biostudies-literatureYuan XNational Natural Science Foundation of China (National Science Foundation of China)5700https://www.ebi.ac.uk/biostudies/studies/S-EPMC11228050biostudies-literatureUnknown15(1)Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F1 scores (average F1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative meNature communicationsHEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.PMC1122805012171318Gao RMa SWei TMa YYuan XCui SWang YFa BYu ZfalseHEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F1 scores (average F1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative me2024-01-01T00:00:00Z2024 Jul2025-04-26T05:41:13.087Z2025-04-06T11:35:14.205ZS-EPMC112280503897289610.1038/s41467-024-49846-1