22 Aralık 2023 Dergi makalesi Açık Erişim

Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan

Dublin Core

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Koca, Mehmet Burak</dc:creator>
  <dc:creator>Sevilgen, Fatih Erdoğan</dc:creator>
  <dc:date>2023-12-22</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/263586</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:263586</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by-sa</dc:rights>
  <dc:subject>single-cell</dc:subject>
  <dc:subject>data integration</dc:subject>
  <dc:subject>batch effect</dc:subject>
  <dc:subject>variational autoencoder</dc:subject>
  <dc:subject>cell matching</dc:subject>
  <dc:title>Integration of single-cell proteomic datasets through distinctive proteins in cell clusters</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
</oai_dc:dc>