Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/322
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dc.contributor.authorYang, Luningen_US
dc.contributor.authorZhang, Yihanen_US
dc.contributor.authorMitić, Nenaden_US
dc.contributor.authorKeskin, Derin B.en_US
dc.contributor.authorZhang, Guang Lanen_US
dc.contributor.authorChitkushev, Lou T.en_US
dc.contributor.authorRankin, Richarden_US
dc.contributor.authorBrusic, Vladimiren_US
dc.date.accessioned2022-08-09T12:44:11Z-
dc.date.available2022-08-09T12:44:11Z-
dc.date.issued2021-01-01-
dc.identifier.isbn9781665401265-
dc.identifier.urihttps://research.matf.bg.ac.rs/handle/123456789/322-
dc.description.abstractSingle-cell-derived-class (SCDC) profiles capture characteristic gene expression from single cells representing types and subtypes and their conditions. SCDC profiles show high reproducibility across similar single-cell types processed under the same conditions. We have demonstrated two applications of SCDC profiles-classification of single cells from PBMC into six main classes (B cells, cDC, pDC, monocytes, NK cells, and T cells) and into three super-classes (BC+pDC,MC+cDC, and TC+NK). The minimum number of individual cells required for building an effective reference SCDC profile has been assessed to be between 160 and 640 cells. The variability of SCDC gradually decreases as the number of cells used to derive the profile increases from 10-cells to 640-cells. The classification accuracy of PBMC extracted by PBMC separation by SCDC profiles was 85-100% and 95-100% for supertypes depending on the cell type or supertype. Classification accuracy for PBMC cell types is lower for samples that are processed by cell sorting, or other sample processing steps.en_US
dc.subject10x SCTen_US
dc.subjectclassificationen_US
dc.subjectgene expression profilesen_US
dc.subjectPBMCen_US
dc.subjectquantity controlen_US
dc.subjectsuper-classen_US
dc.titleApplications of single cell profiles of PBMC:Improvements of cell type classificationen_US
dc.typeConference Paperen_US
dc.relation.publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021en_US
dc.identifier.doi10.1109/BIBM52615.2021.9669678-
dc.identifier.scopus2-s2.0-85125205661-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85125205661-
dc.contributor.affiliationInformatics and Computer Scienceen_US
dc.relation.firstpage3334en_US
dc.relation.lastpage3340en_US
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairetypeConference Paper-
crisitem.author.deptInformatics and Computer Science-
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