小柯机器人

美国耶鲁大学Zongming Ma、宾夕法尼亚大学Nancy R. Zhang和斯坦福大学Garry P. Nolan研究组合作，实现弱连接跨模态空间和单细胞数据的整合。这一研究成果于2023年9月7日发表在国际学术期刊《自然-生物技术》上。

研究人员通过模糊平滑嵌入（MaxFuse）建立了匹配X模态，这是一种跨模态数据集成方法，通过迭代共嵌入、数据平滑和单元格匹配，使用每种模态包含的所有信息来获得高质量的集成，即使这些特征具有弱链接。MaxFuse与模态无关，在弱联动情景下表现出很高的准确性；在基准数据集的关键评估指标下，并在现有方法的基础上实现了相对20-70%的改进。

弱连锁的一个典型例子是空间蛋白质组学数据与单细胞测序数据的整合。在这种类型的两个示例分析中，MaxFuse在同一组织切片上以单细胞分辨率实现了蛋白质组学、转录组学和表观基因组信息的空间整合。

据悉，尽管单细胞和空间测序方法可以同时测量多种生物模式，但缺少可以捕获同一细胞内所有模式的技术。对于目前数据集成的方法，跨模态集成的可行性依赖于高度相关的先验“链接”特征存在。

附：英文原文

Title: Integration of spatial and single-cell data across modalities with weakly linked features

Author: Chen, Shuxiao, Zhu, Bokai, Huang, Sijia, Hickey, John W., Lin, Kevin Z., Snyder, Michael, Greenleaf, William J., Nolan, Garry P., Zhang, Nancy R., Ma, Zongming

Issue&Volume: 2023-09-07

Abstract: Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori ‘linked’ features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section. MaxFuse enables data integration between modalities even when features are weakly correlated.

DOI: 10.1038/s41587-023-01935-0

Source: https://www.nature.com/articles/s41587-023-01935-0

Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex