Accurate Bayesian phylogenetic point estimation using a tree distribution parameterized by clade probabilities.

IF 3.6 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2025-02-13 eCollection Date: 2025-02-01 DOI:10.1371/journal.pcbi.1012789

Lars Berling, Jonathan Klawitter, Remco Bouckaert, Dong Xie, Alex Gavryushkin, Alexei J Drummond

引用次数: 0

Abstract

Bayesian phylogenetic analysis with MCMC algorithms generates an estimate of the posterior distribution of phylogenetic trees in the form of a sample of phylogenetic trees and related parameters. The high dimensionality and non-Euclidean nature of tree space complicates summarizing the central tendency and variance of the posterior distribution in tree space. Here we introduce a new tractable tree distribution and associated point estimator that can be constructed from a posterior sample of trees. Through simulation studies we show that this point estimator performs at least as well and often better than standard methods of producing Bayesian posterior summary trees. We also show that the method of summary that performs best depends on the sample size and dimensionality of the problem in non-trivial ways.

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精确的贝叶斯系统发育点估计使用树分布参数化的进化枝概率。

使用MCMC算法的贝叶斯系统发育分析以系统发育树样本和相关参数的形式对系统发育树的后验分布进行估计。树空间的高维性和非欧几里得性质使总结树空间中后验分布的集中趋势和方差变得复杂。在这里，我们引入了一种新的易于处理的树分布和相关的点估计量，它可以由树的后验样本构造。通过仿真研究，我们表明，这种点估计器的性能至少与产生贝叶斯后验总结树的标准方法一样好，而且往往更好。我们还表明，表现最好的总结方法以非平凡的方式取决于问题的样本量和维度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.