Automation and Evaluation of the SOWH Test with SOWHAT.
Automation and Evaluation of the SOWH Test with SOWHAT.
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DOI：
10.1093/sysbio/syv055
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发表时间：
2015-11
影响因子：
6.5
通讯作者：
Dunn CW
中科院分区：
文献类型：
--
作者：
Church SH;Ryan JF;Dunn CW
Swofford-Olsen-Waddell-Hillis (SOWH) 检验评估不一致的系统发育拓扑的统计支持。它通常用于确定系统发育分析中的最大似然树是否与备择假设显着不同。 SOWH 测试将两种拓扑之间观察到的对数似然差异与参数重采样生成的对数似然差异零分布进行比较。该测试是一种行之有效的拓扑测试系统发育方法，但它对模型错误指定很敏感，执行起来计算量很大，并且其实施需要研究者做出多个决定，每个决定都有可能影响测试的结果。我们使用之前应用过 SOWH 测试的七个数据集分析了多个因素的影响。这些因素包括大量样本重复、似然软件、在模拟数据中引入间隙、使用不同的进化模型进行数据模拟和似然推断，以及建议的测试修正，其中使用未解析的“零约束”树来模拟序列数据。为了促进这些分析和 SOWH 测试的未来应用，我们编写了 SOWHAT，一个自动执行 SOWH 测试的程序。我们发现不充分的引导抽样可能会改变 SOWH 测试的结果。结果还表明，使用零约束树进行数据模拟可以导致更宽的零分布和更高的 p 值，但不会改变此处测试的大多数数据集的 SOWH 测试的结果。这些结果将帮助其他人实施和评估 SOWH 测试，并使我们能够为 SOWH 测试的未来应用提供建议。 SOWHAT 可以从 https://github.com/josephryan/SOWHAT 下载。
The Swofford–Olsen–Waddell–Hillis (SOWH) test evaluates statistical support for incongruent phylogenetic topologies. It is commonly applied to determine if the maximum likelihood tree in a phylogenetic analysis is significantly different than an alternative hypothesis. The SOWH test compares the observed difference in log-likelihood between two topologies to a null distribution of differences in log-likelihood generated by parametric resampling. The test is a well-established phylogenetic method for topology testing, but it is sensitive to model misspecification, it is computationally burdensome to perform, and its implementation requires the investigator to make several decisions that each have the potential to affect the outcome of the test. We analyzed the effects of multiple factors using seven data sets to which the SOWH test was previously applied. These factors include a number of sample replicates, likelihood software, the introduction of gaps to simulated data, the use of distinct models of evolution for data simulation and likelihood inference, and a suggested test correction wherein an unresolved “zero-constrained” tree is used to simulate sequence data. To facilitate these analyses and future applications of the SOWH test, we wrote SOWHAT, a program that automates the SOWH test. We find that inadequate bootstrap sampling can change the outcome of the SOWH test. The results also show that using a zero-constrained tree for data simulation can result in a wider null distribution and higher p-values, but does not change the outcome of the SOWH test for most of the data sets tested here. These results will help others implement and evaluate the SOWH test and allow us to provide recommendations for future applications of the SOWH test. SOWHAT is available for download from https://github.com/josephryan/SOWHAT.