{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T16:18:16Z","timestamp":1793290696495,"version":"4.5.0"},"reference-count":61,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Gates Cambridge Scholarship"},{"DOI":"10.13039\/100015652","name":"Barts Charity","doi-asserted-by":"crossref","award":["MGU045"],"award-info":[{"award-number":["MGU045"]}],"id":[{"id":"10.13039\/100015652","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["WT206194"],"award-info":[{"award-number":["WT206194"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["WT211276\/Z\/18\/Z"],"award-info":[{"award-number":["WT211276\/Z\/18\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014989","name":"Chan Zuckerberg Initiative","doi-asserted-by":"crossref","award":["CZF2019-002445"],"award-info":[{"award-number":["CZF2019-002445"]}],"id":[{"id":"10.13039\/100014989","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Genome Biol"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Multimodal data is rapidly growing in many fields of science and engineering, including single-cell biology. We introduce MultiMAP, a novel algorithm for dimensionality reduction and integration. MultiMAP can integrate any number of datasets, leverages features not present in all datasets, is not restricted to a linear mapping, allows the user to specify the influence of each dataset, and is extremely scalable to large datasets. We apply MultiMAP to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data and show that it outperforms current approaches. On a new thymus dataset, we use MultiMAP to integrate cells along a temporal trajectory. This enables quantitative comparison of transcription factor expression and binding site accessibility over the course of T cell differentiation, revealing patterns of expression versus binding site opening kinetics.<\/jats:p>","DOI":"10.1186\/s13059-021-02565-y","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T04:02:51Z","timestamp":1639972971000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":74,"title":["MultiMAP: dimensionality reduction and integration of multimodal data"],"prefix":"10.1186","volume":"22","author":[{"given":"Mika Sarkin","family":"Jain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krzysztof","family":"Polanski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cecilia Dominguez","family":"Conde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongeun","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lira","family":"Mamanova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Knights","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel A.","family":"Botting","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily","family":"Stephenson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzlifah","family":"Haniffa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Austen","family":"Lamacraft","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8107-9974","authenticated-orcid":false,"given":"Mirjana","family":"Efremova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah A.","family":"Teichmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,20]]},"reference":[{"issue":"9","key":"2565_CR1","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1038\/nmeth.4380","volume":"14","author":"M Stoeckius","year":"2017","unstructured":"Stoeckius M, Hafemeister C. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14(9):865\u20138. https:\/\/doi.org\/10.1038\/nmeth.4380.","journal-title":"Nat Methods."},{"issue":"10","key":"2565_CR2","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1038\/nbt.3973","volume":"35","author":"VM Peterson","year":"2017","unstructured":"Peterson VM, Zhang KX. Multiplexed quantification of proteins and transcripts in single cells. Nat Biotechnol. 2017;35(10):936\u20139. https:\/\/doi.org\/10.1038\/nbt.3973.","journal-title":"Nat Biotechnol."},{"issue":"4","key":"2565_CR3","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1038\/s41576-018-0089-8","volume":"20","author":"SL Klemm","year":"2019","unstructured":"Klemm SL, Shipony Z, Greenleaf WJ. Chromatin accessibility and the regulatory epigenome. Nat Rev Genet. 2019;20(4):207\u201320. https:\/\/doi.org\/10.1038\/s41576-018-0089-8.","journal-title":"Nat Rev Genet."},{"issue":"9","key":"2565_CR4","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1016\/j.tibtech.2018.04.002","volume":"36","author":"ID Karemaker","year":"2018","unstructured":"Karemaker ID, Vermeulen M. Single-cell DNA methylation profiling: technologies and biological applications. Trends Biotechnol. 2018;36(9):952\u201365. https:\/\/doi.org\/10.1016\/j.tibtech.2018.04.002.","journal-title":"Trends Biotechnol."},{"key":"2565_CR5","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1242\/dev.176727","volume":"146","author":"U Mayr","year":"2019","unstructured":"Mayr U, Serra D, Liberali P. Exploring single cells in space and time during tissue development, homeostasis and regeneration. Development. 2019;146:12. https:\/\/doi.org\/10.1242\/dev.176727.","journal-title":"Development."},{"key":"2565_CR6","doi-asserted-by":"publisher","unstructured":"Regev A, Teichmann SA. The Human Cell Atlas. Elife. 2017;6. https:\/\/doi.org\/10.7554\/eLife.27041.","DOI":"10.7554\/eLife.27041"},{"issue":"7777","key":"2565_CR7","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1038\/s41586-019-1629-x","volume":"574","author":"HuBMAP Consortium","year":"2019","unstructured":"HuBMAP Consortium. The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature. 2019;574(7777):187\u201392. https:\/\/doi.org\/10.1038\/s41586-019-1629-x.","journal-title":"Nature."},{"issue":"1","key":"2565_CR8","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41592-019-0692-4","volume":"17","author":"M Efremova","year":"2020","unstructured":"Efremova M, Teichmann SA. Computational methods for single-cell omics across modalities. Nat Methods. 2020;17(1):14\u20137. https:\/\/doi.org\/10.1038\/s41592-019-0692-4.","journal-title":"Nat Methods."},{"issue":"1","key":"2565_CR9","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s13059-020-1926-6","volume":"21","author":"D L\u00e4hnemann","year":"2020","unstructured":"L\u00e4hnemann D, K\u00f6ster J. Eleven grand challenges in single-cell data science. Genome Biol. 2020;21(1):31. https:\/\/doi.org\/10.1186\/s13059-020-1926-6.","journal-title":"Genome Biol."},{"key":"2565_CR10","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. 2019;177:1888\u20131902.e21.","journal-title":"Cell."},{"key":"2565_CR11","doi-asserted-by":"publisher","first-page":"1873","DOI":"10.1016\/j.cell.2019.05.006","volume":"177","author":"JD Welch","year":"2019","unstructured":"Welch JD, et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell. 2019;177:1873\u20131887.e17.","journal-title":"Cell."},{"issue":"8","key":"2565_CR12","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1038\/s41587-021-00867-x","volume":"39","author":"C Gao","year":"2021","unstructured":"Gao C, Liu J. Iterative single-cell multi-omic integration using online learning. Nat Biotechnol. 2021;39(8):1000\u20137. https:\/\/doi.org\/10.1038\/s41587-021-00867-x.","journal-title":"Nat Biotechnol."},{"key":"2565_CR13","volume-title":"A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. arXiv [csLG]","author":"R Lopez","year":"2019","unstructured":"Lopez R, et al. A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements. arXiv [csLG]. 2019."},{"issue":"29","key":"2565_CR14","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes L, Healy J, Saul N, Gro\u00dfberger L. UMAP: Uniform Manifold Approximation and Projection. J Open Source Software. 2018;3(29):861. https:\/\/doi.org\/10.21105\/joss.00861.","journal-title":"J Open Source Software."},{"issue":"1","key":"2565_CR15","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1038\/nbt.4314","volume":"37","author":"E Becht","year":"2019","unstructured":"Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al. Dimensionality reduction for visualizing single-cell data using umap. Nat Biotechnol. 2019;37(1):38\u201344. https:\/\/doi.org\/10.1038\/nbt.4314.","journal-title":"Nat Biotechnol."},{"key":"2565_CR16","volume-title":"Metric realization of fuzzy simplicial sets. Preprint","author":"ID Spivak","year":"2009","unstructured":"Spivak ID. Metric realization of fuzzy simplicial sets. Preprint; 2009."},{"issue":"4","key":"2565_CR17","doi-asserted-by":"publisher","first-page":"501","DOI":"10.4153\/CMB-1986-079-9","volume":"29","author":"M Barr","year":"1986","unstructured":"Barr M. Fuzzy set theory and topos theory. Can Math Bull. 1986;29(4):501\u20138. https:\/\/doi.org\/10.4153\/CMB-1986-079-9.","journal-title":"Can Math Bull."},{"key":"2565_CR18","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1016\/S0167-8655(97)00028-7","volume":"18","author":"X-G Shang","year":"1997","unstructured":"Shang X-G, Jiang W-S. A note on fuzzy information measures. Pattern Recogn Lett. 1997;18:425\u201332. https:\/\/doi.org\/10.1016\/S0167-8655(97)00028-7.","journal-title":"Pattern Recogn Lett."},{"key":"2565_CR19","doi-asserted-by":"publisher","DOI":"10.5555\/2999792.2999959","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2 3111\u20133119","author":"T Mikolov","year":"2013","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. In:  Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2 3111\u20133119: (Curran Associates Inc.; 2013. https:\/\/doi.org\/10.5555\/2999792.2999959."},{"key":"2565_CR20","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883041","volume-title":"Proceedings of the 25th International Conference on World Wide Web 287\u2013297","author":"J Tang","year":"2016","unstructured":"Tang J, Liu J, Zhang M, Mei Q. Visualizing large-scale and high-dimensional data. In:  Proceedings of the 25th International Conference on World Wide Web 287\u2013297: International World Wide Web Conferences Steering Committee; 2016. https:\/\/doi.org\/10.1145\/2872427.2883041."},{"key":"2565_CR21","doi-asserted-by":"publisher","unstructured":"Gradient-based learning applied to document recognition. Intell Signal Process. 2009. https:\/\/doi.org\/10.1109\/9780470544976.ch9.","DOI":"10.1109\/9780470544976.ch9"},{"issue":"1","key":"2565_CR22","doi-asserted-by":"publisher","first-page":"5345","DOI":"10.1038\/s41467-018-07771-0","volume":"9","author":"X Chen","year":"2018","unstructured":"Chen X, Miragaia RJ, Natarajan KN, Teichmann SA. A rapid and robust method for single cell chromatin accessibility profiling. Nat Commun. 2018;9(1):5345. https:\/\/doi.org\/10.1038\/s41467-018-07771-0.","journal-title":"Nat Commun."},{"issue":"12","key":"2565_CR23","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1038\/s41587-019-0332-7","volume":"37","author":"JM Granja","year":"2019","unstructured":"Granja JM, Klemm S. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat Biotechnol. 2019;37(12):1458\u201365. https:\/\/doi.org\/10.1038\/s41587-019-0332-7.","journal-title":"Nat Biotechnol."},{"key":"2565_CR24","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1016\/j.cell.2018.07.028","volume":"174","author":"A Saunders","year":"2018","unstructured":"Saunders A, et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell. 2018;174:1015\u20131030.e16.","journal-title":"Cell."},{"key":"2565_CR25","doi-asserted-by":"publisher","unstructured":"Wang X, Allen WE. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018;361(6400). https:\/\/doi.org\/10.1126\/science.aat5691.","DOI":"10.1126\/science.aat5691"},{"key":"2565_CR26","doi-asserted-by":"publisher","unstructured":"Yao Z, et al. An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types. 2020.02.29.970558. 2020. https:\/\/doi.org\/10.1101\/2020.02.29.970558.","DOI":"10.1101\/2020.02.29.970558"},{"key":"2565_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/b138298","volume-title":"Brodmann\u2019s: Localisation in the Cerebral Cortex","author":"K Brodmann","year":"2010","unstructured":"Brodmann K. Brodmann\u2019s: Localisation in the Cerebral Cortex: Springer; 2010. https:\/\/doi.org\/10.1007\/b138298."},{"key":"2565_CR28","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.05422","volume":"3","author":"N Yamawaki","year":"2014","unstructured":"Yamawaki N, Borges K, Suter BA, Harris KD, Shepherd GMG. A genuine layer 4 in motor cortex with prototypical synaptic circuit connectivity. Elife. 2014;3:e05422. https:\/\/doi.org\/10.7554\/eLife.05422.","journal-title":"Elife."},{"issue":"8","key":"2565_CR29","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1038\/s41592-019-0466-z","volume":"16","author":"N Barkas","year":"2019","unstructured":"Barkas N, Petukhov V. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat Methods. 2019;16(8):695\u20138. https:\/\/doi.org\/10.1038\/s41592-019-0466-z.","journal-title":"Nat Methods."},{"key":"2565_CR30","doi-asserted-by":"publisher","unstructured":"Peng T, Chen GM, Tan K. GLUER: integrative analysis of single-cell omics and imaging data by deep neural network. https:\/\/doi.org\/10.1101\/2021.01.25.427845.","DOI":"10.1101\/2021.01.25.427845"},{"key":"2565_CR31","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1016\/j.cels.2019.05.003","volume":"8","author":"B Hie","year":"2019","unstructured":"Hie B, Cho H, DeMeo B, Bryson B, Berger B. Geometric sketching compactly summarizes the single-cell transcriptomic landscape. Cell Syst. 2019;8:483\u2013493.e7.","journal-title":"Cell Syst."},{"key":"2565_CR32","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.cels.2016.09.002","volume":"3","author":"MJ Muraro","year":"2016","unstructured":"Muraro MJ, et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 2016;3:385\u2013394.e3.","journal-title":"Cell Syst."},{"key":"2565_CR33","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.cmet.2016.08.020","volume":"24","author":"\u00c5 Segerstolpe","year":"2016","unstructured":"Segerstolpe \u00c5, et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 2016;24:593\u2013607.","journal-title":"Cell Metab."},{"key":"2565_CR34","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","volume":"3","author":"M Baron","year":"2016","unstructured":"Baron M, et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 2016;3:346\u2013360.e4.","journal-title":"Cell Syst."},{"key":"2565_CR35","doi-asserted-by":"publisher","unstructured":"Chazarra-Gil R, van Dongen S, Kiselev VY, Hemberg M. Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench. Nucleic Acids Res. 2021. https:\/\/doi.org\/10.1093\/nar\/gkab004.","DOI":"10.1093\/nar\/gkab004"},{"issue":"10","key":"2565_CR36","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1038\/s41590-020-0747-9","volume":"21","author":"J Roels","year":"2020","unstructured":"Roels J, Kuchmiy A. Distinct and temporary-restricted epigenetic mechanisms regulate human \u03b1\u03b2 and \u03b3\u03b4 T cell development. Nat Immunol. 2020;21(10):1280\u201392. https:\/\/doi.org\/10.1038\/s41590-020-0747-9.","journal-title":"Nat Immunol."},{"issue":"1","key":"2565_CR37","doi-asserted-by":"publisher","first-page":"4877","DOI":"10.1038\/s41467-018-07307-6","volume":"9","author":"G Jia","year":"2018","unstructured":"Jia G, Preussner J. Single cell RNA-seq and ATAC-seq analysis of cardiac progenitor cell transition states and lineage settlement. Nat Commun. 2018;9(1):4877. https:\/\/doi.org\/10.1038\/s41467-018-07307-6.","journal-title":"Nat Commun."},{"issue":"1","key":"2565_CR38","doi-asserted-by":"publisher","first-page":"1903","DOI":"10.1038\/s41467-019-09670-4","volume":"10","author":"H Chen","year":"2019","unstructured":"Chen H, Albergante L. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat Commun. 2019;10(1):1903. https:\/\/doi.org\/10.1038\/s41467-019-09670-4.","journal-title":"Nat Commun."},{"key":"2565_CR39","doi-asserted-by":"publisher","unstructured":"Park J-E, Botting RA. A cell atlas of human thymic development defines T cell repertoire formation. Science. 2020;367(6480). https:\/\/doi.org\/10.1126\/science.aay3224.","DOI":"10.1126\/science.aay3224"},{"key":"2565_CR40","doi-asserted-by":"publisher","unstructured":"Hosokawa H, Rothenberg EV. How transcription factors drive choice of the T cell fate. Nat Rev Immunol. 2020. https:\/\/doi.org\/10.1038\/s41577-020-00426-6.","DOI":"10.1038\/s41577-020-00426-6"},{"issue":"11","key":"2565_CR41","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278\u2013324. https:\/\/doi.org\/10.1109\/5.726791.","journal-title":"Proc IEEE."},{"issue":"1","key":"2565_CR42","first-page":"26","volume":"63","author":"WM Hern","year":"1984","unstructured":"Hern WM. Correlation of fetal age and measurements between 10 and 26 weeks of gestation. Obstet Gynecol. 1984;63(1):26\u201332.","journal-title":"Obstet Gynecol."},{"issue":"10","key":"2565_CR43","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1038\/nmeth.4437","volume":"14","author":"SC van den Brink","year":"2017","unstructured":"van den Brink SC, Sage F. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat Methods. 2017;14(10):935\u20136. https:\/\/doi.org\/10.1038\/nmeth.4437.","journal-title":"Nat Methods."},{"key":"2565_CR44","doi-asserted-by":"publisher","unstructured":"Fang R, et al. Fast and accurate clustering of single cell epigenomes reveals Cis-regulatory elements in rare cell types. https:\/\/doi.org\/10.1101\/615179.","DOI":"10.1101\/615179"},{"key":"2565_CR45","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1038\/nmeth.4401","volume":"14","author":"AN Schep","year":"2017","unstructured":"Schep AN, Wu B, Buenrostro JD, Greenleaf WJ. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat Methods. 2017;14:975\u20138.","journal-title":"Nat Methods."},{"key":"2565_CR46","doi-asserted-by":"publisher","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","volume":"2008","author":"VD Blondel","year":"2008","unstructured":"Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mechanics. 2008;2008:P10008.","journal-title":"J Stat Mechanics."},{"issue":"10","key":"2565_CR47","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1038\/nmeth.3971","volume":"13","author":"L Haghverdi","year":"2016","unstructured":"Haghverdi L, B\u00fcttner M, Wolf FA, Buettner F, Theis FJ. Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods. 2016;13(10):845\u20138. https:\/\/doi.org\/10.1038\/nmeth.3971.","journal-title":"Nat Methods."},{"issue":"1","key":"2565_CR48","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s13059-017-1382-0","volume":"19","author":"FA Wolf","year":"2018","unstructured":"Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15. https:\/\/doi.org\/10.1186\/s13059-017-1382-0.","journal-title":"Genome Biol."},{"issue":"1","key":"2565_CR49","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1038\/s41467-020-14766-3","volume":"11","author":"K Van den Berge","year":"2020","unstructured":"Van den Berge K, et al. Trajectory-based differential expression analysis for single-cell sequencing data. Nat Commun. 2020;11(1):1201. https:\/\/doi.org\/10.1038\/s41467-020-14766-3.","journal-title":"Nat Commun."},{"key":"2565_CR50","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.cels.2018.11.005","volume":"8","author":"SL Wolock","year":"2019","unstructured":"Wolock SL, Lopez R, Klein AM. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 2019;8:281\u2013291.e9.","journal-title":"Cell Syst"},{"issue":"2","key":"2565_CR51","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006731","volume":"15","author":"I Grytten","year":"2019","unstructured":"Grytten I, Rand KD. Graph Peak Caller: calling ChIP-seq peaks on graph-based reference genomes. PLoS Comput Biol. 2019;15(2):e1006731. https:\/\/doi.org\/10.1371\/journal.pcbi.1006731.","journal-title":"PLoS Comput Biol."},{"key":"2565_CR52","first-page":"912","volume-title":"Proceedings of the 20th International conference on Machine learning (ICML-03)","author":"X Zhu","year":"2003","unstructured":"Zhu X, Ghahramani Z, Lafferty JD. Semi-supervised learning using Gaussian fields and harmonic functions. In:  Proceedings of the 20th International conference on Machine learning (ICML-03); 2003. p. 912\u20139."},{"key":"2565_CR53","doi-asserted-by":"publisher","first-page":"858","DOI":"10.1016\/j.molcel.2018.06.044","volume":"71","author":"HA Pliner","year":"2018","unstructured":"Pliner HA, et al. Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data. Mol Cell. 2018;71:858\u2013871.e8.","journal-title":"Mol Cell."},{"key":"2565_CR54","doi-asserted-by":"publisher","unstructured":"Kozareva V, et al. A transcriptomic atlas of the mouse cerebellum reveals regional specializations and novel cell types. https:\/\/doi.org\/10.1101\/2020.03.04.976407.","DOI":"10.1101\/2020.03.04.976407"},{"key":"2565_CR55","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 1987;20:53\u201365. https:\/\/doi.org\/10.1016\/0377-0427(87)90125-7.","journal-title":"J Comput Appl Math."},{"key":"2565_CR56","volume-title":"scRNA-seq data of mouse splenocytes. ArrayExpress. E-MTAB-9769","author":"JM Sarkin","year":"2021","unstructured":"Sarkin JM, Krzysztof P, Cecilia DC, Xi C, Jongeun P, Lira M, et al. scRNA-seq data of mouse splenocytes. ArrayExpress. E-MTAB-9769; 2021."},{"key":"2565_CR57","volume-title":"scRNA-seq data of human fetal thymus. ArrayExpress. E-MTAB-9840","author":"JM Sarkin","year":"2021","unstructured":"Sarkin JM, Krzysztof P, Cecilia DC, Xi C, Jongeun P, Lira M, et al. scRNA-seq data of human fetal thymus. ArrayExpress. E-MTAB-9840; 2021."},{"key":"2565_CR58","volume-title":"scATAC-seq data of human fetal thyms. ArrayExpress. E-MTAB-9828","author":"JM Sarkin","year":"2021","unstructured":"Sarkin JM, Krzysztof P, Cecilia DC, Xi C, Jongeun P, Lira M, et al. scATAC-seq data of human fetal thyms. ArrayExpress. E-MTAB-9828; 2021."},{"key":"2565_CR59","volume-title":"Multiome RNA + ATAC data of human PBMCs. ArrayExpress. E-MTAB-11225 and E-MTAB-11226","author":"JM Sarkin","year":"2021","unstructured":"Sarkin JM, Krzysztof P, Cecilia DC, Xi C, Jongeun P, Lira M, et al. Multiome RNA + ATAC data of human PBMCs. ArrayExpress. E-MTAB-11225 and E-MTAB-11226; 2021."},{"key":"2565_CR60","volume-title":"Multiome RNA + ATAC data of human PBMCs. MultiMAP: dimensionality reduction and integration of multimodal data","author":"JM Sarkin","year":"2021","unstructured":"Sarkin JM, Krzysztof P, Cecilia DC, Xi C, Jongeun P, Lira M, et al. Multiome RNA + ATAC data of human PBMCs. MultiMAP: dimensionality reduction and integration of multimodal data: Github. https:\/\/github.com\/Teichlab\/MultiMAP; 2021."},{"key":"2565_CR61","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.5747678","volume-title":"Multiome RNA + ATAC data of human PBMCs. MultiMAP: dimensionality reduction and integration of multimodal data","author":"JM Sarkin","year":"2021","unstructured":"Sarkin JM, Krzysztof P, Cecilia DC, Xi C, Jongeun P, Lira M, et al. Multiome RNA + ATAC data of human PBMCs. MultiMAP: dimensionality reduction and integration of multimodal data: Zenodo; 2021. https:\/\/doi.org\/10.5281\/zenodo.5747678."}],"container-title":["Genome Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13059-021-02565-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13059-021-02565-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13059-021-02565-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T04:09:41Z","timestamp":1639973381000},"score":1,"resource":{"primary":{"URL":"https:\/\/genomebiology.biomedcentral.com\/articles\/10.1186\/s13059-021-02565-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":61,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["2565"],"URL":"https:\/\/doi.org\/10.1186\/s13059-021-02565-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.02.16.431421","asserted-by":"object"}]},"ISSN":["1474-760X"],"issn-type":[{"value":"1474-760X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12]]},"assertion":[{"value":"26 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The thymus tissue sample used for this study was obtained with written informed consent from the participant in accordance with the guidelines in The Declaration of Helsinki 2000. The human fetal tissue was obtained from the MRC\/Wellcome Trust-funded Human Developmental Biology Resource (HDBR,\n                      \n                      ) with appropriate maternal written consent and approval from the Newcastle and North Tyneside NHS Health Authority Joint Ethics Committee (08\/H0906\/21 + 5). HDBR is regulated by the UK Human Tissue Authority (HTA;\n                      \n                      ) and operates in accordance with the relevant HTA Codes of Practice.The mice for the splenocyte data were maintained under specific pathogen-free conditions at the Wellcome Trust Genome Campus Research Support Facility (Cambridge, UK). These animal facilities are approved by and registered with the UK Home Office. All procedures were in accordance with the Animals (Scientific Procedures) Act 1986. The protocols were approved by the Animal Welfare and Ethical Review Body of the Wellcome Trust Genome Campus.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"S.A.T. has received remunerations for consulting and Scientific Advisory Board work from Genentech, Biogen, Roche, and GlaxoSmithKline as well as Foresite Labs over the past 3 years.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"346"}}