scHyper: reconstructing cell-cell communication through hypergraph neural networks.

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.