Yang Lu, Ng Yan Er, Sun Haipeng, Li Ying, Chini Lucas C S, LeBrasseur Nathan K, Chen Jun, Zhang Xu
bioRxiv. 2023 May 4:2023.05.03.538463. doi: 10.1101/2023.05.03.538463.
Single-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed. We curated a comprehensive cell marker database named and developed a companion R package , an easy-to-use single cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. performs better than the currently available annotation tools on all the datasets tested. Additionally, the can be integrated with other tools and further improve their performance. and will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way.
单细胞RNA测序(scRNA-seq)已成为基础生物医学研究和转化医学研究中广泛使用的工具。在scRNA-seq数据分析中,细胞类型注释是一个必不可少但具有挑战性的步骤。在过去几年中,已经开发了几种注释工具。这些方法要么需要有标签的训练/参考数据集(但这些数据集并非总是可用),要么需要预定义细胞亚群标志物列表(而这些标志物存在偏差)。因此,仍然迫切需要一种用户友好且精确的注释工具。我们精心策划了一个名为 的全面细胞标志物数据库,并开发了一个配套的R包 ,这是一个易于使用的单细胞注释工具,以提供快速准确的细胞类型注释。 在跨不同平台和组织的48个独立scRNA-seq数据集中证明了其有效性。 在所有测试数据集中的表现均优于目前可用的注释工具。此外, 可以与其他工具集成并进一步提高其性能。 和 将帮助研究人员以简化且用户友好的方式定义其scRNA-seq数据中的细胞类型。