DISCO: A Diffusion Model For Spatial Transcriptomics Data Completion

Spatial transcriptomics enables the study of gene expression within the spatial context of tissues, offering valuable insights into tissue organization and function. However, technical limitations can result in large missing regions of data, which hinder accurate downstream analyses and biological interpretation. To address these challenges, we propose <i>DISCO</i> (<i>DI</i>ffusion model for <i>S</i>patial transcriptomics data <i>CO</i>mpletion), a framework with three key features. First, DISCO employs a graph neural network-based region encoder to integrate spatial and gene expression information from observed regions, generating latent representations that guide the prediction of missing regions. Second, it uses two diffusion modules: a position diffusion module to predict the spatial layout of missing regions, and a gene expression diffusion module to generate gene expression profiles conditioned on the predicted coordinates. Third, DISCO incorporates neighboring region information during inference to guide the denoising process, ensuring smooth transitions and biologically coherent results. We validate DISCO across multiple sequencing platforms, species, and datasets, demonstrating its effectiveness in reconstructing large missing regions. DISCO is implemented as open-source software, providing researchers with a powerful tool to enhance data completeness and advance spatial transcriptomics research.