Multi-scale spatial testing recovers gene programs missed by existing detection methods

Abstract Identifying spatially variable genes (SVGs) is the first analytical step in spatial transcriptomics, determining which genes and pathways are prioritized for downstream validation. Yet the restricted spatial models of current detection methods create systematic blind spots that can exclude biologically coherent programs from discovery. Here we present FlashS, which reformulates kernel-based spatial testing in the frequency domain to detect arbitrary multi-scale expression patterns while scaling to millions of cells. In human cardiac tissue, this broader detection capacity recovers a coherent PGC-1α-regulated mitochondrial biogenesis program—40 of 49 pathway genes spatially associated with ventricular cardiomyocytes—that PreTSA, a leading parametric alternative, largely misses (1 of 49 genes), a finding replicated in an independent cohort. Across 50 benchmark datasets spanning 9 platforms, FlashS achieves state-of-the-art ranking accuracy (mean Kendall τ = 0.935) and completes on the Allen Brain MERFISH atlas (3.94 million cells) in 12.6 minutes with 21.5 GB memory. Competing Interest Statement The authors have declared no competing interest. Footnotes This version updates the manuscript title, abstract, funding disclosures, and narrative framing. The revised paper strengthens the biological validation in human heart tissue, expands pathway-level and module-completeness analyses, adds additional cross-dataset and cross-platform validation, and clarifies statistical calibration and scalability results. Figures, supplementary information, and manuscript metadata have also been updated to match the revised analysis and presentation.