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Integration of single-cell proteomic datasets through distinctive proteins in cell clusters

Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan


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    "creators": [
        "affiliation": "Gebze Teknik \u00dcniversitesi", 
        "name": "Koca, Mehmet Burak"
        "affiliation": "Bo\u011fazi\u00e7i \u00dcniversitesi", 
        "name": "Sevilgen, Fatih Erdo\u011fan"
    "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>", 
    "doi": "10.1002/pmic.202300282", 
    "has_grant": true, 
    "keywords": [
      "data integration", 
      "batch effect", 
      "variational autoencoder", 
      "cell matching"
    "language": "eng", 
    "license": {
      "id": "cc-by-sa"
    "publication_date": "2023-12-22", 
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    "science_branches": [
      "Temel Bilimler > Ya\u015fam Bilimleri > Biyoinformatik", 
      "Teknik Bilimler > Bilgisayar Bilimleri"
    "title": "Integration of single-cell proteomic datasets through distinctive proteins in cell clusters", 
    "tubitak_grants": [
        "program": "1002", 
        "project_number": "122E587", 
        "workgroup": "EEEAG"
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