{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["35(2)"],"submitter":["Zhang H"],"pubmed_abstract":["Spatial transcriptomics are a collection of technologies that have enabled characterization of gene expression profiles and spatial information in tissue samples. Existing methods for clustering spatial transcriptomics data have primarily focused on data transformation techniques to represent the data suitably for subsequent clustering analysis, often using an existing clustering algorithm. These methods have limitations in handling complex data characteristics with varying densities, sizes, and shapes (in the transformed space on which clustering is performed), and they have high computational complexity, resulting in unsatisfactory clustering outcomes and slow execution time even with GPUs. Rather than focusing on data transformation techniques, we propose a new clustering algorithm call"],"journal":["Genome research"],"pagination":["355-367"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11874963"],"repository":["biostudies-literature"],"pubmed_title":["Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains."],"pmcid":["PMC11874963"],"pubmed_authors":["Ting KM","Zhang J","Zhang Y","Zhao Q","Zhang H"],"additional_accession":[]},"is_claimable":false,"name":"Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains.","description":"Spatial transcriptomics are a collection of technologies that have enabled characterization of gene expression profiles and spatial information in tissue samples. Existing methods for clustering spatial transcriptomics data have primarily focused on data transformation techniques to represent the data suitably for subsequent clustering analysis, often using an existing clustering algorithm. These methods have limitations in handling complex data characteristics with varying densities, sizes, and shapes (in the transformed space on which clustering is performed), and they have high computational complexity, resulting in unsatisfactory clustering outcomes and slow execution time even with GPUs. Rather than focusing on data transformation techniques, we propose a new clustering algorithm call","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Feb","modification":"2025-04-03T23:43:11.91Z","creation":"2025-04-03T23:43:11.91Z"},"accession":"S-EPMC11874963","cross_references":{"pubmed":["39909714"],"doi":["10.1101/gr.278983.124"]}}