An algorithm for constructing principal geodesics in phylogenetic treespace
Tom M. W. Nye

TL;DR
This paper introduces a stochastic algorithm to construct principal geodesics in treespace, enabling better summarization and visualization of phylogenetic tree samples by capturing their most variable features.
Contribution
It presents a novel stochastic method for identifying principal geodesics in treespace, improving on previous approaches for analyzing phylogenetic tree samples.
Findings
Successfully constructed principal geodesics for experimental data
Demonstrated improved insights over previous methods
Provided a freely available Java package for implementation
Abstract
Most phylogenetic analyses result in a sample of trees, but summarizing and visualizing these samples can be challenging. Consensus trees often provide limited information about a sample, and so methods such as consensus networks, clustering and multidimensional scaling have been developed and applied to tree samples. This paper describes a stochastic algorithm for constructing a principal geodesic or line through treespace which is analogous to the first principal component in standard Principal Components Analysis. A principal geodesic summarizes the most variable features of a sample of trees, in terms of both tree topology and branch lengths, and it can be visualized as an animation of smoothly changing trees. The algorithm performs a stochastic search through parameter space for a geodesic which minimises the sum of squared projected distances of the data points. This procedure…
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