Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2022-03-10
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en
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10
1-10
1-10
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Nature Communications, Volume 13, issue 1
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.Description
Funding Information: The authors thank Dr. Pirkko M Mattila, Dr. Jenni Lahtela and Bhiswa Ghimire for their valuable suggestions on how to improve the web-tool, Olle Hansson for the FIMM cluster server machine to host the web-tool and the database, and all the beta-testers for confirming the smooth operation and reproducibility of the analyses. This work was supported by the Academy of Finland (grants 295504, 310507, 326238, 340141 and 344698 to TA), European Union’s Horizon 2020 Research and Innovation Programme (ERA PerMed JAKSTAT-TARGET), the Cancer Society of Finland (TA), the Sigrid Jusélius Foundation (TA), and the Norwegian Cancer Society (grant 216104 to TA). Publisher Copyright: © 2022, The Author(s).
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Ianevski, A, Giri, A K & Aittokallio, T 2022, ' Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data ', Nature Communications, vol. 13, no. 1, 1246, pp. 1-10 . https://doi.org/10.1038/s41467-022-28803-w
