SeqCCC: A Sequence-Informed Ligand-Receptor Interaction Prediction Method for Cell-Cell Communication Inference

Cell-cell communications (CCCs) mediated by ligand-receptor interactions (LRIs) play a pivotal role in coordination and function of biological systems. The two primary steps of CCC inference methods are usually filtering significant LRIs and measuring the intercellular communication strength. Biological language models have demonstrated notable progress in bioinformatics across several biological problems, inspiring by the success of language models in the field of natural language processing. Here, we proposed a computational approach for CCC inference called SeqCCC. First, SeqCCC employed two sophisticated protein language models and the protein sequences of ligands and receptors to predict potential LRIs with the help of existing LRIs. Then, SeqCCC employed permutation test to filter significantly expressed LRIs from the single-cell expression matrix based on these known and predicted LRIs. Finally, SeqCCC calculated communication strength by applying the molecular diffusion and law of mass action in chemistry, and then it improves this by eliminating non-specific CCCs using a permutation test. In the results, SeqCCC yielded an average AUC of 0.93 (mean of the five folds, with a standard deviation of 0.02). The validity and reliability of SeqCCC are further supported by a comparative analysis with common CCC inference methods, which reveals high concordance in inference results. Additionally, SeqCCC provided different visualization methods for CCC results, including Circos Plot, Heatmap Plot, Dot Plot and Heatmap for LRIs. In conclusion, SeqCCC provides a new option for CCC inference by fusing biological language models with sequence information to improve our comprehension of intercellular communications.