Single cell data measures multiple cellular markers at the single-cell level for thousands to millions of cells. Identification of distinct cell populations is a key step for further biological understanding, usually performed by clustering this data. Dimensionality reduction based clustering tools are either not scalable to large datasets containing millions of cells, or not fully automated requiring an initial manual estimation of the number of clusters. Graph clustering tools provide automated and reliable clustering for single cell data, but suffer heavily from scalability to large datasets.

中文翻译：

---

SCHNEL：高维单细胞数据的可伸缩集群

单细胞数据可在单细胞水平上测量数千个到数百万个细胞的多个细胞标记。识别不同细胞群是进一步了解生物学的关键步骤，通常是通过对这些数据进行聚类来完成的。基于降维的聚类工具要么不能扩展到包含数百万个单元的大型数据集，要么不能完全自动化，而需要对簇数进行初始手动估计。图聚类工具为单个单元格数据提供了自动且可靠的聚类，但是在扩展到大型数据集方面却遭受了沉重的打击。