Dergi makalesi Açık Erişim

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

Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan

DataCite XML

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="" xmlns="" xsi:schemaLocation="">
  <identifier identifierType="URL"></identifier>
      <creatorName>Koca, Mehmet Burak</creatorName>
      <givenName>Mehmet Burak</givenName>
      <affiliation>Gebze Teknik Üniversitesi</affiliation>
      <creatorName>Sevilgen, Fatih Erdoğan</creatorName>
      <givenName>Fatih Erdoğan</givenName>
      <affiliation>Boğaziçi Üniversitesi</affiliation>
    <title>Integration Of Single-Cell Proteomic Datasets Through Distinctive Proteins In Cell Clusters</title>
    <subject>data integration</subject>
    <subject>batch effect</subject>
    <subject>variational autoencoder</subject>
    <subject>cell matching</subject>
    <date dateType="Issued">2023-12-22</date>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1002/pmic.202300282</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution Share-Alike</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <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>
      <funderName>Türkiye Bilimsel ve Teknolojik Araştirma Kurumu</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID"></funderIdentifier>
Görüntülenme 67
İndirme 4
Veri hacmi 619.2 MB
Tekil görüntülenme 61
Tekil indirme 4

Alıntı yap