%0 Journal Article %A Aleksandr Ianevski %A Anil K Giri %A Tero Aittokallio %T Fully-automated cell-type identification with specific markers extracted from single-cell transcriptomic data %D 2019 %R 10.1101/812131 %J bioRxiv %P 812131 %X Single-cell transcriptomics enables systematic charting of cellular composition of complex tissues. Identification of cell populations often relies on unsupervised clustering of cells based on the similarity of the scRNA-seq profiles, followed by manual annotation of cell clusters using established marker genes. However, manual selection of marker genes for cell-type annotation is a laborious and error-prone task since the selected markers must be specific both to the individual cell clusters and various cell types. Here, we developed a computational method, termed ScType, which enables data-driven selection of marker genes based solely on given scRNA-seq data. Using a compendium of 7 scRNA-seq datasets from various human and mouse tissues, we demonstrate how ScType enables unbiased, accurate and fully-automated single-cell type annotation by guaranteeing the specificity of marker genes both across cell clusters and cell types. The widely-applicable method is implemented as an interactive web-tool (https://sctype.fimm.fi), connected with comprehensive database of specific markers. %U https://www.biorxiv.org/content/biorxiv/early/2019/10/21/812131.full.pdf