{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T06:37:32Z","timestamp":1794551852478,"version":"4.9.1"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Genet"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1038\/s41588-026-02573-3","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T09:03:00Z","timestamp":1777021380000},"page":"1126-1137","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Mosaic integration of spatial multi-omics with SpaMosaic"],"prefix":"10.1038","volume":"58","author":[{"given":"Xuhua","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoyu","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kok Siong","family":"Ang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lynn van","family":"Olst","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Edwards","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Watson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6372-6798","authenticated-orcid":false,"given":"Ruiqing","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7805-8059","authenticated-orcid":false,"given":"Rong","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0188-1394","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0481-9657","authenticated-orcid":false,"given":"David","family":"Gate","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7547-6423","authenticated-orcid":false,"given":"Jinmiao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"2573_CR1","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1126\/science.abg7216","volume":"375","author":"Y Deng","year":"2022","unstructured":"Deng, Y. et al. Spatial-CUT&Tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681\u2013686 (2022).","journal-title":"Science"},{"key":"2573_CR2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1038\/s41586-023-05795-1","volume":"616","author":"D Zhang","year":"2023","unstructured":"Zhang, D. et al. Spatial epigenome-transcriptome co-profiling of mammalian tissues. Nature 616, 113\u2013122 (2023).","journal-title":"Nature"},{"key":"2573_CR3","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1038\/s41586-025-09663-y","volume":"647","author":"D Zhang","year":"2025","unstructured":"Zhang, D. et al. Spatial dynamics of brain development and neuroinflammation. Nature 647, 213\u2013227 (2025).","journal-title":"Nature"},{"key":"2573_CR4","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1038\/s41592-024-02576-0","volume":"22","author":"P Guo","year":"2025","unstructured":"Guo, P. et al. Multiplexed spatial mapping of chromatin features, transcriptome and proteins in tissues. Nat. Methods 22, 520\u2013529 (2025).","journal-title":"Nat. Methods"},{"key":"2573_CR5","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1038\/s41592-024-02316-4","volume":"21","author":"Y Long","year":"2024","unstructured":"Long, Y. et al. Deciphering spatial domains from spatial multi-omics with SpatialGlue. Nat. Methods 21, 1658\u20131667 (2024).","journal-title":"Nat. Methods"},{"key":"2573_CR6","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1038\/s41587-021-00895-7","volume":"39","author":"R Argelaguet","year":"2021","unstructured":"Argelaguet, R., Cuomo, A. S. E., Stegle, O. & Marioni, J. C. Computational principles and challenges in single-cell data integration. Nat. Biotechnol. 39, 1202\u20131215 (2021).","journal-title":"Nat. Biotechnol."},{"key":"2573_CR7","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-36066-2","volume":"14","author":"Z Zhang","year":"2023","unstructured":"Zhang, Z. et al. scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection. Nat. Commun. 14, 384 (2023).","journal-title":"Nat. Commun."},{"key":"2573_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-021-02556-z","volume":"22","author":"B Gong","year":"2021","unstructured":"Gong, B., Zhou, Y. & Purdom, E. Cobolt: integrative analysis of multimodal single-cell sequencing data. Genome Biol. 22, 351 (2021).","journal-title":"Genome Biol."},{"key":"2573_CR9","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1038\/s41587-023-01766-z","volume":"42","author":"S Ghazanfar","year":"2024","unstructured":"Ghazanfar, S., Guibentif, C. & Marioni, J. C. Stabilized mosaic single-cell data integration using unshared features. Nat. Biotechnol. 42, 284\u2013292 (2024).","journal-title":"Nat. Biotechnol."},{"key":"2573_CR10","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.1038\/s41587-023-02040-y","volume":"42","author":"Z He","year":"2024","unstructured":"He, Z. et al. Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS. Nat. Biotechnol. 42, 1594\u20131605 (2024).","journal-title":"Nat. Biotechnol."},{"key":"2573_CR11","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1038\/s41588-024-01664-3","volume":"56","author":"V Singhal","year":"2024","unstructured":"Singhal, V. et al. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat. Genet. 56, 431\u2013441 (2024).","journal-title":"Nat. Genet."},{"key":"2573_CR12","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-024-01283-x","volume":"16","author":"H Xu","year":"2024","unstructured":"Xu, H. et al. Unsupervised spatially embedded deep representation of spatial transcriptomics. Genome Med. 16, 12 (2024).","journal-title":"Genome Med."},{"key":"2573_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-36796-3","volume":"14","author":"Y Long","year":"2023","unstructured":"Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14, 1155 (2023).","journal-title":"Nat. Commun."},{"key":"2573_CR14","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1038\/s43588-023-00528-w","volume":"3","author":"X Zhou","year":"2023","unstructured":"Zhou, X., Dong, K. & Zhang, S. Integrating spatial transcriptomics data across different conditions, technologies and developmental stages. Nat. Comput. Sci. 3, 894\u2013906 (2023).","journal-title":"Nat. Comput. Sci."},{"key":"2573_CR15","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1038\/s42256-023-00734-1","volume":"5","author":"G Wang","year":"2023","unstructured":"Wang, G. et al. Construction of a 3D whole organism spatial atlas by joint modelling of multiple slices with deep neural networks. Nat. Mach. Intell. 5, 1200\u20131213 (2023).","journal-title":"Nat. Mach. Intell."},{"key":"2573_CR16","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/s41588-023-01588-4","volume":"56","author":"M Varrone","year":"2024","unstructured":"Varrone, M., Tavernari, D., Santamaria-Martinez, A., Walsh, L. A. & Ciriello, G. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat. Genet. 56, 74\u201384 (2024).","journal-title":"Nat. Genet."},{"key":"2573_CR17","doi-asserted-by":"publisher","unstructured":"Li, Z. et al. Cross-modality representation and multi-sample integration of spatially resolved omics data. Preprint at bioRxiv https:\/\/doi.org\/10.1101\/2024.06.10.598155 (2025).","DOI":"10.1101\/2024.06.10.598155"},{"key":"2573_CR18","unstructured":"Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. Proc. International Conference on Machine Learning (eds Daum\u00e9, H. & Singh, A.) 1597\u20131607 (PMLR, 2020)."},{"key":"2573_CR19","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","volume":"16","author":"I Korsunsky","year":"2019","unstructured":"Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289\u20131296 (2019).","journal-title":"Nat. Methods"},{"key":"2573_CR20","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","volume":"177","author":"T Stuart","year":"2019","unstructured":"Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888\u20131902 (2019).","journal-title":"Cell"},{"key":"2573_CR21","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/nbt.4091","volume":"36","author":"L Haghverdi","year":"2018","unstructured":"Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421\u2013427 (2018).","journal-title":"Nat. Biotechnol."},{"key":"2573_CR22","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1038\/s41592-022-01687-w","volume":"20","author":"FW Townes","year":"2023","unstructured":"Townes, F. W. & Engelhardt, B. E. Nonnegative spatial factorization applied to spatial genomics. Nat. Methods 20, 229\u2013238 (2023).","journal-title":"Nat. Methods"},{"key":"2573_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-34879-1","volume":"13","author":"L Shang","year":"2022","unstructured":"Shang, L. & Zhou, X. Spatially aware dimension reduction for spatial transcriptomics. Nat. Commun. 13, 7203 (2022).","journal-title":"Nat. Commun."},{"key":"2573_CR24","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41592-021-01336-8","volume":"19","author":"MD Luecken","year":"2022","unstructured":"Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41\u201350 (2022).","journal-title":"Nat. Methods"},{"key":"2573_CR25","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1038\/s41592-023-01884-1","volume":"20","author":"F Jiang","year":"2023","unstructured":"Jiang, F. et al. Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development. Nat. Methods 20, 1048\u20131057 (2023).","journal-title":"Nat. Methods"},{"key":"2573_CR26","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2023070118","volume":"118","author":"KE Wu","year":"2021","unstructured":"Wu, K. E., Yost, K. E., Chang, H. Y. & Zou, J. BABEL enables cross-modality translation between multiomic profiles at single-cell resolution. Proc. Natl Acad. Sci. USA 118, e2023070118 (2021).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2573_CR27","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1038\/s41592-020-01050-x","volume":"18","author":"A Gayoso","year":"2021","unstructured":"Gayoso, A. et al. Joint probabilistic modeling of single-cell multi-omic data with totalVI. Nat. Methods 18, 272\u2013282 (2021).","journal-title":"Nat. Methods"},{"key":"2573_CR28","unstructured":"Allen Institute for Brain Science. Allen Reference Atlas\u2014mouse brain. https:\/\/atlas.brain-map.org\/ (2011)."},{"key":"2573_CR29","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1038\/s41587-021-00865-z","volume":"39","author":"SJ Wu","year":"2021","unstructured":"Wu, S. J. et al. Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat. Biotechnol. 39, 819\u2013824 (2021).","journal-title":"Nat. Biotechnol."},{"key":"2573_CR30","doi-asserted-by":"publisher","first-page":"1802","DOI":"10.1523\/JNEUROSCI.1291-17.2018","volume":"38","author":"S Zhang","year":"2018","unstructured":"Zhang, S. et al. Sox2 is essential for oligodendroglial proliferation and differentiation during postnatal brain myelination and CNS remyelination. J. Neurosci. 38, 1802\u20131820 (2018).","journal-title":"J. Neurosci."},{"key":"2573_CR31","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1002\/dneu.22849","volume":"81","author":"E Sock","year":"2021","unstructured":"Sock, E. & Wegner, M. Using the lineage determinants Olig2 and Sox10 to explore transcriptional regulation of oligodendrocyte development. Dev. Neurobiol. 81, 892\u2013901 (2021).","journal-title":"Dev. Neurobiol."},{"key":"2573_CR32","doi-asserted-by":"publisher","DOI":"10.1186\/s13041-019-0440-9","volume":"12","author":"F Yang","year":"2019","unstructured":"Yang, F. et al. Proteomics of the corpus callosum to identify novel factors involved in hypomyelinated Niemann\u2013Pick type C disease mice. Mol. Brain 12, 17 (2019).","journal-title":"Mol. Brain"},{"key":"2573_CR33","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1038\/nn.4321","volume":"19","author":"S Quintes","year":"2016","unstructured":"Quintes, S. et al. Zeb2 is essential for Schwann cell differentiation, myelination and nerve repair. Nat. Neurosci. 19, 1050\u20131059 (2016).","journal-title":"Nat. Neurosci."},{"key":"2573_CR34","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1016\/j.devcel.2018.05.022","volume":"45","author":"C Zhao","year":"2018","unstructured":"Zhao, C. et al. Dual requirement of CHD8 for chromatin landscape establishment and histone methyltransferase recruitment to promote CNS myelination and repair. Dev. Cell 45, 753\u2013768 (2018).","journal-title":"Dev. Cell"},{"key":"2573_CR35","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-52025-x","volume":"15","author":"K Farrell","year":"2024","unstructured":"Farrell, K. et al. Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes. Nat. Commun. 15, 7880 (2024).","journal-title":"Nat. Commun."},{"key":"2573_CR36","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1523\/JNEUROSCI.4317-05.2006","volume":"26","author":"D Brockschnieder","year":"2006","unstructured":"Brockschnieder, D., Sabanay, H., Riethmacher, D. & Peles, E. Ermin, a myelinating oligodendrocyte-specific protein that regulates cell morphology. J. Neurosci. 26, 757\u2013762 (2006).","journal-title":"J. Neurosci."},{"key":"2573_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/srep20995","volume":"6","author":"J Morita","year":"2016","unstructured":"Morita, J. et al. Structure and biological function of ENPP6, a choline-specific glycerophosphodiester-phosphodiesterase. Sci. Rep. 6, 20995 (2016).","journal-title":"Sci. Rep."},{"key":"2573_CR38","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.614107","volume":"15","author":"X Chen","year":"2021","unstructured":"Chen, X., Lu, W. & Wu, D. Sirtuin 2 (SIRT2): confusing roles in the pathophysiology of neurological disorders. Front. Neurosci. 15, 614107 (2021).","journal-title":"Front. Neurosci."},{"key":"2573_CR39","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.ajhg.2008.10.010","volume":"83","author":"S Edvardson","year":"2008","unstructured":"Edvardson, S. et al. Mutations in the fatty acid 2-hydroxylase gene are associated with leukodystrophy with spastic paraparesis and dystonia. Am. J. Hum. Genet. 83, 643\u2013648 (2008).","journal-title":"Am. J. Hum. Genet."},{"key":"2573_CR40","doi-asserted-by":"publisher","first-page":"10130","DOI":"10.1073\/pnas.1821306116","volume":"116","author":"RR Voskuhl","year":"2019","unstructured":"Voskuhl, R. R. et al. Gene expression in oligodendrocytes during remyelination reveals cholesterol homeostasis as a therapeutic target in multiple sclerosis. Proc. Natl Acad. Sci. USA 116, 10130\u201310139 (2019).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2573_CR41","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/s12031-018-1112-4","volume":"65","author":"K Sharma","year":"2018","unstructured":"Sharma, K., Singh, J. & Pillai, P. P. MeCP2 differentially regulate the myelin MBP and PLP protein expression in oligodendrocytes and C6 glioma. J. Mol. Neurosci. 65, 343\u2013350 (2018).","journal-title":"J. Mol. Neurosci."},{"key":"2573_CR42","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1016\/j.cell.2022.04.003","volume":"185","author":"A Chen","year":"2022","unstructured":"Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777\u20131792 (2022).","journal-title":"Cell"},{"key":"2573_CR43","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1002\/1096-9861(20000828)424:3<409::AID-CNE3>3.0.CO;2-7","volume":"424","author":"L Puelles","year":"2000","unstructured":"Puelles, L. et al. Pallial and subpallial derivatives in the embryonic chick and mouse telencephalon, traced by the expression of the genes Dlx-2, Emx-1, Nkx-2.1, Pax-6, and Tbr-1. J. Comp. Neurol. 424, 409\u2013438 (2000).","journal-title":"J. Comp. Neurol."},{"key":"2573_CR44","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.adm7039","volume":"10","author":"N Khandelwal","year":"2024","unstructured":"Khandelwal, N. et al. FOXP1 regulates the development of excitatory synaptic inputs onto striatal neurons and induces phenotypic reversal with reinstatement. Sci. Adv. 10, eadm7039 (2024).","journal-title":"Sci. Adv."},{"key":"2573_CR45","doi-asserted-by":"publisher","first-page":"3797","DOI":"10.1093\/cercor\/bhx241","volume":"28","author":"R Pla","year":"2018","unstructured":"Pla, R. et al. Dlx1 and Dlx2 promote interneuron GABA synthesis, synaptogenesis, and dendritogenesis. Cereb. Cortex 28, 3797\u20133815 (2018).","journal-title":"Cereb. Cortex"},{"key":"2573_CR46","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1038\/s41586-025-09296-1","volume":"647","author":"CTJ Van Velthoven","year":"2024","unstructured":"Van Velthoven, C. T. J. et al. Transcriptomic and spatial organization of telencephalic GABAergic neurons. Nature 647, 143\u2013156 (2024).","journal-title":"Nature"},{"key":"2573_CR47","doi-asserted-by":"publisher","first-page":"8042","DOI":"10.1523\/JNEUROSCI.20-21-08042.2000","volume":"20","author":"A Stoykova","year":"2000","unstructured":"Stoykova, A., Treichel, D., Hallonet, M. & Gruss, P. Pax6 modulates the dorsoventral patterning of the mammalian telencephalon. J. Neurosci. 20, 8042\u20138050 (2000).","journal-title":"J. Neurosci."},{"key":"2573_CR48","unstructured":"10x Genomics. Visium HD CytAssist gene expression libraries of mouse brain (H&E). https:\/\/www.10xgenomics.com\/datasets\/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he (2024)."},{"key":"2573_CR49","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1038\/s41586-022-05094-1","volume":"609","author":"Y Deng","year":"2022","unstructured":"Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375\u2013383 (2022).","journal-title":"Nature"},{"key":"2573_CR50","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.14264","volume":"5","author":"JC Leggere","year":"2016","unstructured":"Leggere, J. C. et al. NOVA regulates Dcc alternative splicing during neuronal migration and axon guidance in the spinal cord. eLife 5, e14264 (2016).","journal-title":"eLife"},{"key":"2573_CR51","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1038\/s42256-022-00518-z","volume":"4","author":"M Yang","year":"2022","unstructured":"Yang, M. et al. Contrastive learning enables rapid mapping to multimodal single-cell atlas of multimillion scale. Nat. Mach. Intell. 4, 696\u2013709 (2022).","journal-title":"Nat. Mach. Intell."},{"key":"2573_CR52","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R.B. Momentum contrast for unsupervised visual representation learning. Proc. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 9729\u20139738 (IEEE, 2020).","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"2573_CR53","unstructured":"Radford, A. et al. Learning transferable visual models from natural language supervision. Proc.International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 8748\u20138763 (PMLR, 2021)."},{"key":"2573_CR54","doi-asserted-by":"publisher","first-page":"997","DOI":"10.26599\/BDMA.2025.9020009","volume":"8","author":"R Zheng","year":"2025","unstructured":"Zheng, R. et al. A flexible data-driven framework for correcting coarsely annotated scRNA-seq data. Big Data Min. Anal. 8, 997\u20131010 (2025).","journal-title":"Big Data Min. Anal."},{"key":"2573_CR55","doi-asserted-by":"publisher","unstructured":"Waikhom, L. & Patgiri, R. Graph neural networks: methods, applications, and opportunities. Preprint at arXiv https:\/\/doi.org\/10.48550\/arXiv.2108.10733 (2021).","DOI":"10.48550\/arXiv.2108.10733"},{"key":"2573_CR56","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1016\/j.tics.2020.09.004","volume":"24","author":"R Hadsell","year":"2020","unstructured":"Hadsell, R., Rao, D., Rusu, A. A. & Pascanu, R. Embracing change: continual learning in deep neural networks. Trends Cogn. Sci. 24, 1028\u20131040 (2020).","journal-title":"Trends Cogn. Sci."},{"key":"2573_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"KR Weiss","year":"2016","unstructured":"Weiss, K. R., Khoshgoftaar, T. M. & Wang, D. A survey of transfer learning. J. Big Data 3, 1\u201340 (2016).","journal-title":"J. Big Data"},{"key":"2573_CR58","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M. & Zhou, Z.-H. Isolation forest. Proc. 2008 Eighth IEEE International Conference on Data Mining 413\u2013422 (2008).","DOI":"10.1109\/ICDM.2008.17"},{"key":"2573_CR59","unstructured":"Velickovic, P. et al. Graph attention networks. International Conference on Learning Representations (2018)."},{"key":"2573_CR60","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29439-6","volume":"13","author":"K Dong","year":"2022","unstructured":"Dong, K. & Zhang, S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13, 1739 (2022).","journal-title":"Nat. Commun."},{"key":"2573_CR61","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dong, Y., Wang, K. & Sun, Y. Heterogeneous graph transformer. Proc. Web Conference 2020 (eds Huang, Y. et al.) 2704\u20132710 (Association for Computing Machinery, 2020).","DOI":"10.1145\/3366423.3380027"},{"key":"2573_CR62","doi-asserted-by":"crossref","unstructured":"He, X. et al. LightGCN: simplifying and powering graph convolution network for recommendation. Proc. 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (eds Huang, J. et al.) 639\u2013648 (Association for Computing Machinery, 2020).","DOI":"10.1145\/3397271.3401063"},{"key":"2573_CR63","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43105-5","volume":"14","author":"CR Xia","year":"2023","unstructured":"Xia, C. R., Cao, Z. J., Tu, X. M. & Gao, G. Spatial-linked alignment tool (SLAT) for aligning heterogenous slices. Nat. Commun. 14, 7236 (2023).","journal-title":"Nat. Commun."},{"key":"2573_CR64","unstructured":"Maas, A. L., Hannun, A. Y. & Ng, A. Y. Rectifier nonlinearities improve neural network acoustic models. Proc. 30th International Conference on Machine Learning (eds Dasgupta, S. & McAllester, D.) (JMLR, 2013)."},{"key":"2573_CR65","unstructured":"Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. Proc. 32nd International Conference on Machine Learning (eds Bach, F. & Blei, D.) 448\u2013456 (PMLR, 2015)."},{"key":"2573_CR66","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929\u20131958 (2014).","journal-title":"J. Mach. Learn. Res."},{"key":"2573_CR67","unstructured":"Fey, M. & Lenssen, J.E. Fast graph representation learning with PyTorch geometric. Proc. Workshop on Representation Learning on Graphs and Manifolds (ICLR, 2019)."},{"key":"2573_CR68","unstructured":"Kingma, D.P. & Ba, J. Adam: a method for stochastic optimization. Proc. International Conference on Learning Representations (ICLR, 2015)."},{"key":"2573_CR69","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"2573_CR70","first-page":"1","volume":"2020","author":"R Singh","year":"2020","unstructured":"Singh, R. et al. Unsupervised manifold alignment for single-cell multi-omics data. ACM BCB 2020, 1\u201310 (2020).","journal-title":"ACM BCB"},{"key":"2573_CR71","unstructured":"L\u00fccken, M. et al. A sandbox for prediction and integration of DNA, RNA, and proteins in single cells. Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2022)."},{"key":"2573_CR72","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1038\/s41587-022-01284-4","volume":"40","author":"ZJ Cao","year":"2022","unstructured":"Cao, Z. J. & Gao, G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat. Biotechnol. 40, 1458\u20131466 (2022).","journal-title":"Nat. Biotechnol."},{"key":"2573_CR73","unstructured":"Clevert, D.-A. Fast and accurate deep network learning by exponential linear units (ELUs). Proc. International Conference on Learning Representations (ICLR, 2016)."},{"key":"2573_CR74","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1038\/s41587-021-01001-7","volume":"40","author":"M Lotfollahi","year":"2022","unstructured":"Lotfollahi, M. et al. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40, 121\u2013130 (2022).","journal-title":"Nat. Biotechnol."},{"key":"2573_CR75","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s003579900058","volume":"16","author":"C Fraley","year":"1998","unstructured":"Fraley, C. & Rafter, A.E. MCLUST: software for model-based cluster and discriminant analysis. J. Classif. 16, 297\u2013306 (1998).","journal-title":"J. Classif."},{"key":"2573_CR76","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","volume":"2008","author":"VD Blondel","year":"2008","unstructured":"Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008).","journal-title":"J. Stat. Mech."},{"key":"2573_CR77","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-017-1382-0","volume":"19","author":"FA Wolf","year":"2018","unstructured":"Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).","journal-title":"Genome Biol."},{"key":"2573_CR78","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1089\/omi.2011.0118","volume":"16","author":"G Yu","year":"2012","unstructured":"Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284\u2013287 (2012).","journal-title":"OMICS"},{"key":"2573_CR79","doi-asserted-by":"publisher","unstructured":"Yan, X. et al. Datasets used in SpaMosaic\u2019s study. Zenodo https:\/\/doi.org\/10.5281\/zenodo.18946723 (2026).","DOI":"10.5281\/zenodo.18946723"},{"key":"2573_CR80","doi-asserted-by":"publisher","unstructured":"Yan, X. et al. Source code used in SpaMosaic\u2019s study. Zenodo https:\/\/doi.org\/10.5281\/zenodo.18402871 (2026).","DOI":"10.5281\/zenodo.18402871"}],"container-title":["Nature Genetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41588-026-02573-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41588-026-02573-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41588-026-02573-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:02:20Z","timestamp":1778778140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41588-026-02573-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":80,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["2573"],"URL":"https:\/\/doi.org\/10.1038\/s41588-026-02573-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5507983\/v1","asserted-by":"object"}]},"ISSN":["1061-4036","1546-1718"],"issn-type":[{"value":"1061-4036","type":"print"},{"value":"1546-1718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"22 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}