clustur (version 0.1.2)

cluster: 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
)

Value

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

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).

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")