Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed.

中文翻译：

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SCIM：具有不配对特征集的通用单细胞匹配

最近的技术进步导致单细胞数据的产生和可用性增加。整合一组多技术测量的能力将允许通过统一每种技术提供的视角来识别具有生物学或临床意义的观察结果。然而，在大多数情况下，分析技术会消耗已使用的单元格，因此数据集之间的成对对应关系会丢失。由于可以获取的单细胞数据集规模巨大，因此需要可扩展的算法，能够将一个细胞中进行的单细胞测量与另一种技术中相应的同级细胞进行普遍匹配。