22 Aralık 2023 Dergi makalesi Açık Erişim

Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan

Citation Style Language JSON

{
  "DOI": "10.1002/pmic.202300282", 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Koca", 
      "given": " Mehmet Burak"
    }, 
    {
      "family": "Sevilgen", 
      "given": " Fatih Erdo\u011fan"
    }
  ], 
  "id": "263586", 
  "issued": {
    "date-parts": [
      [
        2023, 
        12, 
        22
      ]
    ]
  }, 
  "language": "eng", 
  "title": "Integration of single-cell proteomic datasets through distinctive proteins in cell clusters", 
  "type": "article-journal"
}