Abstract Spatial transcriptomics (ST) offers unprecedented opportunities to decode the spatial organization of gene expression, yet the inherent noise and complexity of ST data pose substantial challenges for accurate analysis. Here, we present DACN, a unified framework that integrates an improved adversarial autoencoder (AAE) with a graph convolutional network (GCN) to robustly analyze ST data across varying resolutions and throughputs. DACN employs a hybrid encoder that couples multi-head attention with residual connections to capture fine-grained local expression patterns while retaining critical global information. The hybrid encoder and generator jointly construct the AAE module, which denoises expression profiles and learns stable latent representations. The GCN component further exploits spatial neighborhood relationships to refine these embeddings. Across multiple ST datasets with varying resolutions, DACN consistently outperforms existing methods in accuracy and robustness. All code and datasets are publicly available at https://github.com/lanbiolab/DACN.
Lan et al. (Thu,) studied this question.