SpaConTDS: A multimodal contrastive learning framework for identifying spatial domains by applying tuple disturbing strategy.

ABSTRACT: The rational utilization of multimodal spatial transcriptomics (ST) data enables accurate identification of spatial domains, which is essential for investigating cellular structure and functions. In this study, we proposed SpaConTDS, a novel framework that integrates reinforcement learning with self-supervised multimodal contrastive learning. SpaConTDS generates positive and negative samples through data augmentation and a pseudo-label tuple perturbation strategy, enabling the learning of fused representations that capture global semantics and cross-modal interactions. The model's hyper-parameters are dynamically optimized using reinforcement learning. Extensive experiments across various resolutions and platforms demonstrate that SpaConTDS achieves state-of-the-art accuracy in spatial dom

SUBMITTER: Xu R 

PROVIDER: S-EPMC12854462 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature