Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data
University of Helsinki · Helsinki Institute for Information Technology · +4 more institutions
Abstract
Identification of cell populations often relies on manual annotation of cell clusters using established marker genes. However, the selection of marker genes is a time-consuming process that may lead to sub-optimal annotations as the markers must be informative of both the individual cell clusters and various cell types present in the sample. Here, we developed a computational platform, ScType, which enables a fully-automated and ultra-fast cell-type identification based solely on a given scRNA-seq data, along with a comprehensive cell marker database as background information. Using six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides unbiased and accurate cell type…
Citation impact
- FWCI
- 63.58
- Percentile
- 100%
- References
- 71
Authors
3- AIAleksandr IanevskiCorresponding
University of Helsinki, Helsinki Institute for Information Technology, Institute for Molecular Medicine Finland, Aalto University
- AKAnil K. Giri
University of Helsinki, Institute for Molecular Medicine Finland
- TATero Aittokallio
Oslo University Hospital, University of Helsinki, University of Oslo, Helsinki Institute for Information Technology, Institute for Molecular Medicine Finland
Topics & keywords
- Identification (biology)
- Cell type
- Cell
- Annotation
- Computational biology
- Computer science
- Biology
- Genetics