cluster: Cluster entities together

In clustur: Clustering

Cluster entities together

Description

Clusters entities represented in a distance matrix and count table using one of several algorithms and outputs information about the composition and abundance of each cluster

Usage

cluster(
  distance_object,
  cutoff,
  method = "opticlust",
  feature_column_name_to = "feature",
  bin_column_name_to = "bin",
  random_seed = 123
)

Arguments

distance_object	The distance object that was created using the 'read_dist()' function.
cutoff	The cutoff you want to cluster towards.
method	The method of clustering to be performed: opticlust (default), furthest, nearest, average, or weighted.
feature_column_name_to	Set the name of the column in the cluster dataframe that contains the sequence names.
bin_column_name_to	Set the name of the column in the cluster dataframe that contains the name of the group of sequence names.
random_seed	the random seed to use, (default = 123).

Value

A list of 'data.frames' that contain abundance, and clustering results. If you used 'method = opticlust', it will also return clustering performance metrics.

Examples

 cutoff <- 0.03
 count_table <- read_count(example_path("amazon.full.count_table"))
 distance_data <- read_dist(example_path("amazon_column.dist"),
                            count_table, cutoff)

 cluster_results <- cluster(distance_data,
                            cutoff, method = "opticlust",
                            feature_column_name_to = "sequence",
                            bin_column_name_to = "omu")
 cluster_results <- cluster(distance_data,
                            cutoff, method = "furthest")
 cluster_results <- cluster(distance_data,
                            cutoff, method = "nearest")
 cluster_results <- cluster(distance_data,
                            cutoff, method = "average")
 cluster_results <- cluster(distance_data,
                            cutoff, method = "weighted")

clustur documentation built on April 15, 2025, 1:33 a.m.