Cell ontology in an age of data-driven cell classification.

David Osumi-Sutherland
Author Information
  1. David Osumi-Sutherland: European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. davidos@ebi.ac.uk.

Abstract

BACKGROUND: Data-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.
RESULTS: Focusing 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.
CONCLUSIONS: Annotation 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.

Keywords

References

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Grants

  1. /Wellcome Trust
  2. WT105023MA/Wellcome Trust

MeSH Term

Animals
Biological Ontologies
Cells
Computational Biology
Databases, Factual
Humans
Mice
Models, Statistical

Word Cloud

Created with Highcharts 10.0.0celldata-drivencanontologiesclassificationCellontologypotentialoutputsscalemadesearchabledatatermsexamplesDrosophilaclassificationsneuronBACKGROUND:Data-drivenbecomingcommonnowimplementedmassiveprojectsHumanAtlaseffortsposeschallengeresultsaccessiblebiologistsgeneral?relatedbackrichclassicalknowledgecell-typesanatomydevelopment?willvarioustypessingleanalysiscross-searchable?StructuredannotationprovidessolutionproblemsturngreatusingstructureintegratequerysystemsRESULTS:Focusingmouseretinaolfactorysystempresentworkedillustratingformalizationenhancequeryingcell-classificationsextendedintegratingCONCLUSIONS:Annotationplayimportantrolemakingdrivenquery-ablefulfillingrequiresstandardizedformalpatternsstructuringannotationslinkingageAntennallobeprojectionatlasMouseOntologyOwlRetinalbipolarSingleUnsupervisedclusteringscRNAseq

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