Duan Ziheng, Li Xi, Zhang Zhuoyang, Song James, Zhang Jing
University of California, Irvine.
Emory University.
Proc Int Conf Image Proc. 2025 Sep;2025:19-24. doi: 10.1109/icip55913.2025.11084277. Epub 2025 Aug 18.
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 (ffusion model for patial transcriptomics data 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.
空间转录组学能够在组织的空间背景下研究基因表达，为组织结构和功能提供有价值的见解。然而，技术限制可能导致大量数据缺失区域，这阻碍了准确的下游分析和生物学解释。为应对这些挑战，我们提出了用于空间转录组学数据填补的扩散模型（DISCO），这是一个具有三个关键特征的框架。首先，DISCO采用基于图神经网络的区域编码器来整合来自观察区域的空间和基因表达信息，生成指导缺失区域预测的潜在表示。其次，它使用两个扩散模块：一个位置扩散模块来预测缺失区域的空间布局，以及一个基因表达扩散模块来根据预测坐标生成基因表达谱。第三，DISCO在推理过程中纳入相邻区域信息以指导去噪过程，确保平滑过渡和生物学上连贯的结果。我们在多个测序平台、物种和数据集上验证了DISCO，证明了其在重建大型缺失区域方面的有效性。DISCO作为开源软件实现，为研究人员提供了一个强大的工具，以提高数据完整性并推进空间转录组学研究。
