Name
Abstract | ||
|---|---|---|
We 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/BIBM49941.2020.9313213 | BIBM |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luning Yang | 1 | 0 | 1.69 |
| Yihan Zhang | 2 | 17 | 14.15 |
| Nenad Mitic | 3 | 0 | 1.69 |
| Derin B. Keskin | 4 | 0 | 2.37 |
| Guanglan Zhang | 5 | 0 | 0.34 |
| Lou Chitkushev | 6 | 31 | 9.07 |
| Vladimir Brusic | 7 | 551 | 63.37 |