Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO

Cheng Zhang, Vu Dinh and Frederick A. Matsen

Journal of the American Statistical Association, 2021, vol. 116, issue 534, 858-873

Abstract: Phylogenetic tree inference using deep DNA sequencing is reshaping our understanding of rapidly evolving systems, such as the within-host battle between viruses and the immune system. Densely sampled phylogenetic trees can contain special features, including sampled ancestors in which we sequence a genotype along with its direct descendants, and polytomies in which multiple descendants arise simultaneously. These features are apparent after identifying zero-length branches in the tree. However, current maximum-likelihood based approaches are not capable of revealing such zero-length branches. In this article, we find these zero-length branches by introducing adaptive-LASSO-type regularization estimators for the branch lengths of phylogenetic trees, deriving their properties, and showing regularization to be a practically useful approach for phylogenetics. Supplementary materials for this article are available online.

Date: 2021
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DOI: 10.1080/01621459.2020.1778481

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