Abstract
As a fundamental characteristic of multicellular organisms, cell-cell communication is achieved through ligand-receptor (L-R) interactions, enabling the exchange of information and revealing the diversity of biological processes and cellular functions. To gain a comprehensive understanding of these complex interaction mechanisms, we constructed a manually curated L-R interaction database and developed a semi-supervised graph embedding model called scSDNE for inferring cell-cell interactions mediated by L-R interactions. scSDNE model utilizes the power of deep learning to map genes from interacting cells into a shared latent space, allowing for a nuanced representation of their relationships. Leveraging the prior information provided by database, scSDNE can infer significant L-R pairs involved in intercellular communication. Experiments on real single-cell RNA sequencing (scRNA-seq) datasets demonstrate that our method detects interactions with a high degree of reliability compared with other methods. More importantly, the model integrates gene regulation information within cells to enhance the accuracy and biological interpretability of the inferences. Our method provides a more comprehensive view of cell-cell interactions, offering new insights into complex intercellular communication.