Single Cell Multi Omics

Single-cell multi-omics integrates data from multiple molecular assays (e.g., genomics, transcriptomics, epigenomics) measured within individual cells to provide a holistic view of cellular heterogeneity and function. Current research focuses on developing advanced computational methods, including variational autoencoders, kernel-based approaches, and contrastive learning frameworks, to effectively integrate these high-dimensional, often noisy datasets, address missing data, and uncover causal relationships between genotype, environment, and phenotype. These advancements enable more accurate cell type identification, improved clustering, and enhanced prediction of cellular responses to perturbations, ultimately accelerating biological discovery and impacting areas like personalized medicine and disease modeling.