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.

Value

binary_cut() returns a vector of numeric cluster labels.

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)
#> create a new dendrogram.

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

plot_binary_cut(mat)
#> use the cached dendrogram.

# }
mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds",
    package = "simplifyEnrichment"))
binary_cut(mat)
#>   [1]  1  2  3  4  5  4  4  4  1  3  3  5  1  5  1  4  1  4  5  4  5  1  4  3  6
#>  [26]  3  7  4  3  1  1  3  8  7  3  3  5  1  4  4  5  2  4  9  5  1  1  7  3  7
#>  [51]  5  3 10  7  1  7  3 10  3  3  7  1  5  5  3  1  1  1  1  3  1  4 11  3  1
#>  [76]  3  4  7  3  3  1  5  4  6  3  3  1  5  4  5  7  3  3  4  5  3  1  1  4  6
#> [101]  4  3  4  4  1  4  3  5  3  7  3  1  3  3  3  1  1  1  9  4  4 12  3  1  1
#> [126]  1  1  5  4  1  4  1  3  5  5  5  1  5  3  5  5  5  1  3  5  5 13  1  2  4
#> [151] 12  3  7  1  3  5  6  1  4  5  1  5  1  1  5  3  3  4  1  1  3  3  3  1  4
#> [176]  4  1  3  4  3  3 14  5  4  4  3  1  3  5  3  5  3  3  3  1  1  1  5  4  3
#> [201]  2  5  4  4 10  3  1  1  3  3  1  2  3  3  3  4  5  3  4  3  4  6  6  4  7
#> [226]  4  5  5  1  1  3  1  6 12  7  7  5  5  3  3  7  3  1  4  1  1  5  6  3  5
#> [251]  5  1 15  1  3 11  4  1  5 13  3  1  7  3  1  5  1  3  5  5  3  6  1  5  3
#> [276] 11  1  2  3  4  5 13  1  1 10  3  5  3  3  3  7  4  5  3  3  4  1  1  4  7
#> [301]  3  2  4  5  3  3  5  3  1  3  2  5  2  3  3  1  1  5  5  4 11  1  1  5  3
#> [326]  3  1  4  5  1  1  1  1  7  3  2  3  2  5  3  5  1 13  3  3  4  3  1  4  6
#> [351]  4  7  1  3  1  4  7  3  5  3  1  2 11  4  3  3  1  1  1  5  7  7  1  1  3
#> [376]  5  3  7  3 10  1  3  1  7  3  1  3 11  3 16  4  6  3  1  3  7  6  1  5  4
#> [401]  5  3  1  3  3  5  3  3  7  4  5  1  7  5  7  3  1 12  3  3 10  1  1  4  3
#> [426]  1  3 13  1  3  3  1  1  5  3  3  1  1  1  1 17  5  1  3  7  3  1  5  1  5
#> [451]  7  3  4  5  4  4  1  1  3  5  7 11  4  4  5  3  4  4  1  1  3  5 12  5  3
#> [476]  3  3  3  5 14  3  3  3  4  1 11  5  1  3  3  1  5  3  1  3 18  7  5  3  1