Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA.
Nat Biotechnol. 2024 Jul;42(7):1096-1106. doi: 10.1038/s41587-023-01935-0. Epub 2023 Sep 7.
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.
虽然单细胞和空间测序方法能够同时测量超过一种生物学模式，但没有一种技术可以在同一细胞内捕获所有模式。对于当前的数据集成方法，跨模式集成的可行性取决于高度相关的、先验的“链接”特征的存在。我们通过模糊平滑嵌入（MaxFuse）来描述匹配 X 模式，这是一种跨模式数据集成方法，通过迭代共嵌入、数据平滑和细胞匹配，利用每个模式中的所有信息来获得高质量的集成，即使特征的关联性较弱。MaxFuse 不依赖于模式，在弱链接场景下具有高度的稳健性和准确性，在基准数据集的关键评估指标上，与现有方法相比，实现了 20%~70%的相对改进。弱链接的一个典型例子是将空间蛋白质组学数据与单细胞测序数据进行整合。在这两种类型的示例分析中，MaxFuse 使空间蛋白质组学、转录组学和表观基因组学信息能够在同一组织切片上以单细胞分辨率进行整合。
