Data from: Practical performance of tree comparison metrics

The phylogenetic literature contains numerous measures for assessing differences between two phylogenetic trees. Individual measures have been criticized on various grounds, but little is known about their comparative performance in typical applications. We evaluate the performance of nine tree distance measures on two tasks: (1) distinguishing trees separated by lesser versus greater numbers of recombinations, and (2) distinguishing trees inferred with lower versus higher quality data. We find that when the trees being compared are similar, measures which make use of branch lengths are superior, with the branch-length version of the Robinson-Foulds metric (Robinson & Foulds, 1979) performing best. In contrast, for dissimilar trees topology-only measures are superior, with the Alignment metric of Nye et al. (2006) performing best. We also apply the measures to a mammalian data set and observe that the best metric depends on whether branch-length information is of interest. We give practical recommendations for choosing a tree distance metric in different applications.系统发育学文献中已提出诸多用于评估两棵系统发育树之间差异的度量方法。各类单一度量方法均曾因多种依据受到诟病，但目前学界对其在典型应用场景中的相对性能仍知之甚少。本研究针对两项任务评估了九种树距离度量的性能：（1）区分经不同数量重组事件分化的系统发育树；（2）区分基于不同质量数据推断得到的系统发育树。研究发现，当待比较的树相似度较高时，利用分支长度信息的度量方法表现更优，其中罗宾逊-福尔兹度量（Robinson-Foulds metric）的分支长度变体（Robinson & Foulds, 1979）性能最佳。与之相对，当待比较的树差异较大时，仅利用拓扑结构的度量方法表现更优，其中Nye等人（2006）提出的对齐度量（Alignment metric）性能最佳。此外，我们将这些度量方法应用于一套哺乳动物数据集，结果显示最优度量的选择取决于是否需要使用分支长度信息。最后，我们针对不同应用场景下如何选择树距离度量方法给出了实用建议。