Modal-NexT: Towards unified heterogeneous cellular data integration

Unified integration of heterogeneous cellular data is the foundation for building artificial intelligence virtual cells (AIVCs). Although current Artificial Intelligence (AI) integration techniques are emerging in an endless stream, they are always isolated in different scenarios. To promote the development of the high-resolution biological data analysis field towards a multi-modal unified model, we propose Modal-Nexus Transductive learning (Modal-NexT), a unified and efficient integration paradigm. Modal-NexT summarizes four scenarios: paired multi-modal integration, unpaired multi-modal integration, spatial multi-modal integration, and multi-source integration. Modal-NexT utilizes transductive learning to capture the biological context on a unified cell-feature joint graph. We have collected benchmark-ready datasets for the four integration scenarios and established comprehensive integration benchmarks. Evaluations on these datasets have verified the accuracy and robustness of Modal-NexT in cellular data integration.