Dergi makalesi Açık Erişim
Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <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"><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></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> </resource>
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