Create histogram or barplot of assay, rowData or colData

This methods visualizes abundances or variables from rowData or colData.

plotHistogram(x, ...)

plotBarplot(x, ...)

# S4 method for class 'SummarizedExperiment'
plotHistogram(
  x,
  assay.type = NULL,
  features = NULL,
  row.var = NULL,
  col.var = NULL,
  ...
)

# S4 method for class 'SummarizedExperiment'
plotBarplot(
  x,
  assay.type = NULL,
  features = NULL,
  row.var = NULL,
  col.var = NULL,
  ...
)

Arguments

x

a SummarizedExperiment object.

...

Additional parameters for plotting.

• layout: Character scalar. Specifies the layout of plot. Must be either "histogram" or "density". (Default: "histogram")

• facet.by: Character vector. Specifies variables from colData(x) or rowData(x) used for faceting. (Default: NULL)

• fill.by: Character scalar. Specifies variable from colData(x) or rowData(x) used for coloring. (Default: NULL)

assay.type

NULL or character scalar. Specifies the abundace table to plot. (Default: NULL)

features

NULL or character vector. If assay.type is specified, this specifies rows to visualize in different facets. If NULL, whole data is visualized as a whole. (Default: NULL)

row.var

NULL or character vector. Specifies a variable from rowData(x) to visualize. (Default: NULL)

col.var

NULL or character vector Specifies a variable from colData(x) to visualize. (Default: NULL)

Value

A ggplot2 object.

Details

Histogram and bar plot are a basic visualization techniques in quality control. It helps to visualize the distribution of data. plotAbundance allows researcher to visualise the abundance from assay, or variables from rowData or colData. For visualizing categorical values, one can utilize plotBarplot.

plotAbundanceDensity function is related to plotHistogram. However, the former visualizes the most prevalent features, while the latter can be used more freely to explore the distributions.

See also

• plotAbundanceDensity

• scater::plotExpression

• scater::plotRowData

• scater::plotColData

Examples

data(GlobalPatterns)
tse <- GlobalPatterns

# Visualize the counts data. There are lots of zeroes.
plotHistogram(tse, assay.type = "counts")
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.


# Apply transformation
tse <- transformAssay(tse, method = "clr", pseudocount = TRUE)
#> A pseudocount of 0.5 was applied.
# And plot specified rows
plotHistogram(
    tse,
    assay.type = "clr",
    features = rownames(tse)[1:5],
    facet.by = "rownames"
)
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.


# Calculate shannon diversity and visualize its distribution with density
# plot. Different sample types are separated with color.
tse <- addAlpha(tse, index = "shannon")
plotHistogram(
    tse,
    col.var = "shannon",
    layout = "density",
    fill.by = "SampleType"
)


# For categorical values, one can utilize a bar plot
plotBarplot(tse, col.var = "SampleType")