Department of Control Science and Engineering, Tongji University, No. 4800 Cao'an Road, 201804, Shanghai, China.
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Lane 55, Chuanhe Road, 201210, Shanghai, China.
Brief Bioinform. 2024 May 23;25(4). doi: 10.1093/bib/bbae255.
Spatially resolved transcriptomics data are being used in a revolutionary way to decipher the spatial pattern of gene expression and the spatial architecture of cell types. Much work has been done to exploit the genomic spatial architectures of cells. Such work is based on the common assumption that gene expression profiles of spatially adjacent spots are more similar than those of more distant spots. However, related work might not consider the nonlocal spatial co-expression dependency, which can better characterize the tissue architectures. Therefore, we propose MuCoST, a Multi-view graph Contrastive learning framework for deciphering complex Spatially resolved Transcriptomic architectures with dual scale structural dependency. To achieve this, we employ spot dependency augmentation by fusing gene expression correlation and spatial location proximity, thereby enabling MuCoST to model both nonlocal spatial co-expression dependency and spatially adjacent dependency. We benchmark MuCoST on four datasets, and we compare it with other state-of-the-art spatial domain identification methods. We demonstrate that MuCoST achieves the highest accuracy on spatial domain identification from various datasets. In particular, MuCoST accurately deciphers subtle biological textures and elaborates the variation of spatially functional patterns.
空间分辨转录组学数据正在被以一种革命性的方式用于破译基因表达的空间模式和细胞类型的空间结构。已经做了很多工作来利用细胞的基因组空间结构。这类工作基于一个共同的假设，即空间上相邻的点的基因表达谱比更远的点的更相似。然而，相关工作可能没有考虑到非局部空间共表达依赖性，这种依赖性可以更好地描述组织架构。因此，我们提出了 MuCoST，一种用于破译具有双尺度结构依赖性的复杂空间分辨转录组学结构的多视图图对比学习框架。为了实现这一点，我们通过融合基因表达相关性和空间位置邻近性来进行点依赖性增强，从而使 MuCoST 能够同时建模非局部空间共表达依赖性和空间相邻依赖性。我们在四个数据集上对 MuCoST 进行了基准测试，并将其与其他最先进的空间域识别方法进行了比较。我们证明了 MuCoST 在来自各种数据集的空间域识别上达到了最高的准确性。特别是，MuCoST 准确地破译了微妙的生物学纹理，并详细阐述了空间功能模式的变化。
