Abstract
Integrating single-cell datasets from multiple studies provides a cost-effective way to build comprehensive cell atlases, granting deeper insights into cellular characteristics across diverse biological systems. However, current data integration methods struggle with interference in partially overlapping datasets and varying annotation granularities. Here, a multiselective adversarial network is introduced for the first time and present UniMap, which functions as a "discerner" to identify and exclude interfering cells from various data sources during dataset integration. Compared to other state-of-the-art methods, UniMap emphasizes type-level integration and proves to be the best model for preserving biological variability, achieving noticeably higher accuracy in single-cell automated annotation under various circumstances. Additionally, it enhances interpretability by revealing shared and domain-specific cell types and providing prediction confidence. The efficacy of UniMap is demonstrated in terms of identifying new cell types, creating high-resolution cell atlases, annotating cells along developmental trajectories, and performing cross-species analysis, underscoring its potential as a robust tool for single-cell research.