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10. A network regularized linear model to infer spatial expression pattern for single cell

    This article has 3 authors:

    1. Chaohao Gu
    2. Hu Chen
    3. Zhandong Liu

    This article has been curated by 1 group:

    • Curated by eLife

      eLife Assessment

      The development of glmSMA represents a valuable advancement in spatial transcriptomics analysis, offering a mathematically robust regression-based approach that achieves higher-resolution mapping of single-cell RNA sequencing data to spatial locations than existing methods. The evidence is convincing, as the authors demonstrate the method's superiority by formulating it as a convex optimization problem that ensures stable solutions, coupled with successful validation across multiple biological systems. The rigorous mathematical framework and validation across diverse tissues enable precise spatial mapping of cellular heterogeneity at enhanced resolution.

    Reviewed by eLife