Plot base mean distribution

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

Usage

plotBaseMean(object, ...)

# S4 method for DESeqAnalysis
plotBaseMean(
  object,
  nonzero = TRUE,
  trans = c("log10", "log2", "identity"),
  summary = TRUE,
  labels = list(title = "Base mean distribution", subtitle = NULL)
)

# S4 method for DESeqDataSet
plotBaseMean(
  object,
  nonzero = TRUE,
  trans = c("log10", "log2", "identity"),
  summary = TRUE,
  labels = list(title = "Base mean distribution", subtitle = NULL)
)

# S4 method for DESeqResults
plotBaseMean(
  object,
  nonzero = TRUE,
  trans = c("log10", "log2", "identity"),
  summary = TRUE,
  labels = list(title = "Base mean distribution", subtitle = NULL)
)

Arguments

object

Object.

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.

labels

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

...

Additional arguments.

Value

ggplot.

Functions

• plotBaseMean(DESeqAnalysis): Passes to DESeqDataSet method.

• plotBaseMean(DESeqDataSet): Generates row means of normalized counts. This value corresponds to the baseMean column of DESeqResults.

• plotBaseMean(DESeqResults): Uses baseMean column of results.

Note

Updated 2022-05-17.

See also

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

• summary().

Examples

data(deseq)

## DESeqAnalysis ====
plotBaseMean(deseq)
#> ℹ Removing 1 zero-count feature.
#> ℹ Summary prior to transformation:
#> Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> 0.08    6.46   19.51   42.53   44.43  681.66 
#> → Applying 'log10(x + 1)' transformation.
#> ℹ Summary after transformation:
#> Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> 0.03    0.87    1.31    1.28    1.66    2.83