Uncertainty in phylogenetic tree estimates

TL;DR

This paper presents a method to explicitly model and visualize the uncertainty in phylogenetic tree estimates, aiding biologists in distinguishing meaningful differences from estimation errors.

Contribution

It introduces a novel approach for modeling multivariate uncertainty in phylogenetic trees, improving visualization and interpretability in high-dimensional tree space.

Findings

Uncertainty modeling enhances understanding of gene tree differences.

The method improves visualization speed and reproducibility.

It helps determine if differences are biologically meaningful.

Abstract

Estimating phylogenetic trees is an important problem in evolutionary biology, environmental policy and medicine. Although trees are estimated, their uncertainties are discarded by mathematicians working in tree space. Here we explicitly model the multivariate uncertainty of tree estimates. We consider both the cases where uncertainty information arises extrinsically (through covariate information) and intrinsically (through the tree estimates themselves). The importance of accounting for tree uncertainty in tree space is demonstrated in two case studies. In the first instance, differences between gene trees are small relative to their uncertainties, while in the second, the differences are relatively large. Our main goal is visualization of tree uncertainty, and we demonstrate advantages of our method with respect to reproducibility, speed and preservation of topological differences compared to visualization based on multidimensional scaling. The proposal highlights that phylogenetic trees are estimated in an extremely high-dimensional space, resulting in uncertainty information that cannot be discarded. Most importantly, it is a method that allows biologists to diagnose whether differences between gene trees are biologically meaningful, or due to uncertainty in estimation.