multiVIB: A unified probabilistic contrastive learning framework for atlas-scale integration of single-cell multi-omics data

Abstract

Comprehensive brain cell atlases are essential for understanding neural function and advancing translational research. As single-cell technologies proliferate across platforms, species, and modalities, atlas construction increasingly depends on integration frameworks that are capable of aligning heterogeneous datasets without obscuring biological variations. However, existing methods are typically limited to narrow use cases, often requiring ad hoc workflows that may introduce artifacts. Here, we introduce multiVIB, a unified probabilistic contrastive learning framework for diverse scenarios of data integration. We demonstrate that multiVIB achieves state-of-the-art performance while minimizing spurious alignments. Applied to atlas-scale datasets generated by the BRAIN Initiative, multiVIB supports robust and scalable integration across data modalities and preserves species-specific variation in cross-species analyses. These results position multiVIB as a scalable, biologically faithful framework for the construction of next-generation brain cell atlases.