英国惠康基因组研究园John C. Marioni、Shila Ghazanfar研究组近日取得一项新成果。经过不懈努力，他们研发了使用非共享要素的稳定镶嵌单细胞数据集。相关论文发表在2023年5月25日出版的《自然—生物技术》杂志上。

研究人员开发了StabMap，这是一种镶嵌数据集成技术，通过利用非重叠特征来稳定单细胞数据映射。StabMap首先根据共享要素推断镶嵌数据拓扑，然后通过拓扑最短路径将所有像元投影到已验证或非验证的参考坐标上。研究表明StabMap在各种模拟环境中表现良好，促进了“多跳”镶嵌数据集成，其中某些数据集并不共享任何特征，StabMap可使用空间基因表达特征将解离的单细胞数据映射到空间转录组参考数据库中。

据悉，利用现有的单细胞组学技术，人们捕获了许多具有不同生物信息学内容的独特特征。数据集成旨在将使用不同技术捕获的细胞数据归一到通用平台，以促进下游数据分析。当前数据集成技术使用一组通用的特征从而忽略了非重叠特征，这导致信息丢失。

附：英文原文

Title: Stabilized mosaic single-cell data integration using unshared features

Author: Ghazanfar, Shila, Guibentif, Carolina, Marioni, John C.

Issue&Volume: 2023-05-25

Abstract: 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.

DOI: 10.1038/s41587-023-01766-z

Source: https://www.nature.com/articles/s41587-023-01766-z