Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/317
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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, Louen_US
dc.contributor.authorBrusic, Vladimiren_US
dc.date.accessioned2022-08-09T12:40:05Z-
dc.date.available2022-08-09T12:40:05Z-
dc.date.issued2020-12-16-
dc.identifier.isbn9781728162157-
dc.identifier.urihttps://research.matf.bg.ac.rs/handle/123456789/317-
dc.description.abstractWe developed a method for building gene expression profiles from single-cell gene expression matrices. We named these profiles the 'single-cell-derived-class' or SCDC profiles. They represent characteristic patterns of gene expressions of the types and subtypes of cells derived from single-cell transcriptome experiments. We deployed this method on classes and subclasses of peripheral blood mononuclear cells (PBMC). We used 47 human single-cell transcriptomics (SCT) data sets representing various classes, subclasses, and sample processing conditions. From comparisons of these profiles we found that they are highly reproducible, even when derived from unrelated studies as long as the processing steps are identical. The most similar profiles are those that are minimally processed. Cell sorting using FACS, cell enrichment, or fixing in methanol make profiles distinct from those derived from normal healthy samples. Our results suggest that approximately 50-200 cells are sufficient for building a useful SCDC profile.en_US
dc.subjectgene expression profilesen_US
dc.subjectheat mapsen_US
dc.subjectmachine learningen_US
dc.subjectprofile comparisonen_US
dc.subjectscRNAseqen_US
dc.titleSingle-cell mRNA Profiles in PBMCen_US
dc.typeConference Paperen_US
dc.relation.publicationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021en_US
dc.identifier.doi10.1109/BIBM49941.2020.9313213-
dc.identifier.scopus2-s2.0-85100353940-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85100353940-
dc.contributor.affiliationInformatics and Computer Scienceen_US
dc.relation.firstpage1318en_US
dc.relation.lastpage1323en_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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