Leveraging the Cell Ontology to classify unseen cell types.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang S
PROVIDER: S-EPMC8455606 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Molecular machine learning algorithms are becoming increasingly powerful at predicting the potency of potential drug candidates to guide molecular discovery, lead series prioritization, and structural optimization. However, a substantial amount of inhibition data is bounded and inaccessible to traditional regression algorithms. Here, we develop a novel molecular pairing approach to process this data. This creates a new classification task of predicting which one of two paired molecules is more potent. This novel classification task can be accurately solved by various, established molecular machine learning algorithms, including XGBoost and Chemprop. Across 230 ChEMBL IC50 datasets, both tree-based and neural network-based "DeltaClassifiers" show improvements over traditional regression approaches in correctly classifying molecular potency improvements. The Chemprop-based deep DeltaClassifier outperformed all here evaluated regression approaches for paired molecules with shared and with distinct scaffolds, highlighting the promise of this approach for molecular optimization and scaffold-hopping.
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Project description:BackgroundAutomatic cell type identification is essential to alleviate a key bottleneck in scRNA-seq data analysis. While most existing classification tools show good sensitivity and specificity, they often fail to adequately not-classify cells that are missing in the used reference. Additionally, many tools do not scale to the continuously increasing size of current scRNA-seq datasets. Therefore, additional tools are needed to solve these challenges.ResultsscAnnotatR is a novel R package that provides a complete framework to classify cells in scRNA-seq datasets using pre-trained classifiers. It supports both Seurat and Bioconductor's SingleCellExperiment and is thereby compatible with the vast majority of R-based analysis workflows. scAnnotatR uses hierarchically organised SVMs to distinguish a specific cell type versus all others. It shows comparable or even superior accuracy, sensitivity and specificity compared to existing tools while being able to not-classify unknown cell types. Moreover, scAnnotatR is the only of the best performing tools able to process datasets containing more than 600,000 cells.ConclusionsscAnnotatR is freely available on GitHub ( https://github.com/grisslab/scAnnotatR ) and through Bioconductor (from version 3.14). It is consistently among the best performing tools in terms of classification accuracy while scaling to the largest datasets.
| S-EPMC8762856 | biostudies-literature
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.
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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.
2019-06-13 | GSE121922 | GEO
Project description:BackgroundNew technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an 'Ontologically BAsed Molecular Signature' (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type's identity.ResultsWe illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types.ConclusionsThis work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis - providing a new method for defining novel biomarkers and providing an opportunity for new biological insights.
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Project description:We develop an algorithm for the integration of single-cell sequencing, RNA-inferred CNVs, and bulk transcriptomic sequencing to improve molecular classification of tumor sub-groups.
2024-04-23 | GSE264680 | GEO
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