{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T23:47:45Z","timestamp":1777420065258,"version":"3.51.4"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121E491"],"award-info":[{"award-number":["121E491"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121E491"],"award-info":[{"award-number":["121E491"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121E491"],"award-info":[{"award-number":["121E491"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121E491"],"award-info":[{"award-number":["121E491"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121E491"],"award-info":[{"award-number":["121E491"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121E491"],"award-info":[{"award-number":["121E491"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121E491"],"award-info":[{"award-number":["121E491"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-025-06087-3","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:05:23Z","timestamp":1743098723000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["SCITUNA: single-cell data integration tool using network alignment"],"prefix":"10.1186","volume":"26","author":[{"given":"Aissa","family":"Houdjedj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yacine","family":"Marouf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mekan","family":"Myradov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00fcleyman Onur","family":"Do\u011fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Burak Onur","family":"Erten","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oznur","family":"Tastan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cesim","family":"Erten","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hilal","family":"Kazan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"6087_CR1","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.mam.2017.07.003","volume":"59","author":"E Hedlund","year":"2018","unstructured":"Hedlund E, Deng Q. Single-cell rna sequencing: technical advancements and biological applications. Mol Asp Med. 2018;59:36\u201346. https:\/\/doi.org\/10.1016\/j.mam.2017.07.003.","journal-title":"Mol Asp Med"},{"issue":"2","key":"6087_CR2","doi-asserted-by":"publisher","first-page":"106","DOI":"10.14348\/molcells.2023.0009","volume":"46","author":"Y Ryu","year":"2023","unstructured":"Ryu Y, Han GH, Jung E, Hwang D. Integration of single-cell RNA-Seq datasets: a review of computational methods. Mol Cells. 2023;46(2):106\u201319. https:\/\/doi.org\/10.14348\/molcells.2023.0009.","journal-title":"Mol Cells"},{"issue":"1","key":"6087_CR3","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, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A, Pinello L, Skums P, Stamatakis A, Attolini CS-O, Aparicio S, Baaijens J, Balvert M, Barbanson Bd, Cappuccio A, Corleone G, Dutilh BE, Florescu M, Guryev V, Holmer R, Jahn K, Lobo TJ, Keizer EM, Khatri I, Kielbasa SM, Korbel JO, Kozlov AM, Kuo T-H, Lelieveldt BPF, Mandoiu II, Marioni JC, Marschall T, M\u00f6lder F, Niknejad A, Raczkowska A, Reinders M, Ridder Jd, Saliba A-E, Somarakis A, Stegle O, Theis FJ, Yang H, Zelikovsky A, McHardy AC, Raphael BJ, Shah SP, Sch\u00f6nhuth A. 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"},{"issue":"1","key":"6087_CR4","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13059-019-1850-9","volume":"21","author":"HTN Tran","year":"2020","unstructured":"Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, Chen J. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020;21(1):12. https:\/\/doi.org\/10.1186\/s13059-019-1850-9.","journal-title":"Genome Biol"},{"issue":"9","key":"6087_CR5","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1038\/s41587-020-00748-9","volume":"39","author":"W Chen","year":"2021","unstructured":"Chen W, Zhao Y, Chen X, Yang Z, Xu X, Bi Y, Chen V, Li J, Choi H, Ernest B, Tran B, Mehta M, Kumar P, Farmer A, Mir A, Mehra UA, Li J-L, Moos M, Xiao W, Wang C. A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples. Nat Biotechnol. 2021;39(9):1103\u201314. https:\/\/doi.org\/10.1038\/s41587-020-00748-9.","journal-title":"Nat Biotechnol"},{"issue":"6","key":"6087_CR6","doi-asserted-by":"publisher","first-page":"8746","DOI":"10.15252\/msb.20188746","volume":"15","author":"MD Luecken","year":"2019","unstructured":"Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):8746. https:\/\/doi.org\/10.15252\/msb.20188746.","journal-title":"Mol Syst Biol"},{"issue":"1","key":"6087_CR7","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1093\/bib\/bbaa042","volume":"22","author":"M Forcato","year":"2021","unstructured":"Forcato M, Romano O, Bicciato S. Computational methods for the integrative analysis of single-cell data. Brief Bioinf. 2021;22(1):20\u20139. https:\/\/doi.org\/10.1093\/bib\/bbaa042.","journal-title":"Brief Bioinf"},{"issue":"5","key":"6087_CR8","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/nbt.4091","volume":"36","author":"L Haghverdi","year":"2018","unstructured":"Haghverdi L, Lun ATL, Morgan MD, Marioni JC. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat Biotechnol. 2018;36(5):421\u20137. https:\/\/doi.org\/10.1038\/nbt.4091.","journal-title":"Nat Biotechnol"},{"issue":"5","key":"6087_CR9","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","volume":"36","author":"A Butler","year":"2018","unstructured":"Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018;36(5):411\u201320. https:\/\/doi.org\/10.1038\/nbt.4096.","journal-title":"Nat Biotechnol"},{"issue":"8","key":"6087_CR10","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, Nikolaeva D, Lozinsky Y, Demharter S, Khodosevich K, Kharchenko PV. 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"},{"issue":"6","key":"6087_CR11","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1038\/s41587-019-0113-3","volume":"37","author":"B Hie","year":"2019","unstructured":"Hie B, Bryson B, Berger B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol. 2019;37(6):685\u201391. https:\/\/doi.org\/10.1038\/s41587-019-0113-3.","journal-title":"Nat Biotechnol"},{"issue":"12","key":"6087_CR12","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","volume":"16","author":"I Korsunsky","year":"2019","unstructured":"Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh P-R, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods. 2019;16(12):1289\u201396. https:\/\/doi.org\/10.1038\/s41592-019-0619-0.","journal-title":"Nat Methods"},{"issue":"3","key":"6087_CR13","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1093\/bioinformatics\/btz625","volume":"36","author":"K Polanski","year":"2019","unstructured":"Polanski K, Young MD, Miao Z, Meyer KB, Teichmann SA, Park J-E. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics. 2019;36(3):964\u20135. https:\/\/doi.org\/10.1093\/bioinformatics\/btz625.","journal-title":"Bioinformatics"},{"issue":"11","key":"6087_CR14","doi-asserted-by":"publisher","first-page":"3632","DOI":"10.1038\/s41596-020-0391-8","volume":"15","author":"J Liu","year":"2020","unstructured":"Liu J, Gao C, Sodicoff J, Kozareva V, Macosko EZ, Welch JD. Jointly defining cell types from multiple single-cell datasets using LIGER. Nat Protoc. 2020;15(11):3632\u201362. https:\/\/doi.org\/10.1038\/s41596-020-0391-8.","journal-title":"Nat Protoc"},{"issue":"8","key":"6087_CR15","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1038\/s41592-019-0494-8","volume":"16","author":"M Lotfollahi","year":"2019","unstructured":"Lotfollahi M, Wolf FA, Theis FJ. scGen predicts single-cell perturbation responses. Nat Methods. 2019;16(8):715\u201321. https:\/\/doi.org\/10.1038\/s41592-019-0494-8.","journal-title":"Nat Methods"},{"issue":"11","key":"6087_CR16","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1038\/s41592-019-0576-7","volume":"16","author":"M Amodio","year":"2019","unstructured":"Amodio M, van Dijk D, Srinivasan K, Chen WS, Mohsen H, Moon KR, Campbell A, Zhao Y, Wang X, Venkataswamy M, Desai A, Ravi V, Kumar P, Montgomery R, Wolf G, Krishnaswamy S. Exploring single-cell data with deep multitasking neural networks. Nat Methods. 2019;16(11):1139\u201345. https:\/\/doi.org\/10.1038\/s41592-019-0576-7.","journal-title":"Nat Methods"},{"issue":"12","key":"6087_CR17","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","volume":"15","author":"R Lopez","year":"2018","unstructured":"Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018;15(12):1053\u20138. https:\/\/doi.org\/10.1038\/s41592-018-0229-2.","journal-title":"Nat Methods"},{"issue":"1","key":"6087_CR18","doi-asserted-by":"publisher","first-page":"9620","DOI":"10.15252\/msb.20209620","volume":"17","author":"C Xu","year":"2021","unstructured":"Xu C, Lopez R, Mehlman E, Regier J, Jordan MI, Yosef N. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol Syst Biol. 2021;17(1):9620. https:\/\/doi.org\/10.15252\/msb.20209620.","journal-title":"Mol Syst Biol"},{"key":"6087_CR19","doi-asserted-by":"publisher","unstructured":"Gayoso A, Lopez R, Xing G, Boyeau P, Valiollah Pour\u00a0Amiri V, Hong J, Wu K, Jayasuriya M, Mehlman E, Langevin M, Liu Y, Samaran J, Misrachi G, Nazaret A, Clivio O, Xu C, Ashuach T, Gabitto M, Lotfollahi M, Svensson V, da Veiga Beltrame E, Kleshchevnikov V, Talavera-L\u00f3pez C, Pachter L, Theis FJ, Streets A, Jordan MI, Regier J, Yosef N. A Python library for probabilistic analysis of single-cell omics data. Nat Biotechnol. 2022;40(2):163\u20136. https:\/\/doi.org\/10.1038\/s41587-021-01206-w","DOI":"10.1038\/s41587-021-01206-w"},{"issue":"5","key":"6087_CR20","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1038\/s41587-023-01733-8","volume":"41","author":"I Virshup","year":"2023","unstructured":"Virshup I, Bredikhin D, Heumos L, Palla G, Sturm G, Gayoso A, Kats I, Koutrouli M, Berger B, Pe\u2019er D, Regev A, Teichmann SA, Finotello F, Wolf FA, Yosef N, Stegle O, Theis FJ. The scverse project provides a computational ecosystem for single-cell omics data analysis. Nat Biotechnol. 2023;41(5):604\u20136. https:\/\/doi.org\/10.1038\/s41587-023-01733-8.","journal-title":"Nat Biotechnol"},{"issue":"1","key":"6087_CR21","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/s41592-021-01336-8","volume":"19","author":"MD Luecken","year":"2022","unstructured":"Luecken MD, B\u00fcttner M, Chaichoompu K, Danese A, Interlandi M, Mueller MF, Strobl DC, Zappia L, Dugas M, Colom\u00e9-Tatch\u00e9 M, Theis FJ. Benchmarking atlas-level data integration in single-cell genomics. Nat Methods. 2022;19(1):41\u201350. https:\/\/doi.org\/10.1038\/s41592-021-01336-8.","journal-title":"Nat Methods"},{"key":"6087_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-023-02097-9","author":"H Maan","year":"2024","unstructured":"Maan H, Zhang L, Yu C, Geuenich MJ, Campbell KR, Wang B. Characterizing the impacts of dataset imbalance on single-cell data integration. Nat Biotechnol. 2024. https:\/\/doi.org\/10.1038\/s41587-023-02097-9.","journal-title":"Nat Biotechnol"},{"issue":"1","key":"6087_CR23","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1186\/s13059-017-1305-0","volume":"18","author":"L Zappia","year":"2017","unstructured":"Zappia L, Phipson B, Oshlack A. Splatter: simulation of single-cell RNA sequencing data. Genome Biol. 2017;18(1):174. https:\/\/doi.org\/10.1186\/s13059-017-1305-0.","journal-title":"Genome Biol"},{"issue":"7","key":"6087_CR24","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1038\/s41591-019-0468-5","volume":"25","author":"FA Vieira Braga","year":"2019","unstructured":"...Vieira Braga FA, Kar G, Berg M, Carpaij OA, Polanski K, Simon LM, Brouwer S, Gomes T, Hesse L, Jiang J, Fasouli ES, Efremova M, Vento-Tormo R, Talavera-L\u00f3pez C, Jonker MR, Affleck K, Palit S, Strzelecka PM, Firth HV, Mahbubani KT, Cvejic A, Meyer KB, Saeb-Parsy K, Luinge M, Brandsma C-A, Timens W, Angelidis I, Strunz M, Koppelman GH, van Oosterhout AJ, Schiller HB, Theis FJ, van den Berge M, Nawijn MC, Teichmann SA. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat Med. 2019;25(7):1153\u201363. https:\/\/doi.org\/10.1038\/s41591-019-0468-5.","journal-title":"Nat Med"},{"issue":"2","key":"6087_CR25","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.stem.2016.05.010","volume":"19","author":"D Gr\u00fcn","year":"2016","unstructured":"Gr\u00fcn D, Muraro MJ, Boisset J-C, Wiebrands K, Lyubimova A, Dharmadhikari G, van den Born M, van Es J, Jansen E, Clevers H, de Koning EJP, van Oudenaarden A. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell. 2016;19(2):266\u201377. https:\/\/doi.org\/10.1016\/j.stem.2016.05.010.","journal-title":"Cell Stem Cell"},{"issue":"4","key":"6087_CR26","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, Dharmadhikari G, Gr\u00fcn D, Groen N, Dielen T, Jansen E, van Gurp L, Engelse MA, Carlotti F, de Koning EJP, van Oudenaarden A. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 2016;3(4):385\u20133943. https:\/\/doi.org\/10.1016\/j.cels.2016.09.002.","journal-title":"Cell Syst"},{"issue":"2","key":"6087_CR27","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1101\/gr.212720.116","volume":"27","author":"N Lawlor","year":"2017","unstructured":"Lawlor N, George J, Bolisetty M, Kursawe R, Sun L, Sivakamasundari V, Kycia I, Robson P, Stitzel ML. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res. 2017;27(2):208\u201322. https:\/\/doi.org\/10.1101\/gr.212720.116.","journal-title":"Genome Res"},{"issue":"4","key":"6087_CR28","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, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM, Melton DA, Yanai I. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 2016;3(4):346\u20133604. https:\/\/doi.org\/10.1016\/j.cels.2016.08.011.","journal-title":"Cell Syst"},{"issue":"4","key":"6087_CR29","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.cmet.2016.08.018","volume":"24","author":"Y Xin","year":"2016","unstructured":"Xin Y, Kim J, Okamoto H, Ni M, Wei Y, Adler C, Murphy AJ, Yancopoulos GD, Lin C, Gromada J. RNA sequencing of single human islet cells reveals type 2 diabetes genes. Cell Metab. 2016;24(4):608\u201315. https:\/\/doi.org\/10.1016\/j.cmet.2016.08.018.","journal-title":"Cell Metab"},{"issue":"4","key":"6087_CR30","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.cmet.2016.08.020","volume":"24","author":"A Segerstolpe","year":"2016","unstructured":"Segerstolpe A, Palasantza A, Eliasson P, Andersson E-M, Andr\u00e9asson A-C, Sun X, Picelli S, Sabirsh A, Clausen M, Bjursell MK, Smith DM, Kasper M, Ammala C, Sandberg R. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 2016;24(4):593\u2013607. https:\/\/doi.org\/10.1016\/j.cmet.2016.08.020.","journal-title":"Cell Metab"},{"issue":"7767","key":"6087_CR31","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41586-019-1158-7","volume":"572","author":"MC Vladoiu","year":"2019","unstructured":"Vladoiu MC, El-Hamamy I, Donovan LK, Farooq H, Holgado BL, Sundaravadanam Y, Ramaswamy V, Hendrikse LD, Kumar S, Mack SC, Lee JJY, Fong V, Juraschka K, Przelicki D, Michealraj A, Skowron P, Luu B, Suzuki H, Morrissy AS, Cavalli FMG, Garzia L, Daniels C, Wu X, Qazi MA, Singh SK, Chan JA, Marra MA, Malkin D, Dirks P, Heisler L, Pugh T, Ng K, Notta F, Thompson EM, Kleinman CL, Joyner AL, Jabado N, Stein L, Taylor MD. Childhood cerebellar tumours mirror conserved fetal transcriptional programs. Nature. 2019;572(7767):67\u201373. https:\/\/doi.org\/10.1038\/s41586-019-1158-7.","journal-title":"Nature"},{"issue":"5","key":"6087_CR32","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1016\/j.cell.2018.06.052","volume":"174","author":"DA Cusanovich","year":"2018","unstructured":"Cusanovich DA, Hill AJ, Aghamirzaie D, Daza RM, Pliner HA, Berletch JB, Filippova GN, Huang X, Christiansen L, DeWitt WS, Lee C, Regalado SG, Read DF, Steemers FJ, Disteche CM, Trapnell C, Shendure J. A single-cell atlas of in vivo mammalian chromatin accessibility. Cell. 2018;174(5):1309\u2013132418. https:\/\/doi.org\/10.1016\/j.cell.2018.06.052.","journal-title":"Cell"},{"key":"6087_CR33","unstructured":"10x Genomics: Fresh cortex from adult mouse brain (P50) 2019. https:\/\/support.10xgenomics.com\/single-cell-atac\/datasets\/1.2.0\/atac_v1_adult_brain_fresh_5k? Accessed 2024-10-22"},{"issue":"1","key":"6087_CR34","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1038\/s41467-021-21583-9","volume":"12","author":"R Fang","year":"2021","unstructured":"Fang R, Preissl S, Li Y, Hou X, Lucero J, Wang X, Motamedi A, Shiau AK, Zhou X, Xie F, Mukamel EA, Zhang K, Zhang Y, Behrens MM, Ecker JR, Ren B. Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat Commun. 2021;12(1):1337. https:\/\/doi.org\/10.1038\/s41467-021-21583-9.","journal-title":"Nat Commun"},{"issue":"1","key":"6087_CR35","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":"6087_CR36","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1038\/s41467-023-36635-5","volume":"14","author":"X Yu","year":"2023","unstructured":"Yu X, Xu X, Zhang J, Li X. Batch alignment of single-cell transcriptomics data using deep metric learning. Nat Commun. 2023;14(1):960. https:\/\/doi.org\/10.1038\/s41467-023-36635-5.","journal-title":"Nat Commun"},{"key":"6087_CR37","unstructured":"Single-cell RNA-seq analysis workshop. Teaching materials at the Harvard Chan Bioinformatics Core 2023. https:\/\/github.com\/hbctraining\/scRNA-seq Accessed 2023-10-14"},{"key":"6087_CR38","doi-asserted-by":"publisher","unstructured":"Cristian P-M, Aar\u00f3n V-J, Armando E-HD, Estrella MLY, Daniel N-R, Paul S-CJ, David G-V, Osbaldo R-A. Diffusion on PCA-UMAP manifold captures a well-balance of local, global, and continuum structure to denoise single-cell RNA sequencing data. bioRxiv 2022. https:\/\/doi.org\/10.1101\/2022.06.09.495525","DOI":"10.1101\/2022.06.09.495525"},{"issue":"7","key":"6087_CR39","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, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888\u2013190221. https:\/\/doi.org\/10.1016\/j.cell.2019.05.031.","journal-title":"Cell"},{"key":"6087_CR40","doi-asserted-by":"publisher","unstructured":"Rubner Y, Tomasi C, Guibas LJ. A metric for distributions with applications to image databases. In: Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), 1998;59\u201366. https:\/\/doi.org\/10.1109\/ICCV.1998.710701","DOI":"10.1109\/ICCV.1998.710701"},{"issue":"78","key":"6087_CR41","first-page":"1","volume":"22","author":"R Flamary","year":"2021","unstructured":"...Flamary R, Courty N, Gramfort A, Alaya MZ, Boisbunon A, Chambon S, Chapel L, Corenflos A, Fatras K, Fournier N, Gautheron L, Gayraud NTH, Janati H, Rakotomamonjy A, Redko I, Rolet A, Schutz A, Seguy V, Sutherland DJ, Tavenard R, Tong A, Vayer T. POT: python optimal transport. J Mach Learn Res. 2021;22(78):1\u20138.","journal-title":"J Mach Learn Res"},{"issue":"12","key":"6087_CR42","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","volume":"16","author":"I Korsunsky","year":"2019","unstructured":"Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh P-R, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods. 2019;16(12):1289\u201396. https:\/\/doi.org\/10.1038\/s41592-019-0619-0.","journal-title":"Nat Methods"},{"issue":"1","key":"6087_CR43","doi-asserted-by":"publisher","first-page":"6118","DOI":"10.1038\/s41467-022-33758-z","volume":"13","author":"L Xiong","year":"2022","unstructured":"Xiong L, Tian K, Li Y, Ning W, Gao X, Zhang QC. Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space. Nat Commun. 2022;13(1):6118. https:\/\/doi.org\/10.1038\/s41467-022-33758-z.","journal-title":"Nat Commun"},{"issue":"1","key":"6087_CR44","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/s41592-018-0254-1","volume":"16","author":"M B\u00fcttner","year":"2019","unstructured":"B\u00fcttner M, Miao Z, Wolf FA, Teichmann SA, Theis FJ. A test metric for assessing single-cell RNA-seq batch correction. Nat Methods. 2019;16(1):43\u20139. https:\/\/doi.org\/10.1038\/s41592-018-0254-1.","journal-title":"Nat Methods"},{"issue":"6","key":"6087_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2020.101185","volume":"23","author":"I Mandric","year":"2020","unstructured":"Mandric I, Hill BL, Freund MK, Thompson M, Halperin E. BATMAN: fast and accurate integration of single-cell RNA-seq datasets via minimum-weight matching. iScience. 2020;23(6): 101185. https:\/\/doi.org\/10.1016\/j.isci.2020.101185.","journal-title":"iScience"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06087-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06087-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06087-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:05:39Z","timestamp":1743098739000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06087-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,27]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6087"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06087-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,27]]},"assertion":[{"value":"11 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}],"article-number":"92"}}