plot_binary_cut {simplifyEnrichment}R Documentation

Visualize the process of binary cut

Description

Visualize the process of binary cut

Usage

plot_binary_cut(mat, value_fun = median, cutoff = 0.85,
    partition_fun = partition_by_pam, dend = NULL, dend_width = unit(3, "cm"),
    depth = NULL, show_heatmap_legend = TRUE, ...)

Arguments

mat

The similarity matrix.

value_fun

Value function to calculate the score for each node in the dendrogram.

cutoff

The cutoff for splitting the dendrogram.

partition_fun

A function to split each node into two groups. Pre-defined functions in this package are partition_by_kmeanspp, partition_by_pam and partition_by_hclust.

dend

A dendrogram object, used internally.

depth

Depth of the recursive binary cut process.

dend_width

Width of the dendrogram.

show_heatmap_legend

Whether to show the heatmap legend.

...

Other arguments.

Details

After the functions which performs clustering are executed, such as simplifyGO or binary_cut, the dendrogram is temporarily saved and plot_binary_cut directly uses this dendrogram. So, if the partition function brings randomness, it makes sure the clustering is the same as the one made by e.g. simplifyGO.

Examples


mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds", 
    package = "simplifyEnrichment"))
plot_binary_cut(mat, depth = 1)
plot_binary_cut(mat, depth = 2)
plot_binary_cut(mat)


[Package simplifyEnrichment version 1.3.0 Index]