Frequency-domain kernels enable atlas-scale detection of spatially variable genes

Yang, C.; Zhang, X.; Chen, J.

doi:10.64898/2026.03.12.711372

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bioRxiv · 10.64898/2026.03.12.711372

Frequency-domain kernels enable atlas-scale detection of spatially variable genes

Yang, C.·Zhang, X.·Chen, J.

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Abstract

Spatial transcriptomics links gene expression to tissue architecture, but detecting spatially variable genes at atlas scale remains difficult because biologically relevant patterns are multiscale, sparse, and often non-parametric. Here we show that FlashS, a frequency-domain kernel test using random Fourier features, sparse sketching, and a kurtosis-corrected null, retains Gaussian-kernel flexibility at near-linear per-gene cost without permutation. Across 50 benchmark datasets spanning 9 spatial transcriptomics platforms, FO_SCPLOWLASHC_SCPLOWS achieves the highest ranking accuracy among 14 compared methods while maintaining calibrated inference under simulation and full-atlas permutation. In human heart tissue, it recovers a mitochondrial biogenesis program that co-localizes with ventricular cardiomyocytes. On the Allen Brain MERFISH atlas containing 3.94 million cells, FO_SCPLOWLASHC_SCPLOWS completes in minutes on a single workstation and separates biological signal from negative-control barcodes even when p-values saturate.

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Yang, C., Zhang, X., Chen, J.. 2026-03-16. Frequency-domain kernels enable atlas-scale detection of spatially variable genes. https://doi.org/10.64898/2026.03.12.711372

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