Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference
Using Parsimony-Guided Tree Proposals to Accelerate Convergence in Bayesian Phylogenetic Inference
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DOI：
10.1093/sysbio/syaa002
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发表时间：
2020-09-01
影响因子：
6.5
通讯作者：
Ronquist, Fredrik
中科院分区：
文献类型：
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作者：
Zhang, Chi;Huelsenbeck, John P.;Ronquist, Fredrik
树空间采样是马尔可夫链蒙特卡罗（MCMC）算法在贝叶斯系统发育推断中面临的主要挑战之一。标准MCMC树移动考虑拓扑的小的随机扰动，并且随机地或基于新旧拓扑之间的距离从候选树中选择。使用这种移动的MCMC算法往往会被困在树空间中，使得它们在找到全局最可能的树（称为“收敛”）和估计不同类型树的正确比例（称为“混合”）方面很慢。在这里，我们引入了一类新的移动，提出树的基础上，他们的简约分数。从简约分数中得到的建议分布是候选树上条件后验分布的快速可计算的粗略近似。我们证明了与模拟，简约引导的移动正确采样的拓扑结构的均匀分布从以前的。然后，我们使用六个具有挑战性的经验数据集，我们能够获得准确的参考估计后，使用长MCMC运行，拓扑结构的建议，和大都会耦合的标准动作，评估其性能。在这些数据集上，大小从357到934个分类群，从1740到5681个站点，我们发现使用简约引导移动的单链通常比使用标准移动的链收敛快一个数量级。它们也表现出更好的混合，也就是说，它们更快地覆盖最可能的树。我们的研究结果表明，基于快速和肮脏的后验概率估计的树移动可以显着优于标准移动。未来的研究将不得不表明，通过找到更好的方法来近似后验概率，并考虑到准确性和速度之间的权衡，可以在多大程度上进一步提高这种移动的性能。
Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as "convergence") and in estimating the correct proportions of the different types of them (known as "mixing"). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical data sets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these data sets, ranging in size from 357 to 934 taxa and from 1740 to 5681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account.