Single Cell

Single-cell analysis focuses on understanding the heterogeneity of individual cells within a population, aiming to decipher cellular functions and behaviors in health and disease. Current research heavily utilizes deep learning models, including variational autoencoders, transformers, and graph neural networks, to analyze high-dimensional single-cell data (e.g., RNA sequencing, imaging) and integrate information across multiple modalities. These advancements enable improved cell type identification, trajectory inference, and prediction of cellular responses to perturbations, with significant implications for disease diagnostics, drug discovery, and personalized medicine.