School of Mathematics and System Science, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi, Xinjiang 830017, China.
Institute of Physical Science and Information Technology, Anhui University, No. 111 Jiulong Road, Shushan District, Hefei, Anhui 230601, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae436.
Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand-receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell-cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications.
细胞间通讯对于细胞生命的调控和细胞关系的建立至关重要。大多数从单细胞 RNA 测序 (scRNA-seq) 数据推断细胞间通讯的方法缺乏对多层次通讯的全面全局网络视图。在这种情况下，我们提出了 scHyper，这是一种从全局网络角度推断细胞间通讯并识别所有细胞、配体和受体表达对通讯评分潜在影响的新方法。scHyper 通过提取包含源（配体表达）、目标（受体表达）和相关配体-受体 (L-R) 对的异构超图，设计了一种表示三分体关系的新方法。scHyper 基于超图表示学习，该方法衡量节点内在属性（静态嵌入）与其在上下文中（超边）的观察行为（动态嵌入）之间的匹配程度，量化形成超边的概率，并因此重建细胞间通讯评分。此外，为了有效地挖掘信号传输的关键机制，我们收集了丰富的多亚基复杂 L-R 对数据集，并提出了一种非参数检验来确定显著的细胞间通讯。与其他工具的比较表明，scHyper 表现出优越的性能和功能。在人类肿瘤微环境和免疫细胞上的实验结果表明，scHyper 为分析细胞间通讯网络提供了可靠且独特的能力。因此，我们引入了一种有效的策略，可以构建高阶交互模式，超越大多数只能处理低阶交互的方法的限制，从而更准确地解释细胞间通讯的复杂性。
