Phylogenetically aligned component analysis
Phylogenetically aligned component analysis
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DOI：
10.1111/2041-210x.13515
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发表时间：
2020-11-04
影响因子：
6.6
通讯作者：
Adams, Dean C.
中科院分区：
文献类型：
--
作者：
Collyer, Michael L.;Adams, Dean C.
在进化生物学中，多变量表征表型已经变得很常见。然而，在多变量数据集中可视化宏观进化趋势需要适当的协调方法。在本文中，我们描述了系统发育对齐成分分析（PACA）：一种新的排序方法，将表型数据与系统发育信号对齐。不同于系统发育主成分分析（phylogenetic principal component analysis, Phy-PCA），它找到一个独立于系统发育信号的主特征向量的对齐，PACA最大化了描述系统发育信号方向的变化，同时保留了数据空间中观测值之间的欧几里得距离。我们通过模拟和经验例子证明，使用PACA，可以在多元数据空间中可视化系统发育信号的趋势，而不考虑数据中的其他信号。结合Phy-PCA，可以可视化系统发育信号和独立于系统发育信号的数据趋势。系统发育对齐成分分析可以区分弱系统发育信号和强信号，这些信号只集中在所有数据维度的一部分。我们提供了一些实证例子来强调这种差异。因此，在系统发育信号的研究中使用PACA应该能够更精确地描述系统发育信号。总的来说，无论数据中的其他信号如何，PACA将返回显示前几个成分中最多系统发育信号的投影。通过比较Phy-PCA和PACA结果，人们可以收集数据中系统发育和其他（生态）信号的相对重要性。
It has become common in evolutionary biology to characterize phenotypes multivariately. However, visualizing macroevolutionary trends in multivariate datasets requires appropriate ordination methods.In this paper we describe phylogenetically aligned component analysis (PACA): a new ordination approach that aligns phenotypic data with phylogenetic signal. Unlike phylogenetic principal component analysis (Phy-PCA), which finds an alignment of a principal eigenvector that is independent of phylogenetic signal, PACA maximizes variation in directions that describe phylogenetic signal, while simultaneously preserving the Euclidean distances among observations in the data space.We demonstrate with simulated and empirical examples that with PACA, it is possible to visualize the trend in phylogenetic signal in multivariate data spaces, irrespective of other signals in the data. In conjunction with Phy-PCA, one can visualize both phylogenetic signal and trends in data independent of phylogenetic signal.Phylogenetically aligned component analysis can distinguish between weak phylogenetic signals and strong signals concentrated in only a portion of all data dimensions. We provide empirical examples that emphasize the difference. Use of PACA in studies focused on phylogenetic signal should enable much more precise description of the phylogenetic signal, as a result.Overall, PACA will return a projection that shows the most phylogenetic signal in the first few components, irrespective of other signals in the data. By comparing Phy-PCA and PACA results, one may glean the relative importance of phylogenetic and other (ecological) signals in the data.