![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
Tools
Tools
Preview |
PDF (Original Article)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
2MB |
![[thumbnail of Supplementary Information]](https://edoc.mdc-berlin.de/style/images/fileicons/other.png) |
Other (Supplementary Information)
2MB |
| Item Type: | Article |
|---|---|
| Title: | Identifying tumor cells at the single-cell level using machine learning |
| Creators Name: | Dohmen, J., Baranovskii, A., Ronen, J., Uyar, B., Franke, V. and Akalin, A. |
| Abstract: | Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each sequenced cell-is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts. |
| Keywords: | Single-Cell Genomics, Machine Learning, Cell Type Classification, Cancer |
| Source: | Genome Biology |
| ISSN: | 1474-760X |
| Publisher: | BioMed Central |
| Volume: | 23 |
| Number: | 1 |
| Page Range: | 123 |
| Date: | 30 May 2022 |
| Official Publication: | https://doi.org/10.1186/s13059-022-02683-1 |
| PubMed: | View item in PubMed |
Repository Staff Only: item control page
