{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T06:05:50Z","timestamp":1792389950368,"version":"4.4.0"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T00:00:00Z","timestamp":1751500800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072210"],"award-info":[{"award-number":["62072210"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Spatial multi-omics technologies provide valuable data on gene expression from various omics in the same tissue section while preserving spatial information. However, deciphering spatial domains within spatial omics data remains challenging due to the sparse gene expression. We propose spaLLM, the first multi-omics spatial domain analysis method that integrates large language models to enhance data representation. Our method combines a pre-trained single-cell language model (scGPT) with graph neural networks and multi-view attention mechanisms to compensate for limited gene expression information in spatial omics while improving sensitivity and resolution within modalities. SpaLLM processes multiple spatial modalities, including RNA, chromatin, and protein data, potentially adapting to emerging technologies and accommodating additional modalities. Benchmarking against eight state-of-the-art methods across four different datasets and platforms demonstrates that our model consistently outperforms other advanced methods across multiple supervised evaluation metrics. The source code for spaLLM is freely available at https:\/\/github.com\/liiilongyi\/spaLLM.<\/jats:p>","DOI":"10.1093\/bib\/bbaf304","type":"journal-article","created":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T07:31:36Z","timestamp":1749972696000},"source":"Crossref","is-referenced-by-count":17,"title":["spaLLM: enhancing spatial domain analysis in multi-omics data through large language model integration"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6572-3544","authenticated-orcid":false,"given":"Longyi","family":"Li","sequence":"first","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University , 2699 Qianjin Street, Changchun 130012, Jilin ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyan","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University , 2699 Qianjin Street, Changchun 130012, Jilin ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2058-7123","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University , 2699 Qianjin Street, Changchun 130012, Jilin ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4809-0514","authenticated-orcid":false,"given":"Dong","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri , 1201 Rollins Street, Columbia, MO 65211 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongli","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northeast Normal University , 5268 Renmin Street, Changchun 130117, Jilin ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,7,3]]},"reference":[{"key":"2025070309295600600_ref1","doi-asserted-by":"publisher","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":"2025070309295600600_ref2","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1126\/science.aaw1219","article-title":"Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution","volume":"363","author":"Rodriques","year":"2019","journal-title":"Science"},{"key":"2025070309295600600_ref3","doi-asserted-by":"publisher","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":"2025070309295600600_ref4","doi-asserted-by":"publisher","first-page":"12139","DOI":"10.1038\/ncomms12139","article-title":"Laser capture microscopy coupled with smart-seq2 for precise spatial transcriptomic profiling","volume":"7","author":"Nichterwitz","year":"2016","journal-title":"Nat Commun"},{"key":"2025070309295600600_ref5","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1016\/j.cell.2022.04.003","article-title":"Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays","volume":"185","author":"Chen","year":"2022","journal-title":"Cell"},{"key":"2025070309295600600_ref6","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1016\/j.cell.2020.10.026","article-title":"High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue","volume":"183","author":"Liu","year":"2020","journal-title":"Cell"},{"key":"2025070309295600600_ref7","doi-asserted-by":"publisher","first-page":"8353","DOI":"10.1038\/s41467-023-43458-x","article-title":"High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis","volume":"14","author":"Janesick","year":"2023","journal-title":"Nat Commun"},{"key":"2025070309295600600_ref8","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/s41587-022-01536-3","article-title":"Integration of whole transcriptome spatial profiling with protein markers","volume":"41","author":"Ben-Chetrit","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2025070309295600600_ref9","doi-asserted-by":"publisher","DOI":"10.1101\/2023.04.28.538364","article-title":"Integrated spatial transcriptomic and proteomic analysis of fresh frozen tissue based on stereo-seq","author":"Liao","year":"2023","journal-title":"bioRxiv"},{"key":"2025070309295600600_ref10","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1038\/s41586-023-05795-1","article-title":"Spatial epigenome\u2013transcriptome co-profiling of mammalian tissues","volume":"616","author":"Zhang","year":"2023","journal-title":"Nature"},{"key":"2025070309295600600_ref11","doi-asserted-by":"publisher","first-page":"1405","DOI":"10.1038\/s41587-023-01676-0","article-title":"High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq","volume":"41","author":"Liu","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2025070309295600600_ref12","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1038\/s41592-023-01884-1","article-title":"Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development","volume":"20","author":"Jiang","year":"2023","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref13","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1038\/s41467-022-28445-y","article-title":"SM-omics is an automated platform for high-throughput spatial multi-omics","volume":"13","author":"Vickovic","year":"2022","journal-title":"Nat Commun"},{"key":"2025070309295600600_ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-024-01205-7","article-title":"Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing","volume":"8","author":"Wu","year":"2024","journal-title":"Nat Biomed Eng"},{"key":"2025070309295600600_ref15","doi-asserted-by":"publisher","DOI":"10.1101\/2024.07.28.605493","article-title":"Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping","author":"Zhang","year":"2024","journal-title":"bioRxiv"},{"key":"2025070309295600600_ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02257-y","article-title":"Dependency-aware deep generative models for multitasking analysis of spatial omics data","volume":"21","author":"Tian","year":"2024","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref17","doi-asserted-by":"publisher","first-page":"5618","DOI":"10.1038\/s41467-023-41437-w","article-title":"SiGra: single-cell spatial elucidation through an image-augmented graph transformer","volume":"14","author":"Tang","year":"2023","journal-title":"Nat Commun"},{"key":"2025070309295600600_ref18","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1038\/s41467-023-36796-3","article-title":"Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST","volume":"14","author":"Long","year":"2023","journal-title":"Nat Commun"},{"key":"2025070309295600600_ref19","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41592-021-01255-8","article-title":"SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network","volume":"18","author":"Hu","year":"2021","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref20","doi-asserted-by":"publisher","first-page":"bbad048","DOI":"10.1093\/bib\/bbad048","article-title":"Identifying spatial domain by adapting transcriptomics with histology through contrastive learning","volume":"24","author":"Zeng","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025070309295600600_ref21","doi-asserted-by":"publisher","first-page":"bbad146","DOI":"10.1093\/bib\/bbad146","article-title":"Graph deep learning enabled spatial domains identification for spatial transcriptomics","volume":"24","author":"Liu","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025070309295600600_ref22","doi-asserted-by":"publisher","first-page":"bbad278","DOI":"10.1093\/bib\/bbad278","article-title":"Identifying spatial domains of spatially resolved transcriptomics via multi-view graph convolutional networks","volume":"24","author":"Shi","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025070309295600600_ref23","doi-asserted-by":"publisher","first-page":"bbae259","DOI":"10.1093\/bib\/bbae259","article-title":"SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network","volume":"25","author":"Si","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025070309295600600_ref24","doi-asserted-by":"publisher","first-page":"bbae329","DOI":"10.1093\/bib\/bbae329","article-title":"Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster","volume":"25","author":"Wang","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025070309295600600_ref25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41592-024-02316-4","article-title":"Deciphering spatial domains from spatial multi-omics with SpatialGlue","volume":"21","author":"Long","year":"2024","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref26","doi-asserted-by":"publisher","first-page":"943","DOI":"10.1038\/s41586-024-07563-1","article-title":"Multiscale topology classifies cells in subcellular spatial transcriptomics","volume":"630","author":"Benjamin","year":"2024","journal-title":"Nature"},{"key":"2025070309295600600_ref27","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1038\/s41592-024-02363-x","article-title":"Unlocking the power of spatial omics with AI","volume":"21","author":"Coleman","year":"2024","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref28","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.1038\/s41586-023-06311-1","article-title":"Spatially resolved multiomics","volume":"20","author":"Tang","year":"2023","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref29","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","volume":"618","author":"Theodoris","year":"2023","journal-title":"Nature"},{"key":"2025070309295600600_ref30","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s42256-022-00534-z","article-title":"scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data","volume":"4","author":"Yang","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2025070309295600600_ref31","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1038\/s41592-024-02201-0","article-title":"scGPT: toward building a foundation model for single-cell multi-omics using generative AI","volume":"21","author":"Cui","year":"2024","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref32","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1038\/s41592-024-02305-7","article-title":"Large-scale foundation model on single-cell transcriptomics","volume":"21","author":"Hao","year":"2024","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref33","doi-asserted-by":"publisher","first-page":"bbad077","DOI":"10.1093\/bib\/bbad077","article-title":"NeuroPred-PLM: an interpretable and robust model for neuropeptide prediction by protein language model","volume":"24","author":"Wang","year":"2023","journal-title":"Brief Bioinform"},{"key":"2025070309295600600_ref34","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans Inf Theory"},{"key":"2025070309295600600_ref35","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.5555\/1953048.2078195","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"Fabian","year":"2011","journal-title":"J Mach Learn Res"},{"key":"2025070309295600600_ref36","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1038\/s41467-021-22197-x","article-title":"scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses","volume":"12","author":"Wang","year":"2021","journal-title":"Nat Commun"},{"key":"2025070309295600600_ref37","doi-asserted-by":"publisher","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2025070309295600600_ref38","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1038\/s41592-020-01050-x","article-title":"Joint probabilistic modeling of single-cell multi-omic data with totalVI","volume":"18","author":"Gayoso","year":"2021","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref39","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1038\/s41592-023-01909-9","article-title":"MultiVI: deep generative model for the integration of multimodal data","volume":"20","author":"Ashuach","year":"2023","journal-title":"Nat Methods"},{"key":"2025070309295600600_ref40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13059-020-02015-1","article-title":"MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data","volume":"21","author":"Argelaguet","year":"2020","journal-title":"Genome Biol"},{"key":"2025070309295600600_ref41","doi-asserted-by":"publisher","first-page":"100071","DOI":"10.1016\/j.crmeth.2021.100071","article-title":"A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data","volume":"1","author":"Minoura","year":"2021","journal-title":"Cell Rep Methods"},{"key":"2025070309295600600_ref42","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1038\/s41587-023-01766-z","article-title":"Stabilized mosaic single-cell data integration using unshared features","volume":"42","author":"Ghazanfar","year":"2024","journal-title":"Nat Biotechnol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/4\/bbaf304\/63657927\/bbaf304.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/4\/bbaf304\/63657927\/bbaf304.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T09:30:05Z","timestamp":1751535005000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf304\/8184496"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf304","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,7]]},"published":{"date-parts":[[2025,7]]},"article-number":"bbaf304"}}