Nonbifurcating Phylogenetic Tree Inference via the Adaptive LASSO

• 成果类型: Article
• 署名作者: Zhang, Cheng; Dinh, Vu; Matsen, Frederick A.
• 署名单位: Peking University; Peking University; University of Delaware; Fred Hutchinson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2020.1778481
• 发表日期: 2021
• 页码: 858-873
• 关键词: nonconcave penalized likelihood; evolutionary trees; variable selection; Sparse Estimation; MODEL; Consistency; RECOVERY; Identifiability; polytomies; regression

摘要

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.