Principled, practical, flexible, fast: A new approach to phylogenetic factor analysis. Issue 10 (1st August 2022)

• Record Type: Journal Article
• Authors: Hassler, Gabriel W.; Gallone, Brigida; Aristide, Leandro; Allen, William L.; Tolkoff, Max R.; Holbrook, Andrew J.; Baele, Guy; Lemey, Philippe; Suchard, Marc A.
• Is Part Of: Methods in ecology and evolution. Volume 13:Issue 10(2022)
• Page Start: 2181
• Page End: 2197
• Publication Date: 2022-08-01
• Subjects: Bayesian inference -- BEAST -- latent factor model -- Geodesic Hamiltonian Monte Carlo -- phylogenetic comparative methods -- Stiefel manifold; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.13920
• Languages: English
• ISSNs: 2041-210X

Abstract

Abstract: 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 modelling framework confront numerous modelling and implementation decisions, the details of which pose computational and replicability challenges. We develop new inference techniques that increase both the computational efficiency and modelling 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 modelling decisions. We codify this analysis plan in an automated pipeline that distils 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.