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Generalized UniFrac (GUniFrac)

Source: R/beta_div.r
generalized_unifrac.Rd

A unified UniFrac distance that balances the weight of abundant and rare lineages.

Usage

generalized_unifrac(
  counts,
  tree = NULL,
  alpha = 0.5,
  margin = 1L,
  pairs = NULL,
  cpus = n_cpus()
)

Arguments

counts

A numeric matrix of count data (samples \(\times\) features). Typically contains absolute abundances (integer counts), though proportions are also accepted.

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').

alpha

How much weight to give to relative abundances; a value between 0 and 1, inclusive. Setting alpha=1 is equivalent to normalized_unifrac().

margin

The margin containing samples. 1 if samples are rows, 2 if samples are columns. Ignored when counts is a special object class (e.g. phyloseq). Default: 1

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.

Details

The Generalized UniFrac distance is defined as: $$\frac{\sum_{i=1}^{n} L_i(P_i + Q_i)^{\alpha}\left|\frac{P_i - Q_i}{P_i + Q_i}\right|}{\sum_{i=1}^{n} L_i(P_i + Q_i)^{\alpha}}$$

Where:

  • \(n\) : The number of branches in the tree.

  • \(L_i\) : The length of the \(i\)-th branch.

  • \(P_i\), \(Q_i\) : The proportion of the community descending from branch \(i\) in sample P and Q.

  • \(\alpha\) : A scalable weighting factor.

Parameter: alpha

The alpha parameter controls the weight given to abundant lineages. \(\alpha = 1\) corresponds to Weighted UniFrac, while \(\alpha = 0\) corresponds to Unweighted UniFrac.

Input Types

The counts parameter is designed to accept a simple numeric matrix, but seamlessly supports objects from the following biological data packages:

  • phyloseq

  • rbiom

  • SummarizedExperiment

  • TreeSummarizedExperiment

For large datasets, standard matrix operations may be slow. See vignette('performance') for details on using optimized formats (e.g. sparse matrices) and parallel processing.

References

Chen, J., Bittinger, K., Charlson, E. S., Hoffmann, C., Lewis, J., Wu, G. D., ... & Li, H. (2012). Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics, 28(16), 2106-2113. doi:10.1093/bioinformatics/bts342

See also

beta_div(), vignette('bdiv'), vignette('bdiv_guide')

Other Phylogenetic metrics: faith(), normalized_unifrac(), unweighted_unifrac(), variance_adjusted_unifrac(), weighted_unifrac()

Examples

    generalized_unifrac(ex_counts, tree = ex_tree, alpha = 0.5)
#>          Saliva      Gums      Nose
#> Gums  0.4471644                    
#> Nose  0.8215129 0.7607876          
#> Stool 0.9727827 0.9784242 0.9730332