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Creators/Authors contains: "Yu, Shan"

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  1. ; ; ; (, Journal of Nonparametric Statistics)
    Free, publicly-accessible full text available April 28, 2026
  2. ; ; ; (, Journal of Computational and Graphical Statistics)
    Free, publicly-accessible full text available April 17, 2026
  3. ; (, Genome Biology)
    Abstract Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC’s accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis. 
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    Free, publicly-accessible full text available December 1, 2025
  4. ; ; ; ; (, Journal of Nonparametric Statistics)
    Free, publicly-accessible full text available January 7, 2026
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