Please use this identifier to cite or link to this item:
https://research.matf.bg.ac.rs/handle/123456789/322
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, Luning | en_US |
dc.contributor.author | Zhang, Yihan | en_US |
dc.contributor.author | Mitić, Nenad | en_US |
dc.contributor.author | Keskin, Derin B. | en_US |
dc.contributor.author | Zhang, Guang Lan | en_US |
dc.contributor.author | Chitkushev, Lou T. | en_US |
dc.contributor.author | Rankin, Richard | en_US |
dc.contributor.author | Brusic, Vladimir | en_US |
dc.date.accessioned | 2022-08-09T12:44:11Z | - |
dc.date.available | 2022-08-09T12:44:11Z | - |
dc.date.issued | 2021-01-01 | - |
dc.identifier.isbn | 9781665401265 | - |
dc.identifier.uri | https://research.matf.bg.ac.rs/handle/123456789/322 | - |
dc.description.abstract | Single-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.subject | 10x SCT | en_US |
dc.subject | classification | en_US |
dc.subject | gene expression profiles | en_US |
dc.subject | PBMC | en_US |
dc.subject | quantity control | en_US |
dc.subject | super-class | en_US |
dc.title | Applications of single cell profiles of PBMC:Improvements of cell type classification | en_US |
dc.type | Conference Paper | en_US |
dc.relation.publication | Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 | en_US |
dc.identifier.doi | 10.1109/BIBM52615.2021.9669678 | - |
dc.identifier.scopus | 2-s2.0-85125205661 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85125205661 | - |
dc.contributor.affiliation | Informatics and Computer Science | en_US |
dc.relation.firstpage | 3334 | en_US |
dc.relation.lastpage | 3340 | en_US |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | none | - |
item.openairetype | Conference Paper | - |
crisitem.author.dept | Informatics and Computer Science | - |
Appears in Collections: | Research outputs |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.