.fill.discrete", default =
  acidplots::scale_fill_synesthesia_d()), title = "Counts per cell")

Arguments

object

Object.

geom

character(1). Plot type. Uses match.arg() internally and defaults to the first argument in the character vector.

interestingGroups

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

min

numeric(1). Recommended minimum value cutoff.

max

numeric(1). Recommended maximum value cutoff.

point

character(1). Label either the knee or inflection points per sample. Requires geom = "ecdf".

trans

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

For more information:

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

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

To set the discrete fill palette globally, use:

options(acid.fill.discrete = ggplot2::scale_fill_viridis_d())
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 2019-08-27.

See also

Examples

data(SingleCellExperiment, package = "acidtest") ## SingleCellExperiment ==== object <- SingleCellExperiment object <- calculateMetrics(object)
#> Calculating 100 sample metrics.
#> 497 coding features detected.
#> 0 mitochondrial features detected.
plotCountsPerCell(object, geom = "violin")
plotCountsPerCell(object, geom = "ridgeline")
#> Picking joint bandwidth of 0.0411
plotCountsPerCell(object, geom = "ecdf")
plotCountsPerCell(object, geom = "histogram")
plotCountsPerCell(object, geom = "boxplot")