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

ABSTRACT:

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 Cyclospora, including 639 genotypes linked to epidemiologically defined outbreaks. To evaluate Method B's p

SUBMITTER: Jacobson DK 

PROVIDER: S-EPMC10517639 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature