Dergi makalesi Açık Erişim
Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan
{ "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" }
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