weighted_unifrac: Weighted UniFrac

In ecodive: Parallel and Memory-Efficient Ecological Diversity Metrics

Weighted UniFrac

Description

Weighted UniFrac beta diversity metric.

Usage

weighted_unifrac(counts, tree = NULL, pairs = NULL, cpus = n_cpus())

Arguments

counts	An OTU abundance matrix where each column is a sample, and each row is an OTU. Any object coercible with as.matrix() can be given here, as well as phyloseq, rbiom, SummarizedExperiment, and TreeSummarizedExperiment objects.
tree	A phylo-class object representing the phylogenetic tree for the OTUs in counts. The OTU identifiers given by colnames(counts) must be present in tree. Can be omitted if a tree is embedded with the counts object or as attr(counts, 'tree').
pairs	Which combinations of samples should distances be calculated for? The default value (NULL) calculates all-vs-all. Provide a numeric or logical vector specifying positions in the distance matrix to calculate. See examples.
cpus	How many parallel processing threads should be used. The default, n_cpus(), will use all logical CPU cores.

Value

A dist object.

Calculation

Given n branches with lengths L and a pair of samples' abundances (A and B) on each of those branches:

D = \sum_{i = 1}^{n} L_i|\frac{A_i}{A_T} - \frac{B_i}{B_T}|

See vignette('unifrac') for details and a worked example.

References

Lozupone CA, Hamady M, Kelley ST, Knight R 2007. Quantitative and Qualitative \beta Diversity Measures Lead to Different Insights into Factors That Structure Microbial Communities. Applied and Environmental Microbiology, 73(5). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1128/AEM.01996-06")}

See Also

Other beta_diversity: bray_curtis(), canberra(), euclidean(), generalized_unifrac(), gower(), jaccard(), kulczynski(), manhattan(), unweighted_unifrac(), variance_adjusted_unifrac(), weighted_normalized_unifrac()

Examples

    # Example counts matrix
    ex_counts
    
    # Weighted UniFrac distance matrix
    weighted_unifrac(ex_counts, tree = ex_tree)
    
    # Only calculate distances for A vs all.
    weighted_unifrac(ex_counts, tree = ex_tree, pairs = 1:3)
    

ecodive documentation built on Aug. 23, 2025, 1:13 a.m.