While spatial transcriptomics offer valuable insights into gene expression patterns within the spatial context of tissue, many technologies do not have a single-cell resolution. Here, we present SMART, a marker gene-assisted deconvolution method that simultaneously infers the cell type-specific gene expression profile and the cellular composition at each spot. Using multiple datasets, we show that SMART outperforms the existing methods in realistic settings. It also provides a two-stage approach to enhance its performance on cell subtypes. The covariate model of SMART enables the identification of cell type-specific differentially expressed genes across conditions, elucidating biological changes at a single-cell-type resolution.

中文翻译：

---

SMART：使用标记基因辅助主题模型进行空间转录组学反卷积


虽然空间转录组学为组织空间背景下的基因表达模式提供了有价值的见解，但许多技术不具有单细胞分辨率。在这里，我们提出了 SMART，这是一种标记基因辅助反卷积方法，可同时推断细胞类型特异性基因表达谱和每个点的细胞组成。使用多个数据集，我们表明 SMART 在实际设置下优于现有方法。它还提供了一种两阶段方法来增强其对细胞亚型的性能。SMART 的协变量模型能够识别不同条件下的细胞类型特异性差异表达基因，从而在单细胞类型分辨率下阐明生物学变化。