Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis.
Principled, practical, flexible, fast: a new approach to phylogenetic factor analysis.
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原则、实用、灵活、快速：一种新的系统发育因子分析方法。
DOI：
10.1111/2041-210x.13920
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发表时间：
2022-10
影响因子：
6.6
通讯作者：
中科院分区：
文献类型：
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作者：
生物表型是复杂进化过程的产物，其中选择力以未知的方式影响多个生物性状测量。系统发育比较方法试图在一组生物的进化历史中解开这些关系。然而，现有的方法不能处理高维数据，每个分类单元需要几十个甚至几千个观测值，系统发育因子分析为解决高维数据的问题提供了一个有效的方法。然而，科学家寻求采用这种建模框架面临着许多建模和实施决策，其中的细节构成计算和可复制性的挑战。我们开发了新的推理技术，提高了系统发育因子分析的计算效率和建模灵活性。为了便于采用这些新方法，我们提出了一个实用的分析计划，指导研究人员通过复杂的建模决策的网络。我们将这个分析计划编入一个自动化的管道中，该管道将潜在的压倒性决策组合提炼成少数（通常是二进制）选择。我们证明了这些方法和分析计划在不同规模的四个现实世界中的问题的效用。具体来说，我们研究花的表型和授粉，在工业酵母驯化，在哺乳动物的生活史，在新世界的猴子和脑形态。这些方法的一般和有效的社区应用需要一个数据科学分析计划，该计划平衡了灵活性，速度和易用性，同时最大限度地减少模型和算法调整。即使在存在非平凡的系统发育模型的限制，我们表明，可以分析解决潜在的因素的不确定性的方式，（a）艾滋病模型的灵活性，（B）加速计算（多达500倍）和（c）减少所需的调整。这些努力结合起来，创造一个访问贝叶斯方法，高维系统发育比较方法的大树。
Biological phenotypes are products of complex evolutionary processes in which selective forces influence multiple biological trait measurements in unknown ways. Phylogenetic comparative methods seek to disentangle these relationships across the evolutionary history of a group of organisms. Unfortunately, most existing methods fail to accommodate high-dimensional data with dozens or even thousands of observations per taxon. Phylogenetic factor analysis offers a solution to the challenge of dimensionality. However, scientists seeking to employ this modeling framework confront numerous modeling and implementation decisions, the details of which pose computational and replicability challenges. We develop new inference techniques that increase both the computational efficiency and modeling flexibility of phylogenetic factor analysis. To facilitate adoption of these new methods, we present a practical analysis plan that guides researchers through the web of complex modeling decisions. We codify this analysis plan in an automated pipeline that distills the potentially overwhelming array of decisions into a small handful of (typically binary) choices. We demonstrate the utility of these methods and analysis plan in four real-world problems of varying scales. Specifically, we study floral phenotype and pollination in columbines, domestication in industrial yeast, life history in mammals, and brain morphology in New World monkeys. General and impactful community employment of these methods requires a data scientific analysis plan that balances flexibility, speed and ease of use, while minimizing model and algorithm tuning. Even in the presence of non-trivial phylogenetic model constraints, we show that one may analytically address latent factor uncertainty in a way that (a) aids model flexibility, (b) accelerates computation (by as much as 500-fold) and (c) decreases required tuning. These efforts coalesce to create an accessible Bayesian approach to high-dimensional phylogenetic comparative methods on large trees.