Cell ontology in an age of data-driven cell classification
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...
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| Format: | Article |
| Language: | English |
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BMC
2017-12-01
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| Series: | BMC Bioinformatics |
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| Online Access: | http://link.springer.com/article/10.1186/s12859-017-1980-6 |
| _version_ | 1819099860388806656 |
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| author | David Osumi-Sutherland |
| author_facet | David Osumi-Sutherland |
| author_sort | David Osumi-Sutherland |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T00:53:35Z |
| format | Article |
| id | doaj.art-c22095429a244b26ad05cf189732b5ce |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-22T00:53:35Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c22095429a244b26ad05cf189732b5ce2022-12-21T18:44:23ZengBMCBMC Bioinformatics1471-21052017-12-0118S17354210.1186/s12859-017-1980-6Cell ontology in an age of data-driven cell classificationDavid Osumi-Sutherland0European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome CampusAbstract 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.http://link.springer.com/article/10.1186/s12859-017-1980-6Single cellUnsupervised clusteringscRNAseqCell atlasOntologyOwl |
| spellingShingle | David Osumi-Sutherland Cell ontology in an age of data-driven cell classification BMC Bioinformatics Single cell Unsupervised clustering scRNAseq Cell atlas Ontology Owl |
| title | Cell ontology in an age of data-driven cell classification |
| title_full | Cell ontology in an age of data-driven cell classification |
| title_fullStr | Cell ontology in an age of data-driven cell classification |
| title_full_unstemmed | Cell ontology in an age of data-driven cell classification |
| title_short | Cell ontology in an age of data-driven cell classification |
| title_sort | cell ontology in an age of data driven cell classification |
| topic | Single cell Unsupervised clustering scRNAseq Cell atlas Ontology Owl |
| url | http://link.springer.com/article/10.1186/s12859-017-1980-6 |
| work_keys_str_mv | AT davidosumisutherland cellontologyinanageofdatadrivencellclassification |