Cell-to-cell communication is mainly triggered by ligand-receptor activities. Through ligand-receptor pairs, cells coordinate complex processes such as development, homeostasis, and immune response. In this work, we model the ligand-receptor-mediated cell-to-cell communication network as a weighted directed hypergraph. In this mathematical model, collaborating cell types are considered as a node community while the ligand-receptor pairs connecting them are considered a hyperedge community. We first define the community structures in a weighted directed hypergraph and develop an exact community detection method to identify these communities. We then modify approximate community detection algorithms designed for simple graphs to identify the nodes and hyperedges within each community. Application to synthetic hypergraphs with known community structure confirmed that one of the proposed approximate community identification strategies, named HyperCommunity algorithm, can effectively and precisely detect embedded communities. We then applied this strategy to two organism-wide datasets and identified putative community structures. Notably the method identifies non-overlapping edge-communities mediated by different sets of ligand-receptor pairs, however node-communities can overlap.

中文翻译：

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单元间通信网络的加权有向超图表示中的社区检测

细胞与细胞之间的通讯主要是由配体-受体的活性触发的。通过配体-受体对，细胞可以协调复杂的过程，例如发育，体内平衡和免疫反应。在这项工作中，我们将配体-受体介导的细胞间通信网络建模为加权有向超图。在此数学模型中，协作细胞类型被视为节点社区，而连接它们的配体-受体对被视为超边缘社区。我们首先在加权有向超图中定义社区结构，并开发一种精确的社区检测方法来识别这些社区。然后，我们修改为简单图设计的近似社区检测算法，以识别每个社区内的节点和超边缘。在已知社区结构的合成超图上的应用证实，提出的近似社区识别策略之一称为HyperCommunity算法，可以有效，精确地检测嵌入式社区。然后，我们将此策略应用于两个有机物范围的数据集，并确定了推定的群落结构。值得注意的是，该方法识别由不同组的配体-受体对介导的非重叠边缘社区，但是节点社区可以重叠。