Dataset Information

Leveraging the Cell Ontology to classify unseen cell types.

ABSTRACT: Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantage of OnClass is its capability to classify cells into cell types not present in the training data because it uses the Cell Ontology graph to infer cell type relationships. Furthermore, OnClass can be used to identify marker genes for all the cell ontology categories, regardless of whether the cell types are present or absent in the training data, suggesting that OnClass goes beyond a simple annotation tool for single cell datasets, being the first algorithm capable to identify marker genes specific to all terms of the Cell Ontology and offering the possibility of refining the Cell Ontology using a data-centric approach.

SUBMITTER: Wang S

PROVIDER: S-EPMC8455606 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Leveraging the Cell Ontology to classify unseen cell types.

Wang Sheng S Pisco Angela Oliveira AO McGeever Aaron A Brbic Maria M Zitnik Marinka M Darmanis Spyros S Leskovec Jure J Karkanias Jim J Altman Russ B RB

Nature communications 20210921 1

Single cell technologies are rapidly generating large amounts of data that enables us to understand biological systems at single-cell resolution. However, joint analysis of datasets generated by independent labs remains challenging due to a lack of consistent terminology to describe cell types. Here, we present OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology. A key advantag ...[more]

PMID: 34548483

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Project description:BACKGROUND:One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS:As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS:DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Project description:Purpose: To identify distinct gene expression and functional profiles for the three main cell types (epithelial, keratocyte and endothelial) of the human cornea. Methods: RNA-sequencing was performed using total RNA isolated from ex vivo corneal epithelial cells (evCEpC), keratocytes (evK) and endothelial cells (evCEnC) obtained from 3 donor corneas obtained from a commercial eye bank. Transcriptomic analysis was performed using Kallisto (alignment (hg38) and quantification) and Sleuth (differential gene expression(DGE)), with transcript abundances calculated as transcripts per kilobase million (TPM). Expression was defined as TPM≥7.5 and significant DGE as a fold-change ≥4 and a false-discovery rate adjusted p-value≤0.05. Cell type specificity was defined as genes adhering to the above expression and DGE criteria and not expressed (i.e., TPM<7.5) in the other two cell types. Gene ontology enrichment analysis was performed on the cell type-specific gene lists using the gene group functional profiling (g:GOSt) tool within the web-based g:Profiler suite. Results: We identified 205 genes specific in evCEpC, and enrichment of epithelial-associated GO terms (e.g., cornified envelope, epidermis development, cell-cell adhesion). We identified 76 genes specific in evK, and fibroblast-associated GO terms (e.g., collagen metabolic process, metallopeptidase activity, extracellular matrix organization). We identified 96 genes specific in evCEnC, and at least one CEnC-associated GO term (e.g., cellular cation homeostasis) but several synapse-associated GO terms (e.g., synapse organization, synapse part). Conclusions: The human cornea is comprised of three main cell types that play important roles in maintaining corneal clarity and vision. Our results demonstrate that a small subset (0.4-1%) of the protein-coding genes confers distinct and classic functional properties associated with each cell type. In addition, we identified a novel association and significant functional overlap between CEnC and synapses. This may lead to insights into the molecular mechanisms of transendothelial transport and secretion, which are integral metabolic features of the corneal endothelium.

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Leveraging the Cell Ontology to classify unseen cell types.

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Leveraging the Cell Ontology to classify unseen cell types.

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