{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zhou Z"],"funding":["National Natural Science Foundation of China","National Natural Science Foundation of China (National Science Foundation of China)"],"pagination":["7930"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10692090"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(1)"],"pubmed_abstract":["Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch."],"journal":["Nature communications"],"pubmed_title":["Spatial transcriptomics deconvolution at single-cell resolution using Redeconve."],"pmcid":["PMC10692090"],"funding_grant_id":["92159305","31991171"],"pubmed_authors":["Ren X","Zhang Z","Zhong Y","Zhou Z"],"additional_accession":[]},"is_claimable":false,"name":"Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.","description":"Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Dec","modification":"2026-05-28T08:57:14.791Z","creation":"2025-02-19T01:18:34.518Z"},"accession":"S-EPMC10692090","cross_references":{"pubmed":["38040768"],"doi":["10.1038/s41467-023-43600-9"]}}