Quantifying MCMC Exploration of Phylogenetic Tree Space

TL;DR

This paper investigates how quickly phylogenetic MCMC converges to the posterior distribution using the SPR metric, revealing topological peaks, MCMC traversal challenges, and limitations of CCD in real data analysis.

Contribution

It introduces a graph-based approach to analyze phylogenetic tree posteriors using the SPR metric, demonstrating the presence of topological peaks and MCMC traversal issues.

Findings

Topological peaks occur in Bayesian phylogenetic posteriors.

MCMC struggles to efficiently traverse valleys between peaks.

Conditional clade distribution can be systematically biased with multiple peaks.

Abstract

In order to gain an understanding of the effectiveness of phylogenetic Markov chain Monte Carlo (MCMC), it is important to understand how quickly the empirical distribution of the MCMC converges to the posterior distribution. In this paper we investigate this problem on phylogenetic tree topologies with a metric that is especially well suited to the task: the subtree prune-and-regraft (SPR) metric. This metric directly corresponds to the minimum number of MCMC rearrangements required to move between trees in common phylogenetic MCMC implementations. We develop a novel graph-based approach to analyze tree posteriors and find that the SPR metric is much more informative than simpler metrics that are unrelated to MCMC moves. In doing so we show conclusively that topological peaks do occur in Bayesian phylogenetic posteriors from real data sets as sampled with standard MCMC approaches, investigate the efficiency of Metropolis-coupled MCMC (MCMCMC) in traversing the valleys between peaks, and show that conditional clade distribution (CCD) can have systematic problems when there are multiple peaks.