Statistical summaries of unlabelled evolutionary trees
Statistical summaries of unlabelled evolutionary trees
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DOI：
10.1093/biomet/asad025
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发表时间：
2023-06-23
期刊：
影响因子：
2.7
通讯作者：
Palacios，Julia A.
中科院分区：
文献类型：
--
作者：
Samyak，Rajanala;Palacios，Julia A.
有根和排序的系统发育树是数学对象，可用于建模分层数据和进化关系，并应用于进化生物学和遗传流行病学等许多领域。贝叶斯系统发育推断通常通过马尔可夫链蒙特卡罗方法探索树的后验分布。然而，对于这些类型的结构来说，评估不确定性和总结分布仍然具有挑战性。虽然标记的系统发育树已被广泛研究，但关于未标记的树的文献相对较少，但越来越有用，例如，当人们试图总结用不同方法或从不同样本和环境获得的树样本，并希望评估这些摘要的稳定性和普遍性时。在我们的论文中，我们利用最近提出的未标记排序二叉树和未标记排序谱系或配备分支长度的树的距离度量来定义 Fréchet 均值、方差和四分位数集作为这些树分布的摘要。我们提供了一种有效的组合优化算法，用于计算样本的 Fréchet 平均值或未标记的排名树形状和未标记的排名谱系的分布。我们展示了我们的汇总统计数据对于研究流行树分布以及比较 2020 年 COVID-19 流行期间不同地点的 SARS-CoV-2 进化树的适用性。我们当前的实现可在 https://github.com/RSamyak/fmatrix 上公开获得。
Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, relatively less literature exists for unlabelled trees that are increasingly useful, for example when one seeks to summarize samples of trees obtained with different methods, or from different samples and environments, and wishes to assess the stability and generalizability of these summaries. In our paper, we exploit recently proposed distance metrics of unlabelled ranked binary trees and unlabelled ranked genealogies, or trees equipped with branch lengths, to define the Fréchet mean, variance and interquartile sets as summaries of these tree distributions. We provide an efficient combinatorial optimization algorithm for computing the Fréchet mean of a sample or of distributions on unlabelled ranked tree shapes and unlabelled ranked genealogies. We show the applicability of our summary statistics for studying popular tree distributions and for comparing the SARS-CoV-2 evolutionary trees across different locations during the COVID-19 epidemic in 2020. Our current implementations are publicly available at https://github.com/RSamyak/fmatrix.