bdiv_clusters: Cluster samples by beta diversity k-means.

In rbiom: Read/Write, Analyze, and Visualize 'BIOM' Data

Cluster samples by beta diversity k-means.

Description

Cluster samples by beta diversity k-means.

Usage

bdiv_clusters(
  biom,
  bdiv = "Bray-Curtis",
  weighted = TRUE,
  normalized = TRUE,
  tree = NULL,
  k = 5,
  ...
)

Arguments

biom	An rbiom object, such as from as_rbiom(). Any value accepted by as_rbiom() can also be given here.
bdiv	Beta diversity distance algorithm(s) to use. Options are: "Bray-Curtis", "Manhattan", "Euclidean", "Jaccard", and "UniFrac". For "UniFrac", a phylogenetic tree must be present in biom or explicitly provided via ⁠tree=⁠. Multiple/abbreviated values allowed. Default: "Bray-Curtis"
weighted	Take relative abundances into account. When weighted=FALSE, only presence/absence is considered. Multiple values allowed. Default: TRUE
normalized	Only changes the "Weighted UniFrac" calculation. Divides result by the total branch weights. Default: TRUE
tree	A phylo object representing the phylogenetic relationships of the taxa in biom. Only required when computing UniFrac distances. Default: biom$tree
k	Number of clusters. Default: 5L
...	Passed on to stats::kmeans().

Value

A numeric factor assigning samples to clusters.

See Also

Other beta_diversity: bdiv_boxplot(), bdiv_corrplot(), bdiv_heatmap(), bdiv_ord_plot(), bdiv_ord_table(), bdiv_stats(), bdiv_table(), distmat_stats()

Other clustering: taxa_clusters()

Examples

    library(rbiom)
    
    biom <- rarefy(hmp50)
    biom$metadata$bray_cluster <- bdiv_clusters(biom)
    
    pull(biom, 'bray_cluster')[1:10]
    
    bdiv_ord_plot(biom, stat.by = "bray_cluster")

rbiom documentation built on April 3, 2025, 6:39 p.m.