Dergi makalesi Açık Erişim
Koca, Mehmet Burak; Sevilgen, Fatih Erdoğan
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1002/pmic.202300282</subfield> <subfield code="2">doi</subfield> </datafield> <controlfield tag="005">20240209065240.0</controlfield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2023-12-22</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Integration of single-cell proteomic datasets through distinctive proteins in cell clusters</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Gebze Teknik Üniversitesi</subfield> <subfield code="a">Koca, Mehmet Burak</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">http://www.opendefinition.org/licenses/cc-by-sa</subfield> <subfield code="a">Creative Commons Attribution Share-Alike</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="u">Boğaziçi Üniversitesi</subfield> <subfield code="a">Sevilgen, Fatih Erdoğan</subfield> </datafield> <datafield tag="041" ind1=" " ind2=" "> <subfield code="a">eng</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="2">opendefinition.org</subfield> <subfield code="a">cc-by</subfield> </datafield> <controlfield tag="001">263586</controlfield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:aperta.ulakbim.gov.tr:263586</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="b">article</subfield> <subfield code="a">publication</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="u">https://aperta.ulakbim.gov.trrecord/263586/files/SCPRO-HI.zip</subfield> <subfield code="z">md5:771ae7d5e242edee80a66b8f4ed279f5</subfield> <subfield code="s">154807681</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">single-cell</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">data integration</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">batch effect</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">variational autoencoder</subfield> </datafield> <datafield tag="653" ind1=" " ind2=" "> <subfield code="a">cell matching</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><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></subfield> </datafield> </record>
Görüntülenme | 107 |
İndirme | 7 |
Veri hacmi | 1.1 GB |
Tekil görüntülenme | 95 |
Tekil indirme | 7 |