College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Department of Obstetrics and Gynecology, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, China.
Brief Bioinform. 2023 Nov 22;25(1). doi: 10.1093/bib/bbad469.
With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integrating scRNA-seq and ST data into a unified graph structure, GTAD outperforms traditional 'pseudo-ST' methods, providing robust and information-rich results. GTAD performs exceptionally well with synthesized spatial data and accurately identifies cell spatial composition in tissues like the mouse cerebral cortex, cerebellum, developing human heart and pancreatic ductal carcinoma. GTAD holds the potential to enhance our understanding of tissue microenvironments and cellular diversity in complex bio-logical systems. The source code is available at https://github.com/zzhjs/GTAD.
随着空间转录组测序（ST-seq）的出现，研究现在 heavily relies 依靠 ST-seq 和单细胞 RNA 测序（scRNA-seq）数据的联合分析，以精确识别组织中的细胞空间组成。然而，将这些数据集联合起来的常用方法通常会将来自多个细胞的数据合并，以生成 pseudo-ST 数据，从而忽略了拓扑关系，无法准确地表示空间排列。我们引入了 GTAD，这是一种利用图注意网络对整合的 scRNA-seq 和 ST-seq 数据进行去卷积的方法。GTAD 有效地利用基于图的方法捕捉组织内细胞的空间关系和拓扑结构，增强了细胞类型的识别能力，使我们能够更好地理解复杂的组织细胞景观。通过将 scRNA-seq 和 ST 数据整合到一个统一的图结构中，GTAD 优于传统的“伪-ST”方法，提供了稳健和信息丰富的结果。GTAD 在合成的空间数据上表现出色，并能准确地识别像小鼠大脑皮层、小脑、发育中的人类心脏和胰腺导管腺癌等组织中的细胞空间组成。GTAD 有潜力增强我们对复杂生物系统中组织微环境和细胞多样性的理解。源代码可在 https://github.com/zzhjs/GTAD 获得。
