论文标题
合奏变量选择的进化移位检测
Evolutionary shift detection with ensemble variable selection
论文作者
论文摘要
1。突然的环境变化会导致性状演变的进化转移。识别这些转变是理解表型进化史的重要步骤。 2。我们为进化移位检测任务提出了一种集合变量选择方法（R软件包ELPASO），并将其与几种情况下的现有方法（R packages l1ou和系统发育原理）进行比较。 3。方法的性能高度取决于选择标准。当信号大小很小时，使用贝叶斯信息标准（BIC）的方法具有更好的性能。当信号大小足够大时，使用系统发育信息标准（PBIC）（Khabbazian等，2016）的方法具有更好的性能。此外，性能受到测量误差和树木重建误差的严重影响。 4。集合方法 + PBIC的表现往往比L1OU + PBIC保守，而Ensemble方法 + BIC比L1OU + BIC保守。系统发育剂更加保守，具有小信号大小，并且在L1OU + PBIC和ENSEMELM方法 + BIC之间落在具有较大信号大小的BIC之间。这些方法之间的结果可能会有所不同，但是没有一个明显优于其他方法。通过将多种方法应用于单个数据集，我们可以根据方法之间的协议访问每个检测到的移位的鲁棒性。
1. Abrupt environmental changes can lead to evolutionary shifts in trait evolution. Identifying these shifts is an important step in understanding the evolutionary history of phenotypes. 2. We propose an ensemble variable selection method (R package ELPASO) for the evolutionary shift detection task and compare it with existing methods (R packages l1ou and PhylogeneticEM) under several scenarios. 3. The performances of methods are highly dependent on the selection criterion. When the signal sizes are small, the methods using the Bayesian information criterion (BIC) have better performances. And when the signal sizes are large enough, the methods using the phylogenetic Bayesian information criterion (pBIC) (Khabbazian et al., 2016) have better performance. Moreover, the performance is heavily impacted by measurement error and tree reconstruction error. 4. Ensemble method + pBIC tends to perform less conservatively than l1ou + pBIC, and Ensemble method + BIC is more conservatively than l1ou + BIC. PhylogeneticEM is even more conservative with small signal sizes and falls between l1ou + pBIC and Ensemble method + BIC with large signal sizes. The results can differ between the methods, but none clearly outperforms the others. By applying multiple methods to a single dataset, we can access the robustness of each detected shift, based on the agreement among methods.
