22 Aralık 2023 Dergi makalesi Açık Erişim

Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan

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{
  "@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"
}