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Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO

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  • Cheng Zhang
  • Vu Dinh
  • Frederick A. Matsen

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.

Suggested Citation

  • Cheng Zhang & Vu Dinh & Frederick A. Matsen, 2021. "Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 858-873, April.
  • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:534:p:858-873
    DOI: 10.1080/01621459.2020.1778481
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