Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
Bioinformatics. 2024 Sep 1;40(Suppl 2):ii137-ii145. doi: 10.1093/bioinformatics/btae394.
Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue's intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a graph attention network designed for spatial transcriptomics data integration with unbalanced optimal transport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes.
Graspot software is available at https://github.com/zhan009/Graspot.
空间转录组学（ST）技术能够在测量 mRNA 表达的同时捕获斑点位置。通过整合 ST 数据，可以重建组织的 3D 结构，从而全面了解组织的复杂性。然而，仍然存在一个计算挑战：如何在保留 ST 数据中真实生物结构变化的同时去除批次效应。为了解决这个问题，我们引入了 Graspot，这是一种用于空间转录组学数据整合的图注意力网络，具有不平衡最优传输功能。Graspot 巧妙地利用基因表达和空间信息来对齐多个 ST 数据集的共同结构。它将多个 ST 数据集嵌入到一个统一的潜在空间中，促进了不同切片之间的部分对齐。在四个真实的 ST 数据集上的实验结果表明，与现有方法相比，Graspot 在 ST 数据集成方面表现出色，包括需要部分对齐的任务。特别是，Graspot 可以有效地整合多个 ST 切片并指导坐标对齐。此外，Graspot 可以准确地对齐时空转录组学数据，以重建人类心脏发育过程。
Graspot 软件可在 https://github.com/zhan009/Graspot 上获得。
