| Title: | Manipulate with RNNI Tree Space |
|---|---|
| Description: | Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>. |
| Authors: | Jiří C. Moravec [aut, cre], Lena Collienne [aut] |
| Maintainer: | Jiří C. Moravec <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 0.1.1 |
| Built: | 2024-11-12 04:56:07 UTC |
| Source: | https://github.com/biods/rrnni |
Convert a tree of class "phylo" to a ranked tree of class "rankedPhylo".
as_ranked(x)as_ranked(x)
x |
a tree of class "phylo" |
To convert a tree to a ranked tree, the tree needs to be binary (i.e., fully resolved), be rooted, and ultrametric. Typically, such tree would be produced by coalescent process.
Ranked trees are similar to time-trees, only instead of time, we care about the order of the splits.
a ranked tree of class "rankedPhylo"
# this will throw an error, the tree is not ultrametric x = ape::rtree(5) try(as_ranked(x)) # coalescent trees can be converted without problem y = ape::rcoal(5) as_ranked(y)# this will throw an error, the tree is not ultrametric x = ape::rtree(5) try(as_ranked(x)) # coalescent trees can be converted without problem y = ape::rcoal(5) as_ranked(y)
Find shared tip labels of a collection of trees. This function is useful when pruning a collection of related trees to a trees of same size and taxa.
common_tips(x)common_tips(x)
x |
a collection of trees of class "multiPhylo" |
intersection of tip labels across all trees
trees = rankedPhylo(3:7) common_tips(trees)trees = rankedPhylo(3:7) common_tips(trees)
Prune a tree or a collection of trees and keep only requested tips.
keep_tips(x, tips) ## S3 method for class 'phylo' keep_tips(x, tips) ## S3 method for class 'multiPhylo' keep_tips(x, tips) ## S3 method for class 'rankedPhylo' keep_tips(x, tips)keep_tips(x, tips) ## S3 method for class 'phylo' keep_tips(x, tips) ## S3 method for class 'multiPhylo' keep_tips(x, tips) ## S3 method for class 'rankedPhylo' keep_tips(x, tips)
x |
a tree of class "phylo" or "multiPhylo" |
tips |
tip labels to keep |
a pruned tree or a collection
tree = rankedPhylo(5) # select randomly 3 tips to keep tips = sample(tips(tree), 3) keep_tips(tree, tips) trees = rankedPhylo(3:7) # get tips from the first tree tips = tips(trees[[1]]) # prune all trees, all of them will have 3 tips keep_tips(trees, tips)tree = rankedPhylo(5) # select randomly 3 tips to keep tips = sample(tips(tree), 3) keep_tips(tree, tips) trees = rankedPhylo(3:7) # get tips from the first tree tips = tips(trees[[1]]) # prune all trees, all of them will have 3 tips keep_tips(trees, tips)
Create a random ranked phylogeny using the coalescent method.
random_tree(n)random_tree(n)
n |
the number of tips |
This is implementation of a ranked coalescent algorithm described in Collienne (2021).
Starting from n tips of a tree, all with rank 0, randomly select two tips and merge them
into a new node with rank 1, and add the new node to remaining tips. In next iteration, assign
rank 2 and so on, until only a single node, the root, remains.
random ranked tree of class "rankedPhylo"
Collienne, L. (2021). Spaces of Phylogenetic Time Trees (p. 158). University of Otago.
random_tree(5)random_tree(5)
Create a new, or coerce character vector or an existing tree into a ranked tree.
rankedPhylo(x) ## Default S3 method: rankedPhylo(x) ## S3 method for class 'numeric' rankedPhylo(x) ## S3 method for class 'character' rankedPhylo(x) ## S3 method for class 'phylo' rankedPhylo(x) ## S3 method for class 'multiPhylo' rankedPhylo(x)rankedPhylo(x) ## Default S3 method: rankedPhylo(x) ## S3 method for class 'numeric' rankedPhylo(x) ## S3 method for class 'character' rankedPhylo(x) ## S3 method for class 'phylo' rankedPhylo(x) ## S3 method for class 'multiPhylo' rankedPhylo(x)
x |
a numeric vector, character vector or a object of class |
This is a wrapper for multiple functions that create a ranked tree.
As such, it accepts multiple types of inputs and produce either rankedPhylo or
a collated list of ranked trees as a multiRankedPhylo.
If x is a numeric vector, create n = length(x) random ranked tree using the random_tree
function.
If x is a character vector, try to read the character as a newick-formatted tree.
If x is object of a class phylo or multiPhylo, coerce these objects into rankedPhylo
or multiRankedPhylo using the as_ranked function.
ranked tree or trees of class rankedPhylo or multiRankedPhylo
# Create a single random ranked tree with 5 tips rankedPhylo(5) # Create multiple random ranked trees with 5 tips rankedPhylo(c(5,5,5)) # Convert a coalescent tree into a ranked tree x = ape::rcoal(5) # random coalescent tree rankedPhylo(x)# Create a single random ranked tree with 5 tips rankedPhylo(5) # Create multiple random ranked trees with 5 tips rankedPhylo(c(5,5,5)) # Convert a coalescent tree into a ranked tree x = ape::rcoal(5) # random coalescent tree rankedPhylo(x)
Read a tree in a newick format and convert it to a rankedPhylo
read_newick(x)read_newick(x)
x |
a single character string containing tree in a newick format |
object of class "rankedPhylo"
read_newick("((A:1,B:1):1,C:2);") # Note, not all valid newick trees are valid ranked trees try( read_newick("((A,B),C);") )read_newick("((A:1,B:1):1,C:2);") # Note, not all valid newick trees are valid ranked trees try( read_newick("((A,B),C);") )
Calculate Ranked Nearest Neighbour Interchange (RNNI) distance.
rnni(x, y, normalize = FALSE)rnni(x, y, normalize = FALSE)
x |
a tree of class "rankedPhylo" |
y |
a tree of class "rankedPhylo" |
normalize |
normalize the distance to the maximum distance of diameter |
The RNNI distance is the shortest distance between two trees in the RNNI space, which is defined
by rank and NNI moves. This space is defined only for ranked trees. Non-ranked ultrametric
trees can be coerced to ranked trees with as_ranked or rankedPhylo functions.
The distance can be normalized to the maximum possible distance, the diameter of the tree-space.
The diameter is defined as (n-1)(n-2)/2, where n is the number of tips/leaves.
The used algorithm implements the TREEPATH algorithm described in Collienne (2021).
an integer RNNI distance between x and y
Collienne, L. (2021). Spaces of Phylogenetic Time Trees. University of Otago.
Sort tip labels in a tree or a collection of trees.
sort_tips(x) ## S3 method for class 'phylo' sort_tips(x) ## S3 method for class 'multiPhylo' sort_tips(x)sort_tips(x) ## S3 method for class 'phylo' sort_tips(x) ## S3 method for class 'multiPhylo' sort_tips(x)
x |
a tree of class "phylo" or a collection of trees of class "multiPhylo" |
The rnni function assumes tip labels in an unambiguous order.
This is due to the internal implementation not having tip labels,
tip/leave are instead identified by the index in the node matrix.
The sort_tips orders the tip labels and change the node indices
in the node matrix so that the tips for two different trees of the
same taxa have the same node id.
an input tree with sorted tips
# generate random trees # use rcoal(5) instead of rankedPhylo(5) # as ranked phylo always generate tip.labels # in the same order. t1 = ape::rcoal(5) |> rankedPhylo() t2 = ape::rcoal(5) |> rankedPhylo() t1s = sort_tips(t1) t2s = sort_tips(t2) all(tips(t1s) == tips(t2s)) # for collection of trees: trees = c(t1, t2) sort_tips(trees)# generate random trees # use rcoal(5) instead of rankedPhylo(5) # as ranked phylo always generate tip.labels # in the same order. t1 = ape::rcoal(5) |> rankedPhylo() t2 = ape::rcoal(5) |> rankedPhylo() t1s = sort_tips(t1) t2s = sort_tips(t2) all(tips(t1s) == tips(t2s)) # for collection of trees: trees = c(t1, t2) sort_tips(trees)
Extract tip labels from the object of class "phylo" or "multiPhylo"
tips(x, ...) ## S3 method for class 'phylo' tips(x, ...) ## S3 method for class 'multiPhylo' tips(x, all = FALSE, ...)tips(x, ...) ## S3 method for class 'phylo' tips(x, ...) ## S3 method for class 'multiPhylo' tips(x, all = FALSE, ...)
x |
an object of class "phylo" or "multiPhylo" |
... |
arguments to be passed to methods |
all |
optional extract tips for all trees, only if |
This is convenience method, it's purpose is to easily retrieve tip labels of a tree or
a collection of trees with the same tip labels. Due to this, when called on a collection of
trees (an object of class "multiPhylo"), only the first tree of a collection is accessed.
To obtain tip labels from all trees, specify the argument all=TRUE.
a vector of tip labels, or list of vectors in case of "multiPhylo" with all=TRUE
tree = rankedPhylo(5) tips(tree) trees = rankedPhylo(3:7) # only the first tree is accessed tips(trees) # use this to obtain all tip labels tips(trees, all=TRUE)tree = rankedPhylo(5) tips(tree) trees = rankedPhylo(3:7) # only the first tree is accessed tips(trees) # use this to obtain all tip labels tips(trees, all=TRUE)