Cell ontology in an age of data-driven cell classification

BMC Bioinformatics

Research

David Osumi-Sutherland¹ 

[1] European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, CB10 1SD, Hinxton, UK;

关键词: Single cell;    Unsupervised clustering;    scRNAseq;    Cell atlas;    Ontology;    Owl;    Drosophila;    Mouse;    Retinal bipolar neuron;    Antennal lobe projection neuron;   

DOI  :  10.1186/s12859-017-1980-6

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2017