Single-cell technologies have made it possible to profile millions of cells, but for these resources to be useful they must be easy to query and access. To facilitate interactive and intuitive access to single-cell data we have developed scfind, a single-cell analysis tool that facilitates fast search of biologically or clinically relevant marker genes in cell atlases. Using transcriptome data from six mouse cell atlases, we show how scfind can be used to evaluate marker genes, perform in silico gating, and identify both cell-type-specific and housekeeping genes. Moreover, we have developed a subquery optimization routine to ensure that long and complex queries return meaningful results. To make scfind more user friendly, we use indices of PubMed abstracts and techniques from natural language processing to allow for arbitrary queries. Finally, we show how scfind can be used for multi-omics analyses by combining single-cell ATAC-seq data with transcriptome data.

单细胞技术使分析数百万个细胞成为可能，但要使这些资源有用，它们必须易于查询和访问。为了促进对单细胞数据的交互式和直观访问，我们开发了 scfind，这是一种单细胞分析工具，有助于在细胞图谱中快速搜索生物学或临床相关的标记基因。使用来自六个小鼠细胞图谱的转录组数据，我们展示了如何使用 scfind 评估标记基因、执行计算机门控以及识别细胞类型特异性和管家基因。此外，我们还开发了一个子查询优化例程，以确保长而复杂的查询返回有意义的结果。为了使 scfind 更加用户友好，我们使用 PubMed 摘要的索引和来自自然语言处理的技术来允许任意查询。最后，