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

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

SUBMITTER: Ianevski A 

PROVIDER: S-EPMC8913782 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature