  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 31 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.
