{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T07:16:49Z","timestamp":1784099809227,"version":"3.55.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":39,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62350087"],"award-info":[{"award-number":["62350087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62132015"],"award-info":[{"award-number":["62132015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2037"],"award-info":[{"award-number":["U22A2037"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002277"],"award-info":[{"award-number":["62002277"]}],"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:p>Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.<\/jats:p>","DOI":"10.1093\/bib\/bbae063","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T08:55:23Z","timestamp":1709283323000},"source":"Crossref","is-referenced-by-count":5,"title":["Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics"],"prefix":"10.1093","volume":"25","author":[{"given":"Lanying","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an 710100 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxuan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an 710100 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6396-0787","authenticated-orcid":false,"given":"Lin","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xidian University , Xi\u2019an 710100 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,2,28]]},"reference":[{"key":"2024040614554002400_ref1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1038\/s41592-020-01033-y","article-title":"Method of the Year 2020: spatially resolved transcriptomics","volume":"18","author":"Marx","year":"2021","journal-title":"Nat Methods"},{"issue":"1","key":"2024040614554002400_ref2","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s13059-022-02653-7","article-title":"Statistical and machine learning methods for spatially resolved transcriptomics data analysis","volume":"23","author":"Zeng","year":"2022","journal-title":"Genome Biol"},{"issue":"1","key":"2024040614554002400_ref3","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1038\/s42003-022-03175-5","article-title":"Deciphering tissue structure and function using spatial transcriptomics","volume":"5","author":"Walker","year":"2022","journal-title":"Commun Biol"},{"issue":"7871","key":"2024040614554002400_ref4","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1038\/s41586-021-03634-9","article-title":"Exploring tissue architecture using spatial transcriptomics","volume":"596","author":"Rao","year":"2021","journal-title":"Nature"},{"issue":"6","key":"2024040614554002400_ref5","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1038\/s41587-022-01448-2","article-title":"The expanding vistas of spatial transcriptomics","volume":"41","author":"Tian","year":"2023","journal-title":"Nat Biotechnol"},{"issue":"1","key":"2024040614554002400_ref6","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1038\/s41467-022-28020-5","article-title":"Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data","volume":"13","author":"Danaher","year":"2022","journal-title":"Nat Commun"},{"issue":"6","key":"2024040614554002400_ref7","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"},{"issue":"6294","key":"2024040614554002400_ref8","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"},{"issue":"6434","key":"2024040614554002400_ref9","doi-asserted-by":"crossref","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"},{"issue":"3","key":"2024040614554002400_ref10","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"},{"issue":"1","key":"2024040614554002400_ref11","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.tibtech.2020.05.006","article-title":"Uncovering an organ\u2019s molecular architecture at single-cell resolution by spatially resolved transcriptomics","volume":"39","author":"Liao","year":"2021","journal-title":"Trends Biotechnol"},{"issue":"5","key":"2024040614554002400_ref12","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1038\/s41592-022-01409-2","article-title":"Museum of spatial transcriptomics","volume":"19","author":"Moses","year":"2022","journal-title":"Nat Methods"},{"issue":"3","key":"2024040614554002400_ref13","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1038\/s41587-021-01182-1","article-title":"Spatial components of molecular tissue biology","volume":"40","author":"Palla","year":"2022","journal-title":"Nat Biotechnol"},{"issue":"4","key":"2024040614554002400_ref14","doi-asserted-by":"crossref","first-page":"bbac245","DOI":"10.1093\/bib\/bbac245","article-title":"A comprehensive comparison on cell-type composition inference for spatial transcriptomics data","volume":"23","author":"Chen","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024040614554002400_ref15","doi-asserted-by":"crossref","first-page":"785290","DOI":"10.3389\/fgene.2021.785290","article-title":"Analysis and visualization of spatial transcriptomic data","volume":"12","author":"Liu","year":"2022","journal-title":"Front Genet"},{"issue":"10","key":"2024040614554002400_ref16","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1038\/s41576-021-00370-8","article-title":"Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics","volume":"22","author":"Longo","year":"2021","journal-title":"Nat Rev Genet"},{"issue":"9","key":"2024040614554002400_ref17","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"},{"issue":"1","key":"2024040614554002400_ref18","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"},{"issue":"9","key":"2024040614554002400_ref19","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"},{"issue":"1","key":"2024040614554002400_ref20","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"},{"issue":"4","key":"2024040614554002400_ref21","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"},{"issue":"5","key":"2024040614554002400_ref22","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"},{"issue":"7","key":"2024040614554002400_ref23","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"},{"issue":"5","key":"2024040614554002400_ref24","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"},{"issue":"1","key":"2024040614554002400_ref25","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1038\/s41467-023-37168-7","article-title":"A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics","volume":"14","author":"Li","year":"2023","journal-title":"Nat Commun"},{"key":"2024040614554002400_ref26","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.csbj.2022.12.001","article-title":"Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome","volume":"21","author":"Zhang","year":"2022","journal-title":"Comput Struct Biotechnol J"},{"key":"2024040614554002400_ref27","article-title":"Spotless: a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics","volume":"12","author":"Sang-aram","year":"2023","journal-title":"Elife"},{"issue":"1","key":"2024040614554002400_ref28","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1186\/s13059-023-03123-4","article-title":"Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets","volume":"24","author":"Maden","year":"2023","journal-title":"Genome Biol"},{"issue":"1","key":"2024040614554002400_ref29","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1038\/s41467-020-15816-6","article-title":"Accurate estimation of cell composition in bulk expression through robust integration of single-cell information","volume":"11","author":"Jew","year":"2020","journal-title":"Nat Commun"},{"issue":"11","key":"2024040614554002400_ref30","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1093\/bioinformatics\/bty019","article-title":"Computational deconvolution of transcriptomics data from mixed cell populations","volume":"34","author":"Avila Cobos","year":"2018","journal-title":"Bioinformatics"},{"key":"2024040614554002400_ref31","first-page":"601","article-title":"Correcting sample selection bias by unlabeled data","author":"Sch\u00f6lkopf","year":"2007","journal-title":"Adv Neural Inf Process Syst"},{"issue":"7751","key":"2024040614554002400_ref32","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"},{"issue":"6226","key":"2024040614554002400_ref33","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1126\/science.aaa1934","article-title":"Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq","volume":"347","author":"Zeisel","year":"2015","journal-title":"Science"},{"issue":"10","key":"2024040614554002400_ref34","doi-asserted-by":"crossref","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"},{"issue":"4","key":"2024040614554002400_ref35","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1016\/j.cell.2018.06.021","article-title":"Molecular architecture of the mouse nervous system","volume":"174","author":"Zeisel","year":"2018","journal-title":"Cell"},{"issue":"6","key":"2024040614554002400_ref36","doi-asserted-by":"crossref","first-page":"e8746","DOI":"10.15252\/msb.20188746","article-title":"Current best practices in single-cell RNA-seq analysis: a tutorial","volume":"15","author":"Luecken","year":"2019","journal-title":"Mol Syst Biol"},{"issue":"28","key":"2024040614554002400_ref37","doi-asserted-by":"crossref","first-page":"E6437","DOI":"10.1073\/pnas.1721085115","article-title":"Gene expression distribution deconvolution in single-cell RNA sequencing","volume":"115","author":"Wang","year":"2018","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2024040614554002400_ref38","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J Mach Learn Res"},{"issue":"3","key":"2024040614554002400_ref39","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/s41587-019-0392-8","article-title":"Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas","volume":"38","author":"Moncada","year":"2020","journal-title":"Nat Biotechnol"},{"issue":"7","key":"2024040614554002400_ref40","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1016\/j.cell.2019.11.025","article-title":"A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart","volume":"179","author":"Asp","year":"2019","journal-title":"Cell"},{"issue":"11","key":"2024040614554002400_ref41","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1038\/s41587-021-00935-2","article-title":"Spatial transcriptomics at subspot resolution with BayesSpace","volume":"39","author":"Zhao","year":"2021","journal-title":"Nat Biotechnol"},{"issue":"10","key":"2024040614554002400_ref42","doi-asserted-by":"crossref","first-page":"2689","DOI":"10.1016\/j.celrep.2018.11.034","article-title":"Single-cell RNA-seq of mouse olfactory bulb reveals cellular heterogeneity and activity-dependent molecular census of adult-born neurons","volume":"25","author":"Tepe","year":"2018","journal-title":"Cell Rep"},{"issue":"1","key":"2024040614554002400_ref43","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s40537-022-00652-w","article-title":"Transfer learning: a friendly introduction","volume":"9","author":"Hosna","year":"2022","journal-title":"J Big Data"},{"issue":"3","key":"2024040614554002400_ref44","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.1007\/s11063-021-10719-z","article-title":"Deep transfer learning in mechanical intelligent fault diagnosis: application and challenge","volume":"54","author":"Qian","year":"2022","journal-title":"Neural Process Lett"},{"key":"2024040614554002400_ref45","doi-asserted-by":"crossref","first-page":"106985","DOI":"10.1016\/j.infsof.2022.106985","article-title":"A three-stage transfer learning framework for multi-source cross-project software defect prediction","volume":"150","author":"Bai","year":"2022","journal-title":"Inf Softw Technol"},{"issue":"10","key":"2024040614554002400_ref46","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1038\/s42256-020-00233-7","article-title":"Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis","volume":"2","author":"Hu","year":"2020","journal-title":"Nat Mach Intell"},{"issue":"1","key":"2024040614554002400_ref47","doi-asserted-by":"crossref","first-page":"bbad426","DOI":"10.1093\/bib\/bbad426","article-title":"Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids","volume":"25","author":"Wang","year":"2023","journal-title":"Brief Bioinform"},{"issue":"1","key":"2024040614554002400_ref48","doi-asserted-by":"crossref","first-page":"7640","DOI":"10.1038\/s41467-022-35288-0","article-title":"Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding","volume":"13","author":"Shen","year":"2022","journal-title":"Nat Commun"},{"key":"2024040614554002400_ref49","doi-asserted-by":"crossref","first-page":"107274","DOI":"10.1016\/j.compbiomed.2023.107274","article-title":"Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity","volume":"164","author":"Halawani","year":"2023","journal-title":"Comput Biol Med"},{"key":"2024040614554002400_ref50","first-page":"1","article-title":"A novel dynamic multiobjective optimization algorithm with non-inductive transfer learning based on multi-strategy adaptive selection","volume":"PP","author":"Li","year":"2023","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"2024040614554002400_ref51","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2021","journal-title":"Proc IEEE Inst Electr Electron Eng"},{"issue":"2","key":"2024040614554002400_ref52","first-page":"40","article-title":"A review of deep transfer learning and recent advancements","volume":"11","author":"Iman","year":"2023","journal-title":"Dent Tech"},{"key":"2024040614554002400_ref53","first-page":"1","article-title":"Weakly-supervised transfer learning with application in precision medicine","author":"Mao","year":"2023","journal-title":"IEEE Trans Autom Sci Eng"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/2\/bbae063\/57169462\/bbae063.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/25\/2\/bbae063\/57169462\/bbae063.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T15:10:25Z","timestamp":1712416225000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbae063\/7615968"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,22]]},"references-count":53,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,1,22]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbae063","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":"bbae063"}}