SPIRAL：整合和对齐不同实验、条件和技术下的空间分辨转录组学数据。

School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China.

MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Department of Automation, Tsinghua University, Beijing, 100084, China.

Genome Biol. 2023 Oct 20;24(1):241. doi: 10.1186/s13059-023-03078-6.

Properly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIRAL, consisting of two consecutive modules: SPIRAL-integration, with graph domain adaptation-based data integration, and SPIRAL-alignment, with cluster-aware optimal transport-based coordination alignment. We verify SPIRAL with both synthetic and real SRT datasets. By encoding spatial correlations to gene expressions, SPIRAL-integration surpasses state-of-the-art methods in both batch effect removal and joint spatial domain identification. By aligning spots cluster-wise, SPIRAL-alignment achieves more accurate coordinate alignments than existing methods.

将不同批次生成的空间转录组学（SRT）正确整合到统一的基因-空间坐标系统中，可以构建一个全面的空间转录组图谱。在这里，我们提出了 SPIRAL，它由两个连续的模块组成：SPIRAL-integration，基于图域自适应的数据集成；和 SPIRAL-alignment，基于聚类感知的最优传输协调对齐。我们使用合成和真实的 SRT 数据集来验证 SPIRAL。通过对空间相关性进行编码以得到基因表达，SPIRAL-integration 在去除批次效应和联合空间域识别方面均优于现有方法。通过逐点聚类对齐，SPIRAL-alignment 比现有方法实现了更精确的坐标对齐。