期刊
NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-022-28803-w
关键词
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资金
- Academy of Finland [295504, 310507, 326238, 340141, 344698]
- European Union's Horizon 2020 Research and Innovation Programme (ERA PerMed JAKSTAT-TARGET)
- Cancer Society of Finland
- Sigrid Juselius Foundation
- Norwegian Cancer Society [216104]
- Academy of Finland (AKA) [344698, 295504] Funding Source: Academy of Finland (AKA)
We developed a computational platform, ScType, for automated and fast cell-type identification using scRNA-seq data and a comprehensive cell marker database. ScType provides unbiased and accurate cell type annotations by ensuring the specificity of marker genes across cell clusters and types. It can also distinguish between healthy and malignant cell populations based on single-nucleotide variant calling.
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 annotations by guaranteeing the specificity of positive and negative marker genes across cell clusters and cell types. We also demonstrate how ScType distinguishes between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for anticancer applications. The widely applicable method is deployed both as an interactive web-tool (https://sctype.app ), and as an open-source R-package.
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