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 functions and enabling translational insights. As single-cell technologies proliferate across experimental platforms, species, and modalities, these atlases must scale accordingly, calling for data integration framework that aligns heterogeneous datasets without erasing biologically meaningful variations. Existing tools typically address narrow integration settings, forcing researchers to assemble ad hoc workflows that may generate artifacts. Here, we introduce multiVIB, a unified probabilistic contrastive learning framework that handles diverse integration scenarios. We show that multiVIB achieves state-of-the-art performance while mitigating spurious alignments. Applied to atlas-scale datasets from the BRAIN I

SUBMITTER: Xu Y 

PROVIDER: S-EPMC12694596 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature