系统发育树空间中主测地线的构造算法
An algorithm for constructing principal geodesics in phylogenetic treespace
摘要：
大多数系统发育分析的结果是一个样本的树，但总结和可视化这些样本可能是具有挑战性的。共识树通常提供关于样本的有限信息，因此已经开发出诸如共识网络、聚类和多维缩放等方法并应用于树样本。本文描述了一种构造主测地线或穿过树空间的线的随机算法，它类似于标准主成分分析中的第一主成分。主测地线总结了树的样本的最可变的特征，在树的拓扑结构和分支长度方面，它可以被可视化为平滑变化的树的动画。该算法通过参数空间进行随机搜索，以最小化数据点的平方投影距离之和。这个过程的目的是确定全球最优的主测地线，虽然收敛到局部最优测地线是可能的。该方法是通过构建实验和模拟数据集的主测地线来说明的，展示了可以获得的树木样本的洞察力，以及该方法如何改进以前发表的方法。可以从www.ncl.ac.uk/~ntmwn/geophytter免费获得一个名为GeoPhytter的Java软件包，用于构建和可视化主要测地线。
英文摘要：
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 aims to identify the globally optimal principal geodesic, though convergence to locally optimal geodesics is possible. The methodology is illustrated by constructing principal geodesics for experimental and simulated data sets, demonstrating the insight into samples of trees that can be gained and how the method improves on a previously published approach. A java package called GeoPhytter for constructing and visualising principal geodesics is freely available from www.ncl.ac.uk/~ntmwn/geophytter.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
