Cell Population

Cell population analysis focuses on understanding the composition and dynamics of diverse cell groups, aiming to decipher cellular heterogeneity and its implications for biological processes and disease. Current research heavily utilizes machine learning, employing deep learning architectures like graph convolutional networks and variational autoencoders, along with novel algorithms such as kernel-based methods and multiple instance learning, to analyze high-dimensional single-cell data from various sources (e.g., RNA sequencing, flow cytometry, microscopy). These advancements enable more accurate and efficient identification of cell types, characterization of cellular hierarchies, and improved prediction of patient features from single-cell data, ultimately contributing to a deeper understanding of biological systems and improved diagnostics.