tree_distance: Calculate the distance between two trees.

In castor: Efficient Phylogenetics on Large Trees

Calculate the distance between two trees.

Description

Given two rooted phylogenetic trees with identical tips, calculate their difference using a distance metric or pseudometric.

Usage

tree_distance(  treeA, 
                treeB,
                tipsA2B       = NULL,
                metric        = "RFrooted",
                normalized    = FALSE,
                NLeigenvalues = 10)

Arguments

treeA	A rooted tree of class "phylo".
treeB	A rooted tree of class "phylo". Depending on the metric used, this tree may need to have the same number of tips as treeA (see details below).
tipsA2B	Optional integer vector of size Ntips, mapping treeA tip indices to treeB tip indices (i.e. tipsA2B[a] is the tip index in treeB corresponding to tip index a in treeA). The mapping must be one-to-one. If left unspecified, it is determined by matching tip labels between the two trees (this assumes that the same tip labels are used in both trees). Only relevant if the metric requires tip matching (i.e., considers labeled trees).
metric	Character, specifying the distance measure to be used. Currently the Robinson-Foulds metric for rooted trees ("RFrooted"), the mean-path-difference ("MeanPathLengthDifference"), "WassersteinNodeAges" and "WassersteinLaplacianSpectrum" are implemented. Note that these distances are not necessarily metrics in the strict mathematical sense; in particular, non-identical trees can sometimes have a zero distance. "RFrooted" counts the number of clusters (sets of tips descending from a node) in either of the trees but not shared by both trees (Robinson and Foulds, 1981; Day, 1985); this metric does not take into account branch lengths and depends on the position of the root. "MeanPathLengthDifference" is the square root of the mean squared difference of patristic distances (shortest path lengths) between tip pairs, as described by Steel and Penny (1993); this metric takes into account path lengths and does not depend on the position of the root. "WassersteinNodeAges" calculates the first Wasserstein distance (Ramdas et al. 2017) between the distributions of node ages in the two trees. It depends on the branch lengths and the rooting, but does not depend on tip labeling nor topology (as long as node ages are the same). Hence, this is only a 'pseudometric' in the space of unlabeled trees - any two trees with identical node ages will have distance 0. "WassersteinLaplacianSpectrum" calculates the first Wasserstein distance between the spectra (sets of eigenvalues) of the modified graph Laplacians (Lewitus and Morlon, 2016). This distance depends on tree topology and branch lengths, but not on tip labeling nor on the rooting. Note that Lewitus and Morlon measured the distance between the Laplacian spectra in a different way than here. Also note that if NLeigenvalues>0, only a subset of the eigenvalues may be considered.
normalized	Logical, specifying whether the calculated distance should be normalized to be between 0 and 1. For the Robinson-Foulds distance, the distance will be normalized by dividing it by the total number of nodes in the two trees. For MeanPathLengthDifference, normalization is done by dividing each path-length difference by the maximum of the two path-lengths considered. For WassersteinNodeAges, normalization is achieved by scaling all node ages relative to the oldest node age in any of the two trees (hence times are converted to relative times). Note that normalized distances may no longer satisfy the triangle inequality required for metrics, i.e. the resulting distance function may not be a metric in the mathematical sense.
NLeigenvalues	Integer, number of top eigenvalues (i.e., with largest magnitude) to consider from the Graph-Laplacian's spectrum (e.g., for the metric "WassersteinLaplacianSpectrum"). This option is mostly provided for computational efficiency reasons, because it is cheaper to compute a small subset of eigenvalues rather than the entire spectrum. If <=0, all eigenvalues are considered, which can substantially increase computation time and memory for large trees.

Details

For some metrics ("RFrooted", "MeanPathLengthDifference"), the trees must have the same number of tips and their tips must be matched one-to-one. If the trees differ in theis tips, they must be pruned down to their common set of tips. If tips have different labels in the two trees, but are nevertheless equivalent, the mapping between the two trees must be provided using tipsA2B.

The trees may include multi-furcations as well as mono-furcations (i.e. nodes with only one child).

Note that under some Robinson-Foulds variants the trees can be unrooted; in this present implementation trees must be rooted and the placement of the root influences the distance, following the definition by Day (1985).

Value

A single non-negative number, representing the distance between the two trees.

Author(s)

Stilianos Louca

References

Robinson, D. R., Foulds, L. R. (1981). Comparison of phylogenetic trees. Mathematical Biosciences. 53: 131-147.

Day, W. H. E. (1985). Optimal algorithms for comparing trees with labeled leaves. Journal of Classification. 2:7-28.

Steel, M. A., Penny D. (1993). Distributions of tree comparison metrics - Some new results. Systematic Biology. 42:126-141.

Ramdas, A. et al. (2017). On Wasserstein two-sample testing and related families of nonparametric tests. Entropy. 19(2):47.

Lewitus, E. and Morlon, H. (2016). Characterizing and comparing phylogenies from their laplacian spectrum. Systematic Biology. 65:495-507.

See Also

congruent_divergence_times

Examples

# generate a random tree
Ntips = 1000
treeA = generate_random_tree(list(birth_rate_intercept=1),
                            max_tips=Ntips)$tree
                            
# create a second tree with slightly different topology
treeB = treeA
shuffled_tips = sample.int(Ntips, size=Ntips/10, replace=FALSE)
treeB$tip.label[shuffled_tips] = treeB$tip.label[sample(shuffled_tips)]

# calculate Robinson-Foulds distance between trees
distance = tree_distance(treeA, treeB, metric="RFrooted")

castor documentation built on Aug. 18, 2023, 1:07 a.m.