UniMap: Type-Level Integration Enhances Biological Preservation and Interpretability in Single-Cell Annotation.

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 anno

SUBMITTER: Hu H 

PROVIDER: S-EPMC12021081 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature