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

被引:317
作者
Ianevski, Aleksandr [1 ,2 ]
Giri, Anil K. [1 ]
Aittokallio, Tero [1 ,2 ,3 ,4 ]
机构
[1] Univ Helsinki, Inst Mol Med Finland FIMM, HiLIFE, Helsinki, Finland
[2] Aalto Univ, Helsinki Inst Informat Technol HIIT, Helsinki, Finland
[3] Oslo Univ Hosp, Inst Canc Res, Dept Canc Genet, Oslo, Norway
[4] Univ Oslo, Fac Med, Ctr Biostat & Epidemiol OCBE, Oslo, Norway
基金
芬兰科学院; 欧盟地平线“2020”;
关键词
EXPRESSION; BIOLOGY;
D O I
10.1038/s41467-022-28803-w
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:10
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