Visualize the process of binary cut

plot_binary_cut(mat, value_fun = area_above_ecdf, 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: A function that calculates the scores for the four submatrices on a node.
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 on the plot.
show_heatmap_legend: Whether to show the heatmap legend.
...: Other arguments.

Details

After the functions which perform clustering are executed, such as simplifyGO or binary_cut, the dendrogram is temporarily saved and plot_binary_cut directly uses this dendrogram.

Examples

# \donttest{
mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds", 
    package = "simplifyEnrichment"))
plot_binary_cut(mat, depth = 1)
#> use the cached dendrogram.

plot_binary_cut(mat, depth = 2)
#> use the cached dendrogram.

plot_binary_cut(mat)
#> use the cached dendrogram.

# }