binary_cut: Cluster functional terms by recursively binary cutting the...

In jokergoo/simplifyEnrichment: Simplify Functional Enrichment Results

Cluster functional terms by recursively binary cutting the similarity matrix

Description

Cluster functional terms by recursively binary cutting the similarity matrix

Usage

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,
  ...
)

binary_cut(
  mat,
  value_fun = area_above_ecdf,
  partition_fun = partition_by_hclust,
  cutoff = 0.85,
  try_all_partition_fun = TRUE,
  partial = nrow(mat) > 1500
)

Arguments

mat	A 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.
dend_width	Width of the dendrogram on the plot.
depth	Depth of the recursive binary cut process.
show_heatmap_legend	Whether to show the heatmap legend.
...	Other arguments.
try_all_partition_fun	Different partition_fun may give different clusterings. If the vaule of try_all_partition_fun is set to TRUE, the similarity matrix is clustered by three partitioning method: partition_by_pam(), partition_by_kmeanspp() and partition_by_hclust(). The clustering with the highest difference score is finally selected as the final clustering.
partial	Whether to generate the complete clustering or the clustering stops when sub-matrices cannot be split anymore.

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.

Value

binary_cut() returns a vector of numeric cluster labels.

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)

mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds",
    package = "simplifyEnrichment"))
binary_cut(mat)

jokergoo/simplifyEnrichment documentation built on Sept. 16, 2024, 8:39 a.m.