Survey of branch support methods demonstrates accuracy, power, and robustness of fast likelihood-based approximation schemes.
Survey of branch support methods demonstrates accuracy, power, and robustness of fast likelihood-based approximation schemes.
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DOI：
10.1093/sysbio/syr041
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发表时间：
2011-10
影响因子：
6.5
通讯作者：
Gascuel O
中科院分区：
文献类型：
--
作者：
Anisimova M;Gil M;Dufayard JF;Dessimoz C;Gascuel O
系统发育推断和评估推断关系的支持是许多研究的核心测试进化假说。尽管非参数自举频率和贝叶斯后验概率的流行，这些措施的树分支支持的解释仍然是一个讨论的来源。此外，这两种方法在计算上都是昂贵的，并且对于大数据集变得令人望而却步。最近的快速近似似然为基础的措施分支支持（近似似然比测试[aLRT]和Shimodaira-Hasegawa [SH]-aLRT）提供了一个令人信服的替代这些较慢的传统方法，不仅提供速度优势，但也很好的精度和功率水平。在这里，我们提出了一个额外的方法：aLRT（aBayes）的类贝叶斯变换。考虑到概率和频率框架，我们比较了三个快速的基于似然的方法与标准引导（SBS），贝叶斯方法，和最近推出的快速引导的性能。我们的模拟和真实的数据分析表明，与中度模型违规，所有的测试是足够准确的，但aLRT和aBayes提供了最高的统计能力，是非常快的。在严重违反模型的情况下，aLRT、aBayes和贝叶斯后验可能会产生较高的假阳性率。对于可以检测到这种违规的数据集，我们建议使用SH-aLRT，这是基于类似于Shimodaira-Hasegawa树选择的程序的aLRT的非参数版本。一般来说，SBS似乎过于保守，比我们的近似似然方法慢得多。
Phylogenetic inference and evaluating support for inferred relationships is at the core of many studies testing evolutionary hypotheses. Despite the popularity of nonparametric bootstrap frequencies and Bayesian posterior probabilities, the interpretation of these measures of tree branch support remains a source of discussion. Furthermore, both methods are computationally expensive and become prohibitive for large data sets. Recent fast approximate likelihood-based measures of branch supports (approximate likelihood ratio test [aLRT] and Shimodaira–Hasegawa [SH]-aLRT) provide a compelling alternative to these slower conventional methods, offering not only speed advantages but also excellent levels of accuracy and power. Here we propose an additional method: a Bayesian-like transformation of aLRT (aBayes). Considering both probabilistic and frequentist frameworks, we compare the performance of the three fast likelihood-based methods with the standard bootstrap (SBS), the Bayesian approach, and the recently introduced rapid bootstrap. Our simulations and real data analyses show that with moderate model violations, all tests are sufficiently accurate, but aLRT and aBayes offer the highest statistical power and are very fast. With severe model violations aLRT, aBayes and Bayesian posteriors can produce elevated false-positive rates. With data sets for which such violation can be detected, we recommend using SH-aLRT, the nonparametric version of aLRT based on a procedure similar to the Shimodaira–Hasegawa tree selection. In general, the SBS seems to be excessively conservative and is much slower than our approximate likelihood-based methods.
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