Single Cell Data

Single-cell data analysis focuses on understanding cellular heterogeneity by analyzing the unique molecular profiles of individual cells, aiming to reveal underlying biological mechanisms and disease processes. Current research heavily utilizes deep learning approaches, including graph neural networks, transformers, and generative models like flow-based networks, to address challenges such as data integration, cell type identification, and the inference of dynamic gene regulatory networks. These advancements enable more accurate modeling of complex biological systems, facilitating improved diagnostics, drug discovery, and a deeper understanding of cellular processes in health and disease.