Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data

Aleksandr Ianevski, Anil K. Giri () and Tero Aittokallio ()
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Aleksandr Ianevski: Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki
Anil K. Giri: Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki
Tero Aittokallio: Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: 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 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.

Date: 2022
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DOI: 10.1038/s41467-022-28803-w

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