Building a pre-trained model for single-cell multi-omics analysis beyond highly variable feature selection

Researchers developed scPMMI, a pre-trained multimodal model that integrates single-cell multi-omics data without heavy reliance on feature selection, improving analysis across various biological tasks. It processes large feature sets by grouping them spatially, enabling better cell representation and transfer learning for tasks like cell type annotation and trajectory reconstruction.