MUSE: A Multi-slice Joint Analysis Method for Spatial Transcriptomics Experiments.

ABSTRACT: Recent advances in spatial transcriptomics (ST) and cost reductions have enabled large-scale multi-slice ST data generation, enhancing the statistical power to detect subtle biological signals. However, cross-slice inconsistencies and data quality variability present significant analytical challenges. To overcome these limitations, we developed MUSE, a computational framework designed for multislice joint embedding, spatial domain identification, and gene expression imputation. Specifically, MUSE integrates a two-module architecture to ensure robust cross-slice alignment and data harmonization. The alignment module models each slice as a graph and employs optimal transport to align cells across slices while preserving spatial continuity. The optimization module further refines integration

SUBMITTER: Duan Z 

PROVIDER: S-EPMC12790625 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature