| Title: | Distance Between Phylogenetic Histories |
|---|---|
| Description: | Geodesic distance between phylogenetic trees and associated functions. The theoretical background of 'distory' is published in Billera et al. (2001) "Geometry of the space of phylogenetic trees." <doi:10.1006/aama.2001.0759>. |
| Authors: | John Chakerian [aut], Susan Holmes [aut], Emmanuel Paradis [cre] |
| Maintainer: | Emmanuel Paradis <[email protected]> |
| License: | BSD_3_clause + file LICENSE |
| Version: | 1.4.5 |
| Built: | 2024-11-07 03:10:44 UTC |
| Source: | https://github.com/cran/distory |
The distory package provides functions for computing geodesic distances between phylogenetic trees, as well as functions which use this distance. Methods for computing Gromov delta-hyperbolicity, Markov Chain Monte Carlo routines in tree space, and per-position leverage for DNA sequences are included.
A description of the algorithm used for the distance computation can
be found in dist.multiPhylo.
John Chakerian <[email protected]> and Susan Holmes <[email protected]>
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
Billera, L. J., Holmes, S. P., and Vogtmann, K. (2001) Geometry of the space of phylogenetic trees. Advances in Applied Mathematics, 27, 733_-767.
Megan Owen and J. Scott Provan (2010) A fast algorithm for computing geodesic distances in tree space. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14 Jan. 2010.
Generates a Bethe tree with given tips, inner edge lengths, and outgroup.
bethe.tree(tips, level.lengths = NULL, outgroup="O", outgroup.dist=1)bethe.tree(tips, level.lengths = NULL, outgroup="O", outgroup.dist=1)
tips |
A list of tip names as a character vector. Should be a power of 2. All tip names must be distinct. |
level.lengths |
Edge lengths for each level, counted from the bottom up. NULL means a default of 1. If the vector isn't long enough, the last value will be repeated as necessary. |
outgroup |
The tip label for the outgroup. |
outgroup.dist |
The distance of the outgroup from the root. |
Generates a Bethe tree with specified internal edge lengths.
A class of type phylo representing the tree.
John Chakerian
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
plot(bethe.tree(as.character(1:16), 1:4, "17", 14))plot(bethe.tree(as.character(1:16), 1:4, "17", 14))
Bins trees according to branching topology.
bin.multiPhylo(treelist)bin.multiPhylo(treelist)
treelist |
A list of trees that can be passed to dist.phylo (see the help for dist.phylo for acceptable formats). |
Bins trees according to branching topology. Two trees are considered to have the same topology if the same set of partitions of tips are produced by the edges, which corresponds to the same branching up to rearrangement of tips.
Returns a numeric vector of bin ids. Bin ids are assigned in order of the first tree in that bin, that is, the first k unique trees in the list passed will be assigned bins 1..k in order of appearance.
John Chakerian
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
data(woodmouse) otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 500 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE),trees=TRUE) combined.trees <- c(list(otree), trees$trees) bin.multiPhylo(combined.trees)data(woodmouse) otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 500 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE),trees=TRUE) combined.trees <- c(list(otree), trees$trees) bin.multiPhylo(combined.trees)
Computes the geodesic distance of a list of phylogenetic trees using a polynomial algorithm.
dist.multiPhylo(x, method = "geodesic", force.multi2di = FALSE, outgroup = NULL, convert.multifurcating = FALSE, use.random.resolution = FALSE, scale = NULL, verbose = FALSE)dist.multiPhylo(x, method = "geodesic", force.multi2di = FALSE, outgroup = NULL, convert.multifurcating = FALSE, use.random.resolution = FALSE, scale = NULL, verbose = FALSE)
x |
A list of ape trees (class 'phylo'). The list does not have to be of class 'multiPhylo'. The function will also accept a list of strings of trees in Newick format, or a single string with trees in Newick format separated by semicolons. All the trees must have the same tip labels. |
method |
Determines which distance method is used. Options are 'geodesic' for the tree space geodesic distance, or 'edgeset' for the number of edges (defined by splits of tips) that are different. |
force.multi2di |
Force conversion of every tree to strict bifurcating through the ape function 'multi2di', using the use.random.resolution as its parameter. This option should not be used in conjunction with specification of an outgroup. |
outgroup |
Specifies an outgroup to root each tree with respect to. This calls the ape function 'root' on every tree in the list. |
convert.multifurcating |
Setting this option will check every tree for multifurcations using the ape function 'is.binary.phylo' - if it returns FALSE, the ape function 'multi2di' will be called on it. Note that this does not ensure a tree is strictly binary, since ape considers an unrooted tree binary even if the root node is trifurcating. This option can be used in conjunction with specification of an outgroup. |
use.random.resolution |
Specifies the parameter to 'multi2di' if needed. |
scale |
Specifies a scale to make all trees unformly scaled (that is, the sum of
all edges will be uniform)scale to make all trees unformly scaled (that
is, the sum of all edge lengths will be uniform). The parameter can
either be a tree of class |
verbose |
Turns on incremental status updates and more warnings. Helpful for large computations. |
This function computes the geodesic distance according to Billera et. al. using an algorithm based off of the polynomial time algorithm of Owen and Provan. Since it corresponds to a formal definition of tree-space as a space of strictly binary trees, no mulifurcations are allowed, including on the root node. In addition, negative and 0-lengthed edges are clamped to a very small value (DBL_MIN) for technical reasons.
The Newick parser supports only a subset of the Newick format. In particular, it does not at the moment allow for internal node labels, only weights. Weights will be automatically set to 1 if not specified. It may be necessary to clean data in ape to make the trees conform to this.
Returns a distance matrix of class 'dist' representing the pairwise geodesic distances between all input trees. Keep in mind this distance matrix is not Euclidean. N/A values are provided in the case of an error in determining the distance.
John Chakerian
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
Billera, L. J., Holmes, S. P., and Vogtmann, K. (2001) Geometry of the space of phylogenetic trees. Advances in Applied Mathematics, 27, 733–767.
Megan Owen and J. Scott Provan (2010) A fast algorithm for computing geodesic distances in tree space. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14 Jan. 2010.
dist.dna, boot.phylo,
cmdscale
data(woodmouse) otree <- root(nj(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 250 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(nj(dist.dna(x)), "No305", resolve.root=TRUE), trees = TRUE) combined.trees <- c(list(otree), trees$trees) tree.dists <- dist.multiPhylo(combined.trees) mdres <- cmdscale(tree.dists, k=breps, add=TRUE) plot(mdres$points[,1], mdres$points[,2], col = c("red", rep("black", breps))) text(mdres$points[,1], mdres$points[,2], labels = 1:(breps + 1), cex = 0.7, adj = c(0, 2))data(woodmouse) otree <- root(nj(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 250 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(nj(dist.dna(x)), "No305", resolve.root=TRUE), trees = TRUE) combined.trees <- c(list(otree), trees$trees) tree.dists <- dist.multiPhylo(combined.trees) mdres <- cmdscale(tree.dists, k=breps, add=TRUE) plot(mdres$points[,1], mdres$points[,2], col = c("red", rep("black", breps))) text(mdres$points[,1], mdres$points[,2], labels = 1:(breps + 1), cex = 0.7, adj = c(0, 2))
Computes the Gromov Hyperbolicity Constant of a distance matrix.
gromov.hyperbolicity(d, deltas = FALSE, scale = NA)gromov.hyperbolicity(d, deltas = FALSE, scale = NA)
d |
A distance matrix of type |
deltas |
A logical value specifying whether to return the vector of delta values.
Default is |
scale |
Specifies a scaling method for each delta. Default is no scaling (NA or "none"). Available methods are "max" which scales deltas by the max of the sums computed, and "perimeter" for the largest perimeter of the four points. |
This computes a constant that represents the relaxation of a 4-point condition for delta-hyperbolicity. See (Gromov 1987) for details.
The Gromov hyperbolicity constant of the given distance matrix.
John Chakerian
M. Gromov. Hyperbolic groups. In Essays in Group Theory, pages 73–263. Springer, New York, 1987.
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
# scale final delta by max distance points <- cbind(runif(100), runif(100)) d <- dist(points) gromov.hyperbolicity(d)/max(d) # scale each delta by max distance for the 4 points points <- cbind(runif(100), runif(100)) d <- dist(points) gromov.hyperbolicity(d, scale="max") # scale each delta by the max perimeter for the 4 points points <- cbind(runif(100), runif(100)) d <- dist(points) gromov.hyperbolicity(d, scale="max")# scale final delta by max distance points <- cbind(runif(100), runif(100)) d <- dist(points) gromov.hyperbolicity(d)/max(d) # scale each delta by max distance for the 4 points points <- cbind(runif(100), runif(100)) d <- dist(points) gromov.hyperbolicity(d, scale="max") # scale each delta by the max perimeter for the 4 points points <- cbind(runif(100), runif(100)) d <- dist(points) gromov.hyperbolicity(d, scale="max")
mcmc.target.seq uses MCMC to find a configuration of DNA positions
to get as close as possible to a given tree.
boot.samples.idxs bootstraps over indices into a DNA matrix.
lookup.samples goes from an index representation of a configuration
of DNA to the actual DNAbin format.
convert.table.to.idx converts a table of counts for positions 1..n
into a list of indices corresponding to positions (i.e. goes from the
tabled form to a vector whose tabling matches the input).
mcmc.target.seq(data, x, F, n) boot.samples.idxs(data, B = 100, block = 1) lookup.samples(data, idxs) convert.table.to.idx(T)mcmc.target.seq(data, x, F, n) boot.samples.idxs(data, B = 100, block = 1) lookup.samples(data, idxs) convert.table.to.idx(T)
data |
A DNA matrix in DNAbin format. |
x |
A tree of class 'phylo' to estimate. |
F |
A tree estimation function, accepting a DNA matrix in DNAbin format and returning a tree of class 'phylo.' |
n |
The number of MCMC iterations to perform. |
B |
The number of bootstrap replicates. |
block |
The block size to use during bootstrapping. |
idxs |
A list of numeric vectors of indices to use for lookup. |
T |
A table or table-like vector to convert. |
mcmc.target.seq performs an MCMC with simulated annealing to locate
a configuration of DNA positions from the original matrix that gets as
close as possible to a target tree. Propositions for the MCMC replacing
one character with another uniformly at random.
The remaining functions are intended to be used as support functions.
mcmc.target.seq returns a list of 4 elements: a numeric vector of
counts of each position in the original matrix, the best estimated
tree, the best distance from the estimated tree to the target tree, and
a numeric vector of the distances for every iteration of the
simulation.
boot.samples.idxs returns a numeric vector representing the
bootstrapped idices.
lookup.samples returns a list of objects of class DNAbin
corresponding to the DNA sequences generated from indices into the original
DNA matrix.
convert.table.to.idx returns a numeric vector of indices based on
the table counts.
John Chakerian
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
dist.multiPhylo, orthant.boundary.tree
## Not run: ## This example has been excluded from checks: ## copy/paste the code to try it data(woodmouse) otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 200 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE), trees = TRUE) combined.trees <- c(list(otree), trees$trees) binning <- bin.multiPhylo(combined.trees) tree.a <- combined.trees[[match(1, binning)]] i <- 2 max.bin <- max(binning) tree.b <- combined.trees[[match(2, binning)]] while(length(distinct.edges(tree.a,tree.b)) > 1 && i < max.bin) { i = i + 1 tree.b = combined.trees[[match(i, binning)]] } bdy.tree <- orthant.boundary.tree(tree.a, tree.b) f.est <- function(x) root(nj(dist.dna(x)), "No305", resolve.root=TRUE) res <- mcmc.target.seq(woodmouse, bdy.tree, f.est, 1000) par(mfrow=c(2,1)) plot(res$tree) plot(res$vals) ## End(Not run)## Not run: ## This example has been excluded from checks: ## copy/paste the code to try it data(woodmouse) otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 200 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE), trees = TRUE) combined.trees <- c(list(otree), trees$trees) binning <- bin.multiPhylo(combined.trees) tree.a <- combined.trees[[match(1, binning)]] i <- 2 max.bin <- max(binning) tree.b <- combined.trees[[match(2, binning)]] while(length(distinct.edges(tree.a,tree.b)) > 1 && i < max.bin) { i = i + 1 tree.b = combined.trees[[match(i, binning)]] } bdy.tree <- orthant.boundary.tree(tree.a, tree.b) f.est <- function(x) root(nj(dist.dna(x)), "No305", resolve.root=TRUE) res <- mcmc.target.seq(woodmouse, bdy.tree, f.est, 1000) par(mfrow=c(2,1)) plot(res$tree) plot(res$vals) ## End(Not run)
Produces a degenerate tree on the boundary between trees that differ by one split.
orthant.boundary.tree(x,y)orthant.boundary.tree(x,y)
x |
The tree in the first orthant. |
y |
The tree in the second orthant. |
The tree found is the tree on the boundary between the two orthants such that it is on the straight line connecting the two trees when one orthant is thought of as being the (-,+) quadrant and the second orthant as being the (+,+) quadrant, where the (0,y) line is the particular boundary in question.
Returns an object of class 'phylo' representing the boundary tree.
John Chakerian
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
data(woodmouse) otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 200 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE), trees = TRUE) combined.trees <- c(list(otree), trees$trees) binning <- bin.multiPhylo(combined.trees) tree.a <- combined.trees[[match(1, binning)]] i <- 2 max.bin <- max(binning) tree.b <- combined.trees[[match(2, binning)]] while(length(distinct.edges(tree.a,tree.b)) > 1 && i < max.bin) { i = i + 1 tree.b = combined.trees[[match(i, binning)]] } plot(orthant.boundary.tree(tree.a, tree.b))data(woodmouse) otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 200 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE), trees = TRUE) combined.trees <- c(list(otree), trees$trees) binning <- bin.multiPhylo(combined.trees) tree.a <- combined.trees[[match(1, binning)]] i <- 2 max.bin <- max(binning) tree.b <- combined.trees[[match(2, binning)]] while(length(distinct.edges(tree.a,tree.b)) > 1 && i < max.bin) { i = i + 1 tree.b = combined.trees[[match(i, binning)]] } plot(orthant.boundary.tree(tree.a, tree.b))
A family of functions for determining and plotting the differences between two trees.
phylo.diff plots two trees side by side, highlighting edges unique
to each tree in red.
distinct.edges finds the edges present in the first argument not in
the second.
edge.from.split locates the edge id from a given split.
get.bipartition gets the bipartition of tips formed by a single edge.
partition.leaves returns the set of all bipartitions from all edges.
phylo.diff(x, y, ...) distinct.edges(x, y) edge.from.split(x, split) get.bipartition(x, e) partition.leaves(x)phylo.diff(x, y, ...) distinct.edges(x, y) edge.from.split(x, split) get.bipartition(x, e) partition.leaves(x)
x |
The first (or only) tree. |
y |
The second tree, for the functions that accept two trees. |
split |
A list of bipartitions, probably from |
e |
An edge for a particular tree, given as an id. |
... |
Additional arguments to pass to the |
phylo.diff uses the ape tree plotting function. The other functions
are mostly meant as support functions.
phylo.diff returns invisible.
distinct.edges returns a numeric vector of edge ids for the first
tree.
edge.from.split returns an edge id for a particular tree
corresponding to a given bipartition and NA if none such edge exists.
get.bipartition returns a character vector of the tips below that
edge in the given tree.
partition.leaves returns a list of partitions (themselves character
vectors) of the given tree.
John Chakerian
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
data(woodmouse) otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 10 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE), trees = TRUE) combined.trees <- c(list(otree), trees$trees) binning <- bin.multiPhylo(combined.trees) phylo.diff(combined.trees[[match(1, binning)]], combined.trees[[match(2, binning)]])data(woodmouse) otree <- root(fastme.ols(dist.dna(woodmouse)), "No305", resolve.root=TRUE) breps <- 10 trees <- boot.phylo(otree, woodmouse, B=breps, function(x) root(fastme.ols(dist.dna(x)), "No305", resolve.root=TRUE), trees = TRUE) combined.trees <- c(list(otree), trees$trees) binning <- bin.multiPhylo(combined.trees) phylo.diff(combined.trees[[match(1, binning)]], combined.trees[[match(2, binning)]])
Provides a rough heuristic for determining the degree to which each position in the DNA matrix affects the resulting tree.
position.leverage(data, F, to = NULL, rep = 50, by = 1)position.leverage(data, F, to = NULL, rep = 50, by = 1)
data |
A DNA matrix in |
F |
A tree estimation function, accepting a DNA matrix of class |
to |
The tree with which distances are measured in respect to, or |
rep |
The number of times to replicate the position in question. |
by |
The function will perform the calculation on every |
This function takes a DNA matrix and, for every by-th position,
replicates that position rep times, randomly removing
rep other positions to keep all sequences the same length
other positions to keep all sequences the same length. For each new DNA
matrix created in this way, F is used to estimate the corresponding
tree, and the distance to tree to is computed and stored. This
distance can be thought of as somewhat analogous to the leverage of that
position.
Returns a numeric vector of distances from tree to for each position
sampled.
John Chakerian
Chakerian, J. and Holmes, S. P. Computational Tools for Evaluating Phylogenetic and Heirarchical Clustering Trees. arXiv:1006.1015v1.
data(woodmouse) f.est <- function(x) root(nj(dist.dna(x)), "No305", resolve.root = TRUE) position.leverage(woodmouse, f.est, by = 10)data(woodmouse) f.est <- function(x) root(nj(dist.dna(x)), "No305", resolve.root = TRUE) position.leverage(woodmouse, f.est, by = 10)