An improved framework for detecting discrete epidemiologically meaningful partitions in hierarchically clustered genetic data.

Motivation: Hierarchical clustering of microbial genotypes has the limitation that hierarchical clusters are nested, where smaller groups of related isolates exist within larger groups that get progressively larger as relationships become increasingly distant. In an epidemiologic context, investigators must dissect hierarchical trees into discrete groupings that are epidemiologically meaningful. We recently described a statistical framework (Method A) for dissecting hierarchical trees that attempts to minimize investigator bias. Here, we apply a modified version of that framework (Method B) to a hierarchical tree constructed from 2111 genotypes of the foodborne parasite Results: Compared to the other methods, Method B, TreeCluster, and PARNAS were the most accurate (99.4%) in identifying genetic groups that reflected the epidemiologic groupings, noting that TreeCluster and PARNAS performed identically on our dataset. CutreeHybrid identified groups reflecting patterns in the wider Availability and implementation: Relevant code and data are publicly available. Source code (Method B) and instructions for its use are available here: https://github.com/Joel-Barratt/Hierarchical-tree-dissection-framework.