Plot a dendrogram stored in a ModularExperiment

Plots the dendrogram in the dendrogam slot of a ModularExperiment object using the plotDendroAndColors function.

Usage

# S4 method for class 'ModularExperiment'
plotDendro(
  object,
  groupLabels = "Module colors",
  dendroLabels = FALSE,
  hang = 0.03,
  addGuide = TRUE,
  guideHang = 0.05,
  color_func = WGCNA::labels2colors,
  modules_are_colors = FALSE,
  ...
)

Arguments

object

ModularExperiment object.

groupLabels

Module label axis label. See plotDendroAndColors.

dendroLabels

If TRUE, shows feature names in the dendrogram. See plotDendroAndColors.

hang

The fraction of the plot height by which labels should hang below the rest of the plot. See plot.hclust.

addGuide

If TRUE, adds vertical guide lines to the dendrogram. See plotDendroAndColors.

guideHang

The fraction of the dendrogram's height to leave between the top end of the guide line and the dendrogram merge height. See plotDendroAndColors.

color_func

Function for converting module names to colors. Only used if modules_are_colors is FALSE.

modules_are_colors

If TRUE, expects the module names to be colors. Else, assumes that module names are are numbers that can be converted into colours by color_func.

...

Additional arguments to be passed to plotDendroAndColors.

Value

A plot produced by plotDendroAndColors.

See also

WGCNA::plotDendroAndColors(), plot.hclust

Author

Jack Gisby

Examples

# Create ModularExperiment with random data (100 features, 50 samples,
# 10 modules)
me <- ReducedExperiment:::.createRandomisedModularExperiment(100, 50, 10)
me
#> class: ModularExperiment 
#> dim: 100 50 10 
#> metadata(0):
#> assays(1): normal
#> rownames(100): gene_1 gene_2 ... gene_99 gene_100
#> rowData names(0):
#> colnames(50): sample_1 sample_2 ... sample_49 sample_50
#> colData names(0):
#> 10 components

# The dendrogram is usually produced during module discovery, but we can
# assign any dendrogram to the slot. Let's do hierarchical clustering on the
# features in our object and assign it
dendrogram(me) <- hclust(dist(assay(me)))
dendrogram(me)
#> 
#> Call:
#> hclust(d = dist(assay(me)))
#> 
#> Cluster method   : complete 
#> Distance         : euclidean 
#> Number of objects: 100 
#> 

# Plot the dendrogram - modules are random in this instance, but in general
# features within a module should cluster together
plotDendro(me)