{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T20:54:13Z","timestamp":1771534453659,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Noncommunicable Chronic Diseases-National Science and Technology Major Project","award":["2024ZD0531100"],"award-info":[{"award-number":["2024ZD0531100"]}]},{"name":"Noncommunicable Chronic Diseases-National Science and Technology Major Project","award":["2024ZD0531103"],"award-info":[{"award-number":["2024ZD0531103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The software is coded using python, and is free available for academic https:\/\/github.com\/xkmaxidian\/jMF2D.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf419","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:51:09Z","timestamp":1753357869000},"source":"Crossref","is-referenced-by-count":2,"title":["Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model"],"prefix":"10.1093","volume":"41","author":[{"given":"Yuhong","family":"Zha","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an, Shaanxi 710071,","place":["China"]},{"name":"Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xidian University , Xi\u2019an, Shaanxi 710071,","place":["China"]}]},{"given":"Shaoqing","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Plastic and Reconstructive Surgery, Shanghai Ninth People\u2019s Hospital, Shanghai Jiaotong University , Shanghai 200011,","place":["China"]}]},{"given":"Peng","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Hematology, The First Affiliated Hospital of Xi\u2019an Jiaotong University , Xi\u2019an, Shaanxi 710061,","place":["China"]},{"name":"Genome Institute, The First Affiliated Hospital of Xi\u2019an Jiaotong University , Xi\u2019an, Shaanxi 710061,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China , Chengdu, Sichuan 611731,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5604-7137","authenticated-orcid":false,"given":"Xiaoke","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an, Shaanxi 710071,","place":["China"]},{"name":"Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xidian University , Xi\u2019an, Shaanxi 710071,","place":["China"]}]}],"member":"286","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"2025081213184507500_btaf419-B1","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1038\/s42003-020-01247-y","article-title":"Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography","volume":"3","author":"Andersson","year":"2020","journal-title":"Commun Biol"},{"key":"2025081213184507500_btaf419-B2","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1038\/s41587-021-00830-w","article-title":"Robust decomposition of cell type mixtures in spatial transcriptomics","volume":"40","author":"Cable","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2025081213184507500_btaf419-B3","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1126\/science.aak9787","article-title":"Single-cell whole-genome analyses by linear amplification via transposon insertion (lianti)","volume":"356","author":"Chen","year":"2017","journal-title":"Science"},{"key":"2025081213184507500_btaf419-B4","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1038\/s41592-018-0175-z","article-title":"Spatial organization of the somatosensory cortex revealed by osmfish","volume":"15","author":"Codeluppi","year":"2018","journal-title":"Nat Methods"},{"key":"2025081213184507500_btaf419-B5","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1007\/s12539-025-00700-y","article-title":"Self-supervised graph representation learning for single-cell classification","volume":"17","author":"Dai","year":"2025","journal-title":"Interdiscip Sci Comput Life Sci"},{"key":"2025081213184507500_btaf419-B6","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s13059-021-02362-7","article-title":"Spatialdwls: accurate deconvolution of spatial transcriptomic data","volume":"22","author":"Dong","year":"2021","journal-title":"Genome Biol"},{"key":"2025081213184507500_btaf419-B7","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-025-00688-5","article-title":"scrdit: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling","author":"Dong","year":"2025","journal-title":"Interdiscip Sci Comput Life Sci"},{"key":"2025081213184507500_btaf419-B8","doi-asserted-by":"crossref","first-page":"e50","DOI":"10.1093\/nar\/gkab043","article-title":"Spotlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes","volume":"49","author":"Elosua-Bayes","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025081213184507500_btaf419-B9","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1038\/s41586-019-1049-y","article-title":"Transcriptome-scale super-resolved imaging in tissues by rna seqfish","volume":"568","author":"Eng","year":"2019","journal-title":"Nature"},{"key":"2025081213184507500_btaf419-B10","first-page":"0522","article-title":"emci: an explainable multimodal correlation integration model for unveiling spatial transcriptomics and intercellular signaling","volume":"7","author":"Hong","year":"2024","journal-title":"Research (Wash D C)"},{"key":"2025081213184507500_btaf419-B11","first-page":"619","author":"Huang","year":"2024"},{"key":"2025081213184507500_btaf419-B12","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1038\/s41587-021-01139-4","article-title":"Cell2location maps fine-grained cell types in spatial transcriptomics","volume":"40","author":"Kleshchevnikov","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2025081213184507500_btaf419-B13","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1038\/44565","article-title":"Learning the parts of objects by non-negative matrix factorization","volume":"401","author":"Lee","year":"1999","journal-title":"Nature"},{"key":"2025081213184507500_btaf419-B14","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1038\/s41592-022-01480-9","article-title":"Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution","volume":"19","author":"Li","year":"2022","journal-title":"Nat Methods"},{"key":"2025081213184507500_btaf419-B15","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1038\/s41587-022-01272-8","article-title":"Destvi identifies continuums of cell types in spatial transcriptomics data","volume":"40","author":"Lopez","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2025081213184507500_btaf419-B16","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1016\/j.cell.2020.09.056","article-title":"Chromatin potential identified by shared single-cell profiling of rna and chromatin","volume":"183","author":"Ma","year":"2020","journal-title":"Cell"},{"key":"2025081213184507500_btaf419-B17","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1038\/s41587-022-01273-7","article-title":"Spatially informed cell-type deconvolution for spatial transcriptomics","volume":"40","author":"Ma","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2025081213184507500_btaf419-B18","doi-asserted-by":"crossref","first-page":"e1012881","DOI":"10.1371\/journal.pcbi.1012881","article-title":"Spamask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics","volume":"21","author":"Min","year":"2025","journal-title":"PLoS Comput Biol"},{"key":"2025081213184507500_btaf419-B19","doi-asserted-by":"crossref","first-page":"bbae551","DOI":"10.1093\/bib\/bbae551","article-title":"Multimodal contrastive learning for spatial gene expression prediction using histology images","volume":"25","author":"Min","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025081213184507500_btaf419-B20","first-page":"8584","author":"Min","year":"2022"},{"key":"2025081213184507500_btaf419-B21","doi-asserted-by":"crossref","first-page":"eaau5324","DOI":"10.1126\/science.aau5324","article-title":"Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region","volume":"362","author":"Moffitt","year":"2018","journal-title":"Science"},{"key":"2025081213184507500_btaf419-B22","doi-asserted-by":"crossref","first-page":"bbaa414","DOI":"10.1093\/bib\/bbaa414","article-title":"Dstg: deconvoluting spatial transcriptomics data through graph-based artificial intelligence","volume":"22","author":"Song","year":"2021","journal-title":"Brief Bioinform"},{"key":"2025081213184507500_btaf419-B23","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.aaf2403","article-title":"Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","volume":"353","author":"St\u00e5hl","year":"2016","journal-title":"Science"},{"key":"2025081213184507500_btaf419-B24","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1038\/s41587-020-0739-1","article-title":"Highly sensitive spatial transcriptomics at near-cellular resolution with slide-seqv2","volume":"39","author":"Stickels","year":"2021","journal-title":"Nat Biotechnol"},{"key":"2025081213184507500_btaf419-B25","doi-asserted-by":"crossref","first-page":"e42","DOI":"10.1093\/nar\/gkac150","article-title":"Stride: accurately decomposing and integrating spatial transcriptomics using single-cell rna sequencing","volume":"50","author":"Sun","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2025081213184507500_btaf419-B26","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1038\/s41588-021-00911-1","article-title":"A single-cell and spatially resolved atlas of human breast cancers","volume":"53","author":"Wu","year":"2021","journal-title":"Nat Genet"},{"key":"2025081213184507500_btaf419-B27","doi-asserted-by":"crossref","first-page":"7930","DOI":"10.1038\/s41467-023-43600-9","article-title":"Spatial transcriptomics deconvolution at single-cell resolution using redeconve","volume":"14","author":"Zhou","year":"2023","journal-title":"Nat Commun"},{"key":"2025081213184507500_btaf419-B28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12539-025-00728-0","article-title":"stgnn: spatially informed cell-type deconvolution based on deep graph learning and statistical modeling","author":"Zhu","year":"2025","journal-title":"Interdiscip Sci Comput Life Sci"},{"key":"2025081213184507500_btaf419-B29","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1007\/s12539-025-00702-w","article-title":"Scagcn: graph convolutional network with adaptive aggregation mechanism for scrna-seq data dimensionality reduction","volume":"17","author":"Zhu","year":"2025","journal-title":"Interdiscip Sci Comput Life Sci"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaf419\/63839149\/btaf419.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/8\/btaf419\/63839149\/btaf419.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/41\/8\/btaf419\/63839149\/btaf419.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T17:18:57Z","timestamp":1755019137000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btaf419\/8211672"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,7,24]]},"references-count":29,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf419","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,8]]},"published":{"date-parts":[[2025,7,24]]},"article-number":"btaf419"}}