GraphCellNet: A deep learning method for integrated single-cell and spatial transcriptomic analysis with applications in development and disease.

Ruoyan Dai, Zhenghui Wang, Zhiwei Zhang, Lixin Lei, Mengqiu Wang, Kaitai Han, Zijun Wang, Zhenxing Li, Jirui Zhang, Qianjin Guo
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Abstract

Spatial transcriptomics (ST) integrates gene expression with spatial location, enabling precise mapping of cellular distributions and interactions within tissues, and is a key tool for understanding tissue structure and function. Single-cell RNA sequencing (scRNA-seq) data enhances spatial transcriptomics by providing accurate cell type deconvolution, yet existing methods still face accuracy challenges. We propose GraphCellNet, a model combining cell type deconvolution and spatial domain identification, featuring the Kolmogorov-Arnold Network layer (KAN) to enhance nonlinear feature representation and contextual integration. This design addresses ambiguous cell boundaries and high heterogeneity, improving analytical precision. Evaluated using metrics like Pearson correlation coefficient (PCC), structural similarity index (SSIM), root mean square error (RMSE), Jensen-Shannon divergence (JSD), and Adjusted Rand Index (ARI), GraphCellNet has been applied to various systems, yielding new insights. In myocardial infarction, it identified spatial regions with high Trem2 expression associated with metabolic gene signatures in the infarcted heart. In Drosophila development, it uncovered TWEEDLE dynamics. In human heart development, it identified cell compositions and spatial organization across stages, deepening understanding of cellular spatial dynamics and informing regenerative medicine. KEY MESSAGES: A novel deep learning architecture that effectively captures cellular composition and spatial organization in tissue samples. An innovative KAN layer design that improves the modeling of nonlinear gene expression relationships while maintaining computational efficiency. A graph-based spatial domain identification method that leverages the spatial relationships of cell type information to enhance domain recognition accuracy. Demonstration of the framework's applicability in various biological applications, providing new insights into tissue organization and development.

GraphCellNet:一种用于综合单细胞和空间转录组学分析的深度学习方法,应用于发育和疾病。
空间转录组学(ST)将基因表达与空间定位相结合,能够精确定位细胞分布和组织内的相互作用,是了解组织结构和功能的关键工具。单细胞RNA测序(scRNA-seq)数据通过提供准确的细胞类型反褶积来增强空间转录组学,但现有方法仍然面临准确性挑战。本文提出了一种结合细胞类型反卷积和空间域识别的GraphCellNet模型,该模型采用Kolmogorov-Arnold网络层(KAN)来增强非线性特征表示和上下文集成。该设计解决了细胞边界模糊和异质性高的问题,提高了分析精度。使用诸如Pearson相关系数(PCC)、结构相似性指数(SSIM)、均方根误差(RMSE)、Jensen-Shannon散度(JSD)和调整后兰德指数(ARI)等指标进行评估,GraphCellNet已应用于各种系统,产生了新的见解。在心肌梗死中,它确定了与梗死心脏代谢基因特征相关的Trem2高表达的空间区域。在果蝇的发育过程中,它揭示了TWEEDLE动力学。在人类心脏发育中,它识别了不同阶段的细胞组成和空间组织,加深了对细胞空间动力学的理解,并为再生医学提供了信息。关键信息:一种新颖的深度学习架构,可以有效地捕获组织样本中的细胞组成和空间组织。一种创新的KAN层设计,改进了非线性基因表达关系的建模,同时保持了计算效率。一种基于图的空间域识别方法,利用细胞类型信息的空间关系来提高域识别精度。展示了该框架在各种生物学应用中的适用性,为组织组织和发育提供了新的见解。
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