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<p>Recent advancements in spatially resolved transcriptomics (SRT) technologies have enabled the comprehensive molecular and spatial characterization of single cells, providing valuable insights into the cellular organization of tissues. SRT techniques, such as single-molecule fluorescence in situ hybridization (FISH)-based methods (e.g., seqFISH, STARmap) and next-generation sequencing (NGS)-based methods (e.g., spatial transcriptomics, 10x Visium), allow for the measurement of gene expression across large populations of cells or tissue spots. These approaches generate high-dimensional data that integrate both molecular profiles and spatial context, which is crucial for understanding tissue structure and function in areas like development, neuroscience, and cancer biology. Identifying spatially variable (SV) genes, whose expression patterns differ across spatial locations, is a key step in analyzing these complex spatial transcriptomic maps. To enhance our understanding of the spatial profiles of SV genes, we propose a Bayesian nonparametric zero-inflated Poisson (ZIP) regression model for clustering these genes. Our model explicitly accounts for zero-inflation in the data, uses non-negative matrix factorization to uncover gene expression patterns, and incorporates Moran’s I (MI) basis functions to address potential confounding. Additionally, the model infers the number of clusters directly from the data, obviating the need for pre-specifying the number of clusters. We demonstrate the utility of this approach on two SRT datasets, showing that it provides more robust and interpretable clustering of SV genes, opening new avenues for understanding complex biological processes.</p>

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Bayesian Subgroup Learning of Spatially Resolved Transcriptomics Data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • Hou-Cheng Yang,
  • Huimin Li,
  • Guanyu Hu,
  • Qiwei Li

摘要

Recent advancements in spatially resolved transcriptomics (SRT) technologies have enabled the comprehensive molecular and spatial characterization of single cells, providing valuable insights into the cellular organization of tissues. SRT techniques, such as single-molecule fluorescence in situ hybridization (FISH)-based methods (e.g., seqFISH, STARmap) and next-generation sequencing (NGS)-based methods (e.g., spatial transcriptomics, 10x Visium), allow for the measurement of gene expression across large populations of cells or tissue spots. These approaches generate high-dimensional data that integrate both molecular profiles and spatial context, which is crucial for understanding tissue structure and function in areas like development, neuroscience, and cancer biology. Identifying spatially variable (SV) genes, whose expression patterns differ across spatial locations, is a key step in analyzing these complex spatial transcriptomic maps. To enhance our understanding of the spatial profiles of SV genes, we propose a Bayesian nonparametric zero-inflated Poisson (ZIP) regression model for clustering these genes. Our model explicitly accounts for zero-inflation in the data, uses non-negative matrix factorization to uncover gene expression patterns, and incorporates Moran’s I (MI) basis functions to address potential confounding. Additionally, the model infers the number of clusters directly from the data, obviating the need for pre-specifying the number of clusters. We demonstrate the utility of this approach on two SRT datasets, showing that it provides more robust and interpretable clustering of SV genes, opening new avenues for understanding complex biological processes.