plot_binary_cut: Visualize the process of binary cut
In simplifyEnrichment: Simplify Functional Enrichment Results

Description Usage Arguments Details Examples

Visualize the process of binary cut

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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, ...)

`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.

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.

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)