Su Haohao, Wu Yuesong, Chen Bin, Cui Yuehua
Department of Statistics and Probability, Michigan State University, East Lansing, 48824, MI, USA.
Department of Pharmacology and Toxicology, Michigan State University, East Lansing, 48824, MI, USA.
Nat Commun. 2025 Feb 20;16(1):1793. doi: 10.1038/s41467-025-57117-w.
One of the major challenges in spatial transcriptomics is to detect spatially variable genes (SVGs), whose expression patterns are non-random across tissue locations. Many SVGs correlate with cell type compositions, introducing the concept of cell type-specific SVGs (ctSVGs). Existing ctSVG detection methods treat cell type-specific spatial effects as fixed effects, leading to tissue spatial rotation-dependent results. Moreover, SVGs may exhibit random spatial patterns within cell types, meaning an SVG is not always a ctSVG, and vice versa, further complicating detection. We propose STANCE, a unified statistical model for both SVGs and ctSVGs detection under a linear mixed-effect model framework that integrates gene expression, spatial location, and cell type composition information. STANCE ensures tissue rotation-invariant results, with a two-stage approach: initial SVG/ctSVG detection followed by ctSVG-specific testing. We demonstrate its performance through extensive simulations and analyses of public datasets. Downstream analyses reveal STANCE's potential in spatial transcriptomics analysis.
空间转录组学的主要挑战之一是检测空间可变基因（SVG），其表达模式在组织位置上是非随机的。许多SVG与细胞类型组成相关，从而引入了细胞类型特异性SVG（ctSVG）的概念。现有的ctSVG检测方法将细胞类型特异性空间效应视为固定效应，导致结果依赖于组织空间旋转。此外，SVG在细胞类型内可能表现出随机空间模式，这意味着一个SVG并不总是一个ctSVG，反之亦然，这进一步使检测变得复杂。我们提出了STANCE，这是一个在线性混合效应模型框架下用于SVG和ctSVG检测的统一统计模型，该框架整合了基因表达、空间位置和细胞类型组成信息。STANCE通过两阶段方法确保组织旋转不变的结果：首先进行SVG/ctSVG检测，然后进行ctSVG特异性测试。我们通过广泛的模拟和对公共数据集的分析证明了它的性能。下游分析揭示了STANCE在空间转录组学分析中的潜力。
