In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review \rv{33} state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.

翻译：在空间解析转录组学数据分析中，检测空间可变基因至关重要。现有众多计算方法，但由于SVG定义和方法论的差异，导致结果难以直接比较。本文系统评述了33种前沿方法，将SVGs归纳为三种类型：整体型、细胞类型特异性型及空间区域标记型SVGs。本综述阐释了这些方法的核心思想，总结了其应用场景，并根据其在SVG检测的普适性与特异性权衡中所采用的假设检验策略进行分类。我们探讨了SVG检测面临的挑战，并提出未来改进方向。本综述为方法开发者和使用者提供了重要见解，并倡导建立针对特定类别的基准测试体系。