AGCLD: an adaptive graph contrastive learning method with denoising for spatial domain identification.

Single-cell spatial multi-omics technologies enable the simultaneous acquisition of multimodal molecular profiles and spatial location information in situ, providing a novel perspective for spatial domain identification and functional characterization of tissues. However, existing methods still suffer from several limitations, including insufficient denoising capability for single-cell data, reliance on static graph structures, and inadequate exploitation of the complementary relationships between spatial information and molecular features. To address these challenges, we propose AGCLD, an adaptive graph contrastive learning method with denoising for spatial domain identification. Specifically, a modality-specific denoising variational autoencoder is first employed to learn robust latent representations, thereby effectively mitigating noise interference. A differentiable graph generator is then introduced to adaptively construct spatial adjacency graphs and expression similarity graphs, alleviating the bias introduced by fixed neighborhood assumptions. Finally, AGCLD utilizes a dual-graph attention network to encode the spatial adjacency and expression similarity graphs, yielding spatial and feature embeddings, and incorporates a contrastive learning mechanism to align the dual-view representations, thereby enhancing representation consistency. Extensive experiments on five spatial multi-omics datasets demonstrate that AGCLD outperforms state-of-the-art methods, including SpatialGlue, in spatial domain identification tasks.