Single Cell RNA

Single-cell RNA sequencing (scRNA-seq) analyzes gene expression in individual cells, revealing cellular heterogeneity and facilitating a deeper understanding of biological processes. Current research focuses on improving data analysis through advanced computational methods, including graph neural networks, diffusion models, and large language models, to address challenges like data sparsity, noise, and the need for automated cell type annotation. These advancements are significantly impacting biological research by enabling more accurate and efficient characterization of cellular populations, leading to improved insights in areas such as developmental biology, disease mechanisms, and drug discovery. The development of robust and scalable algorithms is a key focus to handle the ever-increasing size and complexity of scRNA-seq datasets.