Subsumption, Vectorization, Heat Maps, and Word Clouds Support the Visualization of Orphadata Neurology Phenotypes

Disease phenotypes are characterized by signs (what a physician observes during the examination of a patient) and symptoms (the complaints of a patient to a physician). Large repositories of disease phenotypes are accessible through the Online Mendelian Inheritance of Man, Human Phenotype Ontology, and Orphadata initiatives. Many of the diseases in these datasets are neurologic. For each repository, the phenotype of a neurologic disease is represented as a variable-length list of concepts selected from a suitable ontology. Visualizations of these lists are not provided. We address this limitation by using subsumption to collapse the number of descriptive features from 2,946 classes into thirty superclasses. Phenotype feature lists of variable lengths were converted into fixed-length numerical vectors. Phenotype vectors can be aggregated into matrices and visualized as heat maps that allow side-by-side disease comparisons. Individual diseases (representing a row in the matrix) can be visualized as word clouds. We illustrate the utility of this approach with a use case based on 32 dystonic diseases in Orphadata. The use of subsumption to collapse phenotype features into superclasses, the conversion of phenotype lists into vectors, and the visualization of phenotypes vectors as heat maps and word clouds contribute to the improved visualization of neurology phenotypes in Orphadata.