Cell-to-cell communication reveals a dynamic cellular ecosystem that develops, evolves, and responds to environmental factors. The role of cell-to-cell communication has been extensively investigated, particularly in cancer. Breakthroughs arising from discoveries in cell-to-cell communication have led to important clinical applications in cancer therapy. Most existing ligand-receptor interaction studies only utilize single-modal omics data, which only offers a limited view of the cellular communications. The combination of multi-omics data provides information that is more than the sum of its parts and opens new opportunities to comprehensively characterize the cell interactions in tumor ecosystems. This project proposes to address key computational challenges when analyzing multi-omics data in deciphering cell-to-cell communications. By developing and applying innovative statistical and machine learning methods to multi-omics datasets, both newly generated and publicly available, we will discover novel ligand-receptor pairs and provide insights into their mechanisms that empower precision therapeutic targeting of a broad array of complex human diseases.

In collaboration with: Linghua Wang