Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates ‘multi-hop’ mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.

目前可用的单细胞组学技术捕获了许多具有不同生物信息内容的独特特征。数据集成旨在将使用不同技术捕获的细胞放置到共同的嵌入中，以促进下游分析任务。当前的水平数据集成技术使用一组共同特征，从而忽略不重叠的特征并丢失信息。在这里，我们介绍 StabMap，一种镶嵌数据集成技术，它通过利用非重叠特征来稳定单细胞数据的映射。 StabMap 首先基于共享特征推断镶嵌数据拓扑，然后通过沿拓扑遍历最短路径将所有像元投影到有监督或无监督参考坐标上。我们表明，StabMap 在各种模拟环境中表现良好，促进“多跳”镶嵌数据集成，其中某些数据集不共享任何特征，并允许使用空间基因表达特征将分离的单细胞数据映射到空间转录组参考上。