{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:04:28Z","timestamp":1784631868513,"version":"3.55.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"2","funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF1200902"],"award-info":[{"award-number":["2021YFF1200902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31871342"],"award-info":[{"award-number":["31871342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The rapid development of spatial transcriptome technologies has enabled researchers to acquire single-cell-level spatial data at an affordable price. However, computational analysis tools, such as annotation tools, tailored for these data are still lacking. Recently, many computational frameworks have emerged to integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. While some frameworks can utilize well-annotated scRNA-seq data to annotate spatial expression patterns, they overlook critical aspects. First, existing tools do not explicitly consider cell type mapping when aligning the two modalities. Second, current frameworks lack the capability to detect novel cells, which remains a key interest for biologists.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address these problems, we propose an annotation method for spatial transcriptome data called SPANN. The main tasks of SPANN are to transfer cell-type labels from well-annotated scRNA-seq data to newly generated single-cell resolution spatial transcriptome data and discover novel cells from spatial data. The major innovations of SPANN come from two aspects: SPANN automatically detects novel cells from unseen cell types while maintaining high annotation accuracy over known cell types. SPANN finds a mapping between spatial transcriptome samples and RNA data prototypes and thus conducts cell-type-level alignment. Comprehensive experiments using datasets from various spatial platforms demonstrate SPANN\u2019s capabilities in annotating known cell types and discovering novel cell states within complex tissue contexts.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>The source code of SPANN can be accessed at https:\/\/github.com\/ddb-qiwang\/SPANN-torch.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>dengmh@math.pku.edu.cn.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbad533","type":"journal-article","created":{"date-parts":[[2024,1,27]],"date-time":"2024-01-27T07:38:50Z","timestamp":1706341130000},"source":"Crossref","is-referenced-by-count":8,"title":["SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data"],"prefix":"10.1093","volume":"25","author":[{"given":"Musu","family":"Yuan","sequence":"first","affiliation":[{"name":"Center for Quantitative Biology, Peking University , Yiheyuan Road, 100871, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Peking University , Yiheyuan Road, 100871, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihao","family":"Wang","sequence":"additional","affiliation":[{"name":"Biomedical Interdisciplinary Research Center, Peking University , Yiheyuan Road, 100871, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qirui","family":"Guo","sequence":"additional","affiliation":[{"name":"Center for Quantitative Biology, Peking University , Yiheyuan Road, 100871, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minghua","family":"Deng","sequence":"additional","affiliation":[{"name":"Center for Quantitative Biology, Peking University , Yiheyuan Road, 100871, Beijing , China"},{"name":"School of Mathematical Sciences, Peking University , Yiheyuan Road, 100871, Beijing , China"},{"name":"Center for Statistical Science, Peking University , Yiheyuan Road, 100871, Beijing , China"},{"name":"Biomedical Interdisciplinary Research Center, Peking University , Yiheyuan Road, 100871, Beijing , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,1,26]]},"reference":[{"key":"2024050311490197500_ref1","article-title":"A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics","volume":"14.1","author":"Li","year":"2023","journal-title":"Nat Commun"},{"key":"2024050311490197500_ref2","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1038\/nmeth.4636","article-title":"Spatialde: identification of spatially variable genes","volume":"15","author":"Svensson","year":"2017","journal-title":"Nat Methods"},{"key":"2024050311490197500_ref3","doi-asserted-by":"crossref","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":"Jian","year":"2021","journal-title":"Nat Methods"},{"key":"2024050311490197500_ref4","doi-asserted-by":"crossref","DOI":"10.1126\/science.aaa6090","article-title":"Spatially resolved, highly multiplexed rna profiling in single cells","volume":"348","author":"Chen","year":"2015","journal-title":"Science"},{"key":"2024050311490197500_ref5","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":"2024050311490197500_ref6","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":"2020","journal-title":"Nat Biotechnol"},{"key":"2024050311490197500_ref7","doi-asserted-by":"crossref","first-page":"eabp9444","DOI":"10.1126\/science.abp9444","article-title":"Single-cell stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration","volume":"377","author":"Wei","year":"2022","journal-title":"Science"},{"key":"2024050311490197500_ref8","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1093\/bioinformatics\/btab700","article-title":"Scmra: a robust deep learning method to annotate scrna-seq data with multiple reference datasets","volume":"38","author":"Yuan","year":"2021","journal-title":"Bioinformatics"},{"key":"2024050311490197500_ref9","doi-asserted-by":"crossref","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":"2020","journal-title":"Cell"},{"key":"2024050311490197500_ref10","doi-asserted-by":"crossref","DOI":"10.1038\/s41587-023-01767-y","article-title":"Dictionary learning for integrative, multimodal and scalable single-cell analysis","volume-title":"Nat Biotechnol","author":"Hao"},{"key":"2024050311490197500_ref11","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1162\/0899766042321814","article-title":"Canonical correlation analysis: an overview with application to learning methods","volume":"16","author":"Hardoon","year":"2004","journal-title":"Neural Comput"},{"key":"2024050311490197500_ref12","article-title":"Jointly defining cell types from multiple single-cell datasets using liger","volume":"15.11","author":"Liu","year":"2020","journal-title":"Nat Protoc"},{"key":"2024050311490197500_ref13","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TKDE.2012.51","article-title":"Nonnegative matrix factorization: a comprehensive review","volume":"25","author":"Wang","year":"2013","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2024050311490197500_ref14","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":"2024050311490197500_ref15","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1038\/s41592-021-01264-7","article-title":"Deep learning and alignment of spatially resolved single-cell transcriptomes with tangram","volume":"18","author":"Biancalani","year":"2021","journal-title":"Nat Methods"},{"key":"2024050311490197500_ref16","doi-asserted-by":"crossref","volume-title":"Optimal transport: Old and new","author":"Villani","DOI":"10.1007\/978-3-540-71050-9"},{"key":"2024050311490197500_ref17","article-title":"Inferring spatial and signaling relationships between cells from single cell transcriptomic data","volume":"11.1","author":"Cang","year":"2020","journal-title":"Nat Commun"},{"key":"2024050311490197500_ref18","doi-asserted-by":"crossref","first-page":"4177","DOI":"10.1038\/s41596-021-00573-7","article-title":"Novosparc: flexible spatial reconstruction of single-cell gene expression with optimal transport","volume":"16","author":"Moriel","year":"2021","journal-title":"Nat Protoc"},{"key":"2024050311490197500_ref19","article-title":"A unified computational framework for single-cell data integration with optimal transport. Nature","volume":"13.1","author":"Cao","year":"2022","journal-title":"Communications"},{"key":"2024050311490197500_ref20","article-title":"Unbalanced minibatch optimal transport; applications to domain adaptation","volume-title":"International Conference on Machine Learning","author":"Fatras"},{"key":"2024050311490197500_ref21","article-title":"Unified optimal transport framework for universal domain adaptation","author":"Chang"},{"key":"2024050311490197500_ref22","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.gpb.2022.12.008","article-title":"Scemail: universal and source-free annotation method for scrna-seq data with novel cell-type perception","volume":"20","author":"Wan","year":"2022","journal-title":"Genomics Proteomics Bioinformatics"},{"key":"2024050311490197500_ref23","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13059-023-02879-z","article-title":"Srtsim: spatial pattern preserving simulations for spatially resolved transcriptomics","volume":"24","author":"Zhu","year":"2023","journal-title":"Genome Biol"},{"key":"2024050311490197500_ref24","article-title":"Auto-encoding variational bayes","volume-title":"arXiv preprint","author":"Kingma"},{"key":"2024050311490197500_ref25","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2019.00753","article-title":"Domain-specific batch normalization for unsupervised domain adaptation","volume-title":"Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition","author":"Chang"},{"key":"2024050311490197500_ref26","article-title":"Deja vu: continual model generalization for unseen domains","volume-title":"arXiv preprint","author":"Liu"},{"key":"2024050311490197500_ref27","article-title":"Proxymix: proxy-based mixup training with label refinery for source-free domain adaptation","volume-title":"Neural Networks","author":"Ding"},{"key":"2024050311490197500_ref28","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"International conference on machine learning","author":"Verma"},{"key":"2024050311490197500_ref29","doi-asserted-by":"crossref","DOI":"10.3389\/fpsyg.2013.00700","article-title":"Good things peak in pairs: a note on the bimodality coefficient","volume":"4","author":"Pfister","year":"2013","journal-title":"Front Psychol"},{"key":"2024050311490197500_ref30","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1214\/aos\/1176346577","article-title":"The dip test of unimodality","volume":"13","author":"Hartigan","year":"1985","journal-title":"Ann Stat"},{"key":"2024050311490197500_ref31","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1038\/nbt.4314","article-title":"Dimensionality reduction for visualizing single-cell data using umap","volume":"37","author":"Becht","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2024050311490197500_ref32","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1109\/TSMC.1985.6313426","article-title":"A fuzzy k-nearest neighbor algorithm","volume":"SMC-15","author":"Keller","year":"1985","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"2024050311490197500_ref33","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","article-title":"Fast, sensitive, and accurate integration of single cell data with harmony","volume":"16","author":"Korsunsky","year":"2018","journal-title":"Nat Methods"},{"key":"2024050311490197500_ref34","article-title":"Tacco unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics","volume":"41","author":"Mages","journal-title":"Nat Biotechnol"},{"key":"2024050311490197500_ref35","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41587-021-01161-6","article-title":"Scjoint integrates atlas-scale single-cell rna-seq and atac-seq data with transfer learning","volume":"40","author":"Lin","year":"2022","journal-title":"Nat Biotechnol"},{"key":"2024050311490197500_ref36","article-title":"Cellcano: supervised cell type identification for single cell atac-seq data","volume":"14.1","author":"Ma","year":"2023","journal-title":"Nat Commun"},{"key":"2024050311490197500_ref37","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s42256-021-00432-w","article-title":"Cell type annotation of single-cell chromatin accessibility data via supervised bayesian embedding","volume":"4","author":"Chen","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2024050311490197500_ref38","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/nmeth.4207","article-title":"Visualization and analysis of single-cell rna-seq data by kernel-based similarity learning","volume":"14","author":"Wang","year":"2016","journal-title":"Nat Methods"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/2\/bbad533\/57395637\/bbad533.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/2\/bbad533\/57395637\/bbad533.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T11:51:42Z","timestamp":1714737102000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad533\/7590316"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad533","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,3,1]]},"published":{"date-parts":[[2024,1,22]]},"article-number":"bbad533"}}