Kavli Affiliate: Simon Ball
| Authors: Luke T Slater, John A Williams, Paul N Schofield, Sophie Russell, Samantha C Pendleton, Andreas Karwath, Hilary Fanning, Simon Ball, Robert T Hoehndorf and Georgios V Gkoutos
| Summary:
Abstract Background Annotation of biomedical entities with ontology terms facilitates the use of background knowledge in analysis. Described entities may be stratified into groups or otherwise assigned labels, and it is of interest to identify semantic characterisations of these groups based on their ontological annotations. Enrichment analysis is routinely employed to identify classes that are over-represented in annotations across sets of groups, most often applied to gene set analysis. However, these approaches usually consider only univariate relationships, make limited use of the semantic features of ontologies, and provide limited information and evaluation of the explanatory power of both singular and grouped candidate classes. Moreover, they do not solve the problem of deriving cohesive, characteristic, and discriminatory sets of terms for entity groups. Results We develop a new tool, Klarigi, which introduces multiple scoring heuristics used to identify classes that are both explanatory and discriminatory for groups of entities annotated with ontology terms. The tool includes a novel algorithm for derivation of multivariable semantic explanations for entity groups, makes use of semantic inference through live use of an ontology reasoner, and includes a classification method for identifying the discriminatory power of candidate sets. We describe the design and implementation of Klarigi, and evaluate its use in two test cases, comparing and contrasting methods and results with literature and enrichment analysis methods. Conclusions We demonstrate that Klarigi produces characteristic and discriminatory explanations for groups of biomedical entities in two settings. We also show that these explanations recapitulate and extend the knowledge held in existing biomedical databases and literature for several diseases. As such, Klarigi provides a distinct and valuable perspective on biomedical datasets when compared with traditional enrichment methods, and therefore constitutes a new method by which biomedical datasets can be explored, contributing to improved insight into semantic data. Competing Interest Statement John Williams is an employee of Eisai, Inc. Eisai, Inc had no role in funding or design of this study.