The base mean is the mean of normalized counts of all samples, normalizing for sequencing depth.

plotBaseMean(object, ...)

# S4 method for numeric
plotBaseMean(
  object,
  nonzero = TRUE,
  trans = c("log10", "log2", "identity"),
  summary = TRUE,
  color = getOption(x = "acid.color.discrete", default =
    acidplots::scale_color_synesthesia_d()),
  labels = list(title = "Base mean distribution", subtitle = NULL, x =
    "average expression across all samples", y = "density", color = "summary")
)

# S4 method for DESeqDataSet
plotBaseMean(object, ...)

# S4 method for DESeqResults
plotBaseMean(object, ...)

# S4 method for DESeqAnalysis
plotBaseMean(object, ...)

Arguments

object

Object.

...

Additional arguments.

nonzero

logical(1). Remove zero-count features (genes).

trans

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

For more information:

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

logical(1). Include distribution summary statistics as lines on the 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())
labels

list. ggplot2 labels. See ggplot2::labs() for details.

Value

ggplot.

Functions

  • plotBaseMean,DESeqDataSet-method: Generates row means of normalized counts. This value corresponds to the baseMean column of DESeqResults. Passes to numeric method.

  • plotBaseMean,DESeqResults-method: Uses baseMean column of results. Passes to numeric method.

  • plotBaseMean,DESeqAnalysis-method: Passes to DESeqDataSet method.

Note

Updated 2019-10-15.

See also

  • https://support.bioconductor.org/p/75244/

  • summary()

Examples

data(deseq) plotBaseMean(deseq)
#> Removing 1 zero-count feature.
#> Summary prior to transformation: #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 0.07 5.53 18.06 48.90 53.93 1020.58
#> Applying 'log10(x + 1)' transformation.
#> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 0.03 0.81 1.28 1.28 1.74 3.01