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 1.4.0 Index]