PhyloDistance: Calculate Distance between Unrooted Phylogenies

In npcooley/SynExtend: Tools for Working With Synteny Objects

Calculate Distance between Unrooted Phylogenies

Description

Calculates distance between two unrooted phylogenies using a variety of metrics.

Usage

PhyloDistance(dend1, dend2,
              Method=c("CI", "RF", "KF", "JRF"), 
              RawScore=FALSE, JRFExp=2)

Arguments

dend1	An object of class dendrogram, representing an unrooted bifurcating phylogenetic tree.
dend2	An object of class dendrogram, representing an unrooted bifurcating phylogenetic tree.
Method	Method to use for calculating tree distances. The following values are supported: "CI", "RF", "KF", "JRF". See Details for more information.
RawScore	If FALSE, returns distance between the two trees. If TRUE, returns the component values used to calculate the distance. This may be preferred for methods like GRF. See the pages specific to each algorithm for more information on what values are reported.
JRFExp	k-value used in calculation of JRF Distance. Unused if Method is not "JRF".

Details

This function implements a variety of tree distances, specified by the value of Method. The following values are supported, along with links to documentation pages for each function:

• "RF": Robinson-Foulds Distance

• "CI": Clustering Information Distance

• "JRF": Jaccard-Robinson-Foulds Distance, equivalent to the Nye Distance Metric when JRFVal=1

• "KF": Kuhner-Felsenstein Distance

Information on each of these algorithms, how scores are calculated, and references to literature can be found at the above links. Method "CI" is selected by default due to recent work showing this method as the most robust tree distance metric under general conditions.

Value

Returns a normalized distance, with 0 indicating identical trees and 1 indicating maximal difference. If the trees have no leaves in common, the function will return 1.

If RawScore=TRUE, returns a vector of the components used to calculate the distance. This is typically a length 3 vector, but specific details can be found on the description for each algorithm.

Note

Note that this function requires the input dendrograms to be labeled alike (ex. leaf labeled abc in dend1 represents the same species as leaf labeled abc in dend2). Labels can easily be modified using dendrapply.

Author(s)

Aidan Lakshman [e-mail]

See Also

Robinson-Foulds Distance

Clustering Information Distance

Jaccard-Robinson-Foulds Distance

Kuhner-Felsenstein Distance

Examples

# making some toy dendrograms
set.seed(123)
dm1 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8))
dm2 <- as.dist(matrix(runif(64, 0.5, 5), ncol=8))

tree1 <- as.dendrogram(hclust(dm1))
tree2 <- as.dendrogram(hclust(dm2))

# Robinson-Foulds Distance
PhyloDistance(tree1, tree2, Method="RF")

# Clustering Information Distance
PhyloDistance(tree1, tree2, Method="CI")

# Kuhner-Felsenstein Distance
PhyloDistance(tree1, tree2, Method="KF")

# Nye Distance Metric
PhyloDistance(tree1, tree2, Method="JRF", JRFExp=1)

# Jaccard-Robinson-Foulds Distance
PhyloDistance(tree1, tree2, Method="JRF", JRFExp=2)

npcooley/SynExtend documentation built on May 2, 2024, 7:28 p.m.