Abstract：
Spatial transcriptomics provides valuable insights into gene expression within the native tissue context, effectively merging molecular data with spatial information to uncover intricate cellular relationships and tissue organizations. In this context, deciphering cellular spatial domains becomes essential for revealing complex cellular dynamics and tissue structures. However, current methods encounter challenges in seamlessly integrating gene expression data with spatial information, resulting in less informative representations of spots and suboptimal accuracy in spatial domain identification. We introduce stCluster, a novel method that integrates graph contrastive learning with multi-task learning to refine informative representations for spatial transcriptomic data, consequently improving spatial domain identification. stCluster first leverages graph contrastive learning technology to obtain discriminative representations capable of recognizing spatially coherent patterns. Through jointly optimizing multiple tasks, stCluster further fine-tunes the representations to be able to capture complex relationships between gene expression and spatial organization. Benchmarked against six state-of-the-art methods, the experimental results reveal its proficiency in accurately identifying complex spatial domains across various datasets and platforms, spanning tissue, organ, and embryo levels. Moreover, stCluster can effectively denoise the spatial gene expression patterns and enhance the spatial trajectory inference. The source code of stCluster is freely available at https://github.com/hannshu/stCluster.
摘要：
空间转录组学提供了对天然组织环境中基因表达的有价值的见解，有效地将分子数据与空间信息合并，以揭示复杂的细胞关系和组织组织。在这种情况下，破译细胞空间域对于揭示复杂的细胞动力学和组织结构至关重要。然而,当前的方法在将基因表达数据与空间信息无缝集成方面面临挑战，导致斑点的信息表示较少，空间域识别的精度也不理想。我们引入stCluster，一种新颖的方法，将图对比学习与多任务学习相结合，以完善空间转录组数据的信息表示，从而改善空间域识别。stCluster首先利用图对比学习技术来获得能够识别空间相干模式的判别表示。通过联合优化多个任务，stCluster进一步微调表示，以便能够捕获基因表达和空间组织之间的复杂关系。以六种最先进的方法为基准，实验结果揭示了其在各种数据集和平台上准确识别复杂空间域的能力，跨越组织，器官,和胚胎水平。此外，stCluster可以有效的去噪空间基因表达模式，增强空间轨迹推断。stCluster的源代码可在https://github.com/hannshu/stCluster上免费获得。
