Confidence sets for phylogenetic trees

TL;DR

This paper develops a new statistical method to construct confidence sets for phylogenetic trees, addressing the complexity of their non-Euclidean space and enabling more rigorous evolutionary inference.

Contribution

It unifies recent computational and probabilistic advances to create tree--valued confidence sets that account for variability in multiple directions.

Findings

Improved testing of phylogenetic hypotheses using block replicates.

Application to Zika virus ancestor identification.

Formal testing of HIV transmission hypotheses.

Abstract

Inferring evolutionary histories (phylogenetic trees) has important applications in biology, criminology and public health. However, phylogenetic trees are complex mathematical objects that reside in a non-Euclidean space, which complicates their analysis. While our mathematical, algorithmic, and probabilistic understanding of phylogenies in their metric space is mature, rigorous inferential infrastructure is as yet undeveloped. In this manuscript we unify recent computational and probabilistic advances to construct tree--valued confidence sets. The procedure accounts for both centre and multiple directions of tree--valued variability. We draw on block replicates to improve testing, identifying the best supported most recent ancestor of the Zika virus, and formally testing the hypothesis that a Floridian dentist with AIDS infected two of his patients with HIV. The method illustrates connections between variability in Euclidean and tree space, opening phylogenetic tree analysis to techniques available in the multivariate Euclidean setting.