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<p>With the rapid advancement of spatial multi-omics technologies, the simultaneous analysis of molecular profiles and spatial locations has provided unprecedented insights into cellular heterogeneity and tissue microenvironments. However, data sparsity and the diversity of data distributions hinder the effective integration and analysis of spatial multi-omics data. In this study, we propose a novel ensemble learning framework based on dual-graph regularized anchor concept factorization, named SMODEL, for detecting spatial domains from spatial multi-omics data. SMODEL employs an element-wise weighted ensemble strategy to integrate multiple base clustering results, and leverages anchor concept factorization and dual-graph regularization to learn robust spatial consensus representations. We evaluated the performance of SMODEL on both real and simulated spatial multi-omics datasets, encompassing various technologies, tissue types, and species. Experimental results demonstrate that SMODEL not only outperforms existing methods in spatial domain identification but also effectively captures tissue structure, thereby enhancing the understanding of cellular heterogeneity.</p>

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Unveiling spatial domains from spatial multi-omics data using dual-graph regularized ensemble learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月20日

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  • Ying Li,
  • Guangchang Cai,
  • Fuqun Chen,
  • Kepei Wen,
  • Le Ou-Yang

摘要

With the rapid advancement of spatial multi-omics technologies, the simultaneous analysis of molecular profiles and spatial locations has provided unprecedented insights into cellular heterogeneity and tissue microenvironments. However, data sparsity and the diversity of data distributions hinder the effective integration and analysis of spatial multi-omics data. In this study, we propose a novel ensemble learning framework based on dual-graph regularized anchor concept factorization, named SMODEL, for detecting spatial domains from spatial multi-omics data. SMODEL employs an element-wise weighted ensemble strategy to integrate multiple base clustering results, and leverages anchor concept factorization and dual-graph regularization to learn robust spatial consensus representations. We evaluated the performance of SMODEL on both real and simulated spatial multi-omics datasets, encompassing various technologies, tissue types, and species. Experimental results demonstrate that SMODEL not only outperforms existing methods in spatial domain identification but also effectively captures tissue structure, thereby enhancing the understanding of cellular heterogeneity.