{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yuan X"],"funding":["National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["5700"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11228050"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["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 me"],"journal":["Nature communications"],"pubmed_title":["HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics."],"pmcid":["PMC11228050"],"funding_grant_id":["12171318"],"pubmed_authors":["Gao R","Ma S","Wei T","Ma Y","Yuan X","Cui S","Wang Y","Fa B","Yu Z"],"additional_accession":[]},"is_claimable":false,"name":"HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics.","description":"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 me","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2025-04-26T05:41:13.087Z","creation":"2025-04-06T11:35:14.205Z"},"accession":"S-EPMC11228050","cross_references":{"pubmed":["38972896"],"doi":["10.1038/s41467-024-49846-1"]}}