Dergi makalesi Açık Erişim
Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan
{ "@context": "https://schema.org/", "@id": 263586, "@type": "ScholarlyArticle", "creator": [ { "@type": "Person", "affiliation": "Gebze Teknik \u00dcniversitesi", "name": "Koca, Mehmet Burak" }, { "@type": "Person", "affiliation": "Bo\u011fazi\u00e7i \u00dcniversitesi", "name": "Sevilgen, Fatih Erdo\u011fan" } ], "datePublished": "2023-12-22", "description": "<p>The use of mass spectrometry and antibody-based sequencing technologies at the single-cell level has led to an increase in single-cell proteomic datasets. Integrating these datasets is crucial to eliminate the batch effect that often arises due to their limited sequencing molecules. Although methods for horizontally integrating high-dimensional single-cell transcriptomic datasets can also be applied to single-cell proteomic datasets, a specialized approach explicitly tailored for low-dimensional proteomic datasets may enhance the integration process. Here, we introduce SCPRO-HI, an algorithm for the horizontal integration of antibody-based single-cell proteomic datasets. It utilizes a hierarchical cell anchoring technique to match cells based on the similarity of distinctive proteins for constituting cell clusters. A novel variational auto-encoder model is employed for correcting batch effects on the protein abundances, eliminating the need for mapping them into a new domain. Moreover, we propose a technique for extending the algorithm to high-dimensional datasets. The performance of the SCPRO-HI algorithm is evaluated using simulated and real-world single-cell proteomic datasets. The findings demonstrate our algorithm outperforms state-of-the-art methods, achieving a 75% higher silhouette score while preserving HVPs 13% better. Furthermore, the algorithm shows competitive performance in transcriptomic datasets, suggesting potential for integrating high-dimensional mass-spectrometry-based proteomic datasets.</p>", "headline": "Integration of single-cell proteomic datasets through distinctive proteins in cell clusters", "identifier": 263586, "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", "inLanguage": { "@type": "Language", "alternateName": "eng", "name": "English" }, "keywords": [ "single-cell", "data integration", "batch effect", "variational autoencoder", "cell matching" ], "license": "http://www.opendefinition.org/licenses/cc-by-sa", "name": "Integration of single-cell proteomic datasets through distinctive proteins in cell clusters", "url": "https://aperta.ulakbim.gov.tr/record/263586" }
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