Cell Clustering

Cell clustering aims to group similar cells based on their characteristics, facilitating the analysis of cellular heterogeneity and function across diverse biological contexts. Current research heavily utilizes deep learning methods, including graph convolutional networks, variational autoencoders, and contrastive learning approaches, often incorporating additional information like gene expression profiles or spatial arrangements to improve clustering accuracy and robustness. These advancements are significantly impacting fields like single-cell RNA sequencing analysis, enabling more precise identification of cell types and improved understanding of biological processes, as well as applications in medical image analysis and path planning. The development of robust and privacy-preserving methods is also a key focus.