Optimizing hierarchical tree dissection parameters using historic epidemiologic data as 'ground truth'.

ABSTRACT: Hierarchical clustering of pathogen genotypes is widely used to complement epidemiologic investigations of outbreaks. Investigators must dissect trees to obtain genetic partitions that provide epidemiologists with meaningful information. Statistical approaches to tree dissection often require a user-defined parameter to predict the optimal partition number and augmenting this parameter can drastically impact resultant partition memberships. Here, we demonstrate how to optimize a given tree dissection parameter to maximize accuracy irrespective of the tree dissection method used. We hierarchically clustered 1,873 genotypes of the foodborne pathogen Cyclospora spp., including 587 possessing links to historic outbreaks. We dissected the resulting tree using a statistical method requiring user

SUBMITTER: Jacobson D 

PROVIDER: S-EPMC9955612 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature