Multimodal Single Cell

Multimodal single-cell analysis integrates data from multiple sources (e.g., gene expression, protein levels, chromatin accessibility) measured within individual cells to provide a more comprehensive understanding of cellular states and functions. Current research focuses on developing sophisticated computational methods, including variational autoencoders, graph neural networks, and transformers, to effectively integrate these diverse data types, model their interrelationships, and perform downstream tasks like cell clustering and causal inference. These advancements are significantly improving our ability to analyze complex biological systems, with applications ranging from understanding cellular differentiation to identifying disease biomarkers and facilitating drug discovery.