plotCountsVsFeatures: Plot count and feature correlation

In acidgenomics/minimalism: Acid Genomics Plot Functions and Themes

Plot count and feature correlation

Description

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

Usage

plotCountsVsFeatures(object, ...)

## S4 method for signature 'SingleCellExperiment'
plotCountsVsFeatures(
  object,
  interestingGroups = NULL,
  trendline = FALSE,
  trans = "log2",
  labels = list(title = "Counts vs. features", subtitle = NULL, x = "counts", y =
    "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.
trans	character(1). Name of the axis scale transformation to apply. For more information: help(topic = "scale_x_continuous", package = "ggplot2")
labels	list. ggplot2 labels. See ggplot2::labs() for details.
...	Additional arguments.

Details

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

Value

ggplot.

Note

Updated 2022-03-07.

Author(s)

Michael Steinbaugh, Rory Kirchner

See Also

plotCountsPerCell().

Examples

data(SingleCellExperiment_splatter, package = "AcidTest")

## SingleCellExperiment ====
object <- SingleCellExperiment_splatter
plotCountsVsFeatures(object)

acidgenomics/minimalism documentation built on April 1, 2024, 10:34 a.m.