Division of Biology and Biological Engineering, California Institute of Technology, 1200 E California Blvd, Pasadena, CA, 91125, USA.
Database (Oxford). 2020 Nov 28;2020. doi: 10.1093/database/baaa073.
The more than 1000 single-cell transcriptomics studies that have been published to date constitute a valuable and vast resource for biological discovery. While various 'atlas' projects have collated some of the associated datasets, most questions related to specific tissue types, species or other attributes of studies require identifying papers through manual and challenging literature search. To facilitate discovery with published single-cell transcriptomics data, we have assembled a near exhaustive, manually curated database of single-cell transcriptomics studies with key information: descriptions of the type of data and technologies used, along with descriptors of the biological systems studied. Additionally, the database contains summarized information about analysis in the papers, allowing for analysis of trends in the field. As an example, we show that the number of cell types identified in scRNA-seq studies is proportional to the number of cells analysed. Database URL: www.nxn.se/single-cell-studies/gui.
迄今为止，已经发表了超过 1000 项单细胞转录组学研究，这些研究为生物发现提供了宝贵且广泛的资源。虽然各种“图谱”项目已经整理了一些相关数据集，但大多数与特定组织类型、物种或其他研究属性相关的问题都需要通过手动和具有挑战性的文献搜索来确定论文。为了促进使用已发表的单细胞转录组学数据进行发现，我们汇集了一个几乎详尽的、经过人工精心策划的单细胞转录组学研究数据库，其中包含关键信息：所使用的数据和技术类型的描述，以及所研究的生物系统的描述符。此外，该数据库还包含有关论文中分析的汇总信息，可用于分析该领域的趋势。例如，我们表明，在 scRNA-seq 研究中鉴定的细胞类型数量与分析的细胞数量成正比。数据库网址：www.nxn.se/single-cell-studies/gui。
