TreeShrink: Efficient Detection of Outlier Tree Leaves

摘要

Phylogenetic trees include errors for a variety of reasons. We argue that one way to detect errors is to build a phylogeny with all the data then detect taxa that artificially inflate the tree diameter. We formulate an optimization problem that seeks to find k leaves that can be removed to reduce the tree diameter maximally. We present a polynomial time solution to this "k-shrink" problem. Given this solution, we then use non-parametric statistics to find an outlier set of taxa that have an unexpectedly high impact on the tree diameter. We test our method, TreeShrink, on five biological datasets, and show that it is more conservative than rogue taxon removal using RogueNaRok. When the amount of filtering is controlled, TreeShrink outperforms RogueNaRok in three out of the five datasets, and they tie in another dataset.