Generalizable Single-cell Multimodal Data Integration with Self-supervised Learning

Recent breakthroughs in single-cell multi-omics technologies have enabled simultaneous measurement of diverse cellular modalities, offering unprecedented biological insights. However, integrating such multimodal data faces dual challenges: Small-scale paired-modality studies (hundreds of cells) risk overfitting, while large-scale reference atlases often struggle to generalize effectively to new datasets. To overcome these challenges, we present multimodal integration with self-supervised learning (MINERVA), a unified deep learning framework employing self-supervised strategies for single-cell multimodal integration. MINERVA outperforms six state-of-the-art methods in dimensionality reduction, missing feature imputation, and batch effect correction, even with limited training cells. For large-scale applications, MINERVA constructs scalable multi-tissue references that support zero-shot knowledge transfer to unseen datasets, instant cell type annotation, novel cell states identification, and comprehensive downstream analyses, all without model retraining. Uniquely bridging small-scale precision with atlas-level generalization, MINERVA serves as a versatile tool for both de novo integration and cost-effective atlas reuse in single-cell research.