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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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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/263586</identifier>
  <creators>
    <creator>
      <creatorName>Koca, Mehmet Burak</creatorName>
      <givenName>Mehmet Burak</givenName>
      <familyName>Koca</familyName>
      <affiliation>Gebze Teknik Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Sevilgen, Fatih Erdoğan</creatorName>
      <givenName>Fatih Erdoğan</givenName>
      <familyName>Sevilgen</familyName>
      <affiliation>Boğaziçi Üniversitesi</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Integration Of Single-Cell Proteomic Datasets Through Distinctive Proteins In Cell Clusters</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <subjects>
    <subject>single-cell</subject>
    <subject>data integration</subject>
    <subject>batch effect</subject>
    <subject>variational autoencoder</subject>
    <subject>cell matching</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2023-12-22</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/263586</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1002/pmic.202300282</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by-sa">Creative Commons Attribution Share-Alike</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>Türkiye Bilimsel ve Teknolojik Araştirma Kurumu</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">https://doi.org/10.13039/501100004410</funderIdentifier>
      <awardNumber>122E587</awardNumber>
    </fundingReference>
  </fundingReferences>
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