![[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
3MB |
Preview |
PDF (Supplementary Material)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
4MB |
| Item Type: | Article |
|---|---|
| Title: | Adjustments to the reference dataset design improve cell type label transfer |
| Creators Name: | Mölbert, C. and Haghverdi, L. |
| Abstract: | The transfer of cell type labels from pre-annotated (reference) to newly collected data is an important task in single-cell data analysis. As the number of publicly available annotated datasets which can be used as reference, as well as the number of computational methods for cell type label transfer are constantly growing, rationals to understand and decide which reference design and which method to use for a particular query dataset are needed. Using detailed data visualisations and interpretable statistical assessments, we benchmark a set of popular cell type annotation methods, test their performance on different cell types and study the effects of the design of reference data (e.g., cell sampling criteria, inclusion of multiple datasets in one reference, gene set selection) on the reliability of predictions. Our results highlight the need for further improvements in label transfer methods, as well as preparation of high-quality pre-annotated reference data of adequate sampling from all cell types of interest, for more reliable annotation of new datasets. |
| Keywords: | Single-Cell RNA-Seq, Cell Type Annotation, Label Transfer, Reference Data, Benchmark, Interpretability |
| Source: | Frontiers in Bioinformatics |
| ISSN: | 2673-7647 |
| Publisher: | Frontiers Media SA |
| Volume: | 3 |
| Page Range: | 1150099 |
| Date: | 5 April 2023 |
| Official Publication: | https://doi.org/10.3389/fbinf.2023.1150099 |
| PubMed: | View item in PubMed |
Repository Staff Only: item control page
