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")