Abstract
BackgroundData-driven cell classification is becoming common and is now being implemented on a massive scale by projects such as the Human Cell Atlas. The scale of these efforts poses a challenge. How can the results be made searchable and accessible to biologists in general? How can they be related back to the rich classical knowledge of cell-types, anatomy and development? How will data from the various types of single cell analysis be made cross-searchable? Structured annotation with ontology terms provides a potential solution to these problems. In turn, there is great potential for using the outputs of data-driven cell classification to structure ontologies and integrate them with data-driven cell query systems.ResultsFocusing on examples from the mouse retina and Drosophila olfactory system, I present worked examples illustrating how formalization of cell ontologies can enhance querying of data-driven cell-classifications and how ontologies can be extended by integrating the outputs of data-driven cell classifications.ConclusionsAnnotation with ontology terms can play an important role in making data driven classifications searchable and query-able, but fulfilling this potential requires standardized formal patterns for structuring ontologies and annotations and for linking ontologies to the outputs of data-driven classification.
Highlights
Data-driven cell classification is becoming common and is being implemented on a massive scale by projects such as the Human Cell Atlas
Retinal bipolar cell (RBC) are classically divided into classes based on whether they are synapsed by rod or cone cells, which laminas of the inner plexiform layer of the retina their axons arborize in and on the morphology of their axonal arbor [24]
Mammalian RBCs can be divided into functional groups depending on whether they depolarize in response to a light stimulus (ON) or to the removal of a light stimulus (OFF) and whether they carry chromatic or achromatic information
Summary
Classification from transcriptomic profiles is likely to become dominant via large scale projects including cell atlases for Humans [8] and Drosophila [9]. It is still an open question whether these different approaches to classification will produce multiple, orthogonal classifications, distinct from classical classifications, but early results suggest not. Unsupervised clustering of imaged single Drosophila neurons using a similarity score for morphology and location (NBLAST) identifies many well-known Drosophila neuron types [3] These results and others are consistent with the existence of cell types corresponding to stable states in which cells have characteristic morphology, gene expression profile, and functional characteristics etc
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