Abstract
Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, and immune response. Recent computational methods have leveraged single-cell RNA sequencing (scRNA-seq) to infer CCC via ligand-receptor interactions (LRIs), with most approaches focusing on pairwise interactions. However, many biological processes are driven by the coordinated action of multiple cell types, underscoring the need to model higher-order cellular interactions beyond pairwise interactions. Inspired by principles of network diffusion and epidemic dynamics, we first model the receptor expression as: a Poisson-distributed random variable biologically regulated by the collective signaling of multiple ligand-producing cell types. Then, we propose Hi-C3, a unified statistical inference-based framework for inferring both conventional pairwise and new higher-order CCC patterns from scRNA-seq data, which is solved via an efficient likelihood-based (EM) algorithm. Particularly, Hi-C3 employed a modified PageRank algorithm to assess the importance of individual cells or cell types within the higher-order network, revealing key cellular communication hubs supported by independent spatial and biological evidence. When applied to diverse datasets from Arabidopsis thaliana and colorectal cancer, Hi-C3 achieved comparable performance to state-of-the-art methods in the inferring pairwise communication while uniquely uncovering complex higher-order cellular communication structures. Collectively, Hi-C3 offers a powerful statistical model and computational framework for uncovering complex and coordinated multicellular signaling structures disregarded by pairwise communication inference methods, and offers novel biology network insights into the logic of cellular organization and communication in both development and disease.