Researchers at UCSD have developed advanced tools for single-cell ATAC-seq data analysis, which have been highlighted in a recent benchmarking study. Additionally, scMODAL, a deep learning framework, has been designed to align multi-omics single-cell data, addressing the challenge of integrating unpaired datasets with limited known correlated features. scMODAL can project different single-cell datasets into a low-dimensional latent space and apply GANs to align cell embeddings, utilizing prior information from known linked features to identify anchor cell pairs while preserving the topology structure of all input features. scChat, another innovative platform, combines quantitative statistical learning algorithms and large language models to offer contextualized scRNA-seq data analysis. Furthermore, SAFAARI employs an adversarial domain adaptation strategy to integrate single-cell data and annotate cell types, even in the presence of batch effects and biological domain shifts.