Ontological Hierarchical Clustering for Library-Based Microbial Source Tracking

Pyroprinting is a novel library-based microbial source tracking method developed by the Biology department at Cal Poly, San Luis Obispo. Biologists conducting research using pyroprinting rely on methods for partitioning collected bacterial isolates into bacterial strains. Clustering algorithms are often used for bacterial strain analysis of organisms in computational biology. Agglomerative hierarchical clustering, a commonly used clustering algorithm, is inadequate given the nature of data collection for pyroprinting. While the clusters produced are acceptable, pyroprinting requires a method of analysis that is scalable and incorporates useful metadata into the clustering process. We propose an ontology-based hierarchical clustering algorithm OHClust!, a modification of the agglomerative hierarchical clustering algorithm. OHClust! uses metadata associated with the data being clustered to direct the order in which the individual data points and sub clusters are compared to each other. This paper describes OHClust! and compares it to agglomerative hierarchical clustering.