{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:26:55Z","timestamp":1783574815794,"version":"3.55.0"},"reference-count":72,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":50,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001804","name":"Canada Research Chair","doi-asserted-by":"crossref","award":["CRC-2021-00232"],"award-info":[{"award-number":["CRC-2021-00232"]}],"id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Michael Smith Health Research BC Scholar","award":["SCH-2022-2553"],"award-info":[{"award-number":["SCH-2022-2553"]}]},{"name":"National Research Council Canada\u2019s","award":["DHGA-121"],"award-info":[{"award-number":["DHGA-121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Existing cell type annotation methods face significant hurdles: supervised approaches often fail to differentiate between novel cell types not present in reference data, while unsupervised techniques can suffer from cluster impurity and difficulties in robustly distinguishing multiple distinct unknown cell populations. This critical gap motivated the development of HiCat, a semi-supervised pipeline specifically designed to overcome these limitations. HiCat is a semi-supervised pipeline that integrates both approaches, leveraging reference (labeled) and query (unlabeled) genomic data to simultaneously enhance annotation accuracy for known cell types and improve the discovery and differentiation of novel ones. HiCat follows a structured pipeline: (1) removing batch effects and generate a low-dimensional embedding; (2) nonlinear dimensionality reduction for capturing key patterns; (3) unsupervised clustering for proposing novel cell type candidates; (4) merging multi-resolution features from previous steps into a condensed feature space; (5) training a classifier on reference data for supervised annotation; and (6) resolving inconsistencies between supervised predictions and unsupervised clusters to finalize annotations, particularly for unseen types. Performance was evaluated across 10 public genomic datasets and perform a case study on a molecular cell atlas of the human lung. HiCat demonstrated superior performance in both known cell type classification and novel cell type identification. In benchmark evaluations, HiCat consistently outperformed existing methods, critically excelling in identifying and distinguishing multiple novel cell types. HiCat presents a robust framework for scRNA-seq cell annotation, improving classification accuracy and novel type identification. In addition, it provides a scalable and transferable solution for biomedical research, directly addressing key challenges in automated cell annotation.<\/jats:p>","DOI":"10.1093\/bib\/bbaf428","type":"journal-article","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:42:53Z","timestamp":1755693773000},"source":"Crossref","is-referenced-by-count":10,"title":["HiCat: a semi-supervised approach for cell type annotation"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2660-2018","authenticated-orcid":false,"given":"Chang","family":"Bi","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, University of Victoria , 3800 Finnerty Road, Victoria, BC V8P 5C2 ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4996-8875","authenticated-orcid":false,"given":"Kailun","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of Victoria , 3800 Finnerty Road, Victoria, BC V8P 5C2 ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4728-2343","authenticated-orcid":false,"given":"Xuekui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, University of Victoria , 3800 Finnerty Road, Victoria, BC V8P 5C2 ,","place":["Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"2025082008424958800_ref1","doi-asserted-by":"publisher","first-page":"e694","DOI":"10.1002\/ctm2.694","article-title":"Single-cell RNA sequencing technologies and applications: a brief overview","volume":"12","author":"Jovic","year":"2022","journal-title":"Clin Transl Med"},{"key":"2025082008424958800_ref2","doi-asserted-by":"publisher","first-page":"885267","DOI":"10.3389\/fimmu.2022.885267","article-title":"Mapping and validation of scRNA-seq-derived cell-cell communication networks in the tumor microenvironment","volume":"13","author":"Bridges","year":"2022","journal-title":"Front Immunol"},{"key":"2025082008424958800_ref3","doi-asserted-by":"publisher","first-page":"A087","DOI":"10.1158\/1538-7445.OVARIAN23-A087","article-title":"Abstract A087: an ovarian cancer scRNA-seq atlas to dissect tumor-host interactions underlying metastatization and chemoresistance","volume":"84","author":"Sallese","year":"2024","journal-title":"Cancer Res"},{"key":"2025082008424958800_ref4","doi-asserted-by":"publisher","first-page":"1189520","DOI":"10.3389\/fimmu.2023.1189520","article-title":"Mast cell marker gene signature: prognosis and immunotherapy response prediction in lung adenocarcinoma through integrated scRNA-seq and bulk RNA-seq","volume":"14","author":"Zhang","year":"2023","journal-title":"Front Immunol"},{"key":"2025082008424958800_ref5","doi-asserted-by":"publisher","first-page":"961","DOI":"10.1016\/j.csbj.2021.01.015","article-title":"Automated methods for cell type annotation on scRNA-seq data","volume":"19","author":"Pasquini","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"key":"2025082008424958800_ref6","volume-title":"Programs and Abstracts of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"},{"key":"2025082008424958800_ref7","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","article-title":"Comprehensive integration of single-cell data","volume":"177","author":"Stuart","year":"2019","journal-title":"Cell"},{"key":"2025082008424958800_ref8","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1038\/s41592-019-0535-3","article-title":"Supervised classification enables rapid annotation of cell atlases","volume":"16","author":"Pliner","year":"2019","journal-title":"Nat Methods"},{"key":"2025082008424958800_ref9","doi-asserted-by":"publisher","first-page":"e0205499","DOI":"10.1371\/journal.pone.0205499","article-title":"CaSTLe\u2014classification of single cells by transfer learning: harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments","volume":"13","author":"Lieberman","year":"2018","journal-title":"PloS One"},{"key":"2025082008424958800_ref10","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"2025082008424958800_ref11","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.cels.2019.06.004","article-title":"SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species","volume":"9","author":"Tan","year":"2019","journal-title":"Cell Syst"},{"key":"2025082008424958800_ref12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2025082008424958800_ref13","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1186\/s13059-019-1862-5","article-title":"scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data","volume":"20","author":"Alquicira-Hernandez","year":"2019","journal-title":"Genome Biol"},{"key":"2025082008424958800_ref14","doi-asserted-by":"publisher","volume-title":"bioRxiv","DOI":"10.1101\/456129"},{"key":"2025082008424958800_ref15"},{"key":"2025082008424958800_ref16","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1038\/s41590-018-0276-y","article-title":"Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage","volume":"20","author":"Aran","year":"2019","journal-title":"Nat Immunol"},{"key":"2025082008424958800_ref17","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1038\/nmeth.4644","article-title":"scmap: projection of single-cell RNA-seq data across data sets","volume":"15","author":"Kiselev","year":"2018","journal-title":"Nat Methods"},{"key":"2025082008424958800_ref18","doi-asserted-by":"publisher","first-page":"e95","DOI":"10.1093\/nar\/gkz543","article-title":"CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing","volume":"47","author":"de Kanter","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025082008424958800_ref19","doi-asserted-by":"publisher","first-page":"4688","DOI":"10.1093\/bioinformatics\/btz292","article-title":"scMatch: a single-cell gene expression profile annotation tool using reference datasets","volume":"35","author":"Hou","year":"2019","journal-title":"Bioinformatics"},{"key":"2025082008424958800_ref20","doi-asserted-by":"crossref","first-page":"223","DOI":"10.12688\/f1000research.22969.2","article-title":"clustifyr: an R package for automated single-cell RNA sequencing cluster classification","volume":"9","author":"Rui","year":"2020","journal-title":"F1000Research"},{"key":"2025082008424958800_ref21","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1186\/s12859-020-3538-2","article-title":"CIPR: a web-based R\/shiny app and R package to annotate cell clusters in single cell RNA sequencing experiments","volume":"21","author":"Atakan Ekiz","year":"2020","journal-title":"BMC Bioinformatics"},{"key":"2025082008424958800_ref22","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans Inf Theory"},{"key":"2025082008424958800_ref23","doi-asserted-by":"publisher","first-page":"5556","DOI":"10.1038\/s41467-021-25725-x","article-title":"Leveraging the cell ontology to classify unseen cell types","volume":"12","author":"Wang","year":"2021","journal-title":"Nat Commun"},{"key":"2025082008424958800_ref24","doi-asserted-by":"publisher","first-page":"e9620","DOI":"10.15252\/msb.20209620","article-title":"Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models","volume":"17","author":"Chenling","year":"2021","journal-title":"Mol Syst Biol"},{"key":"2025082008424958800_ref25","doi-asserted-by":"publisher","first-page":"e9389","DOI":"10.15252\/msb.20199389","article-title":"scClassify: sample size estimation and multiscale classification of cells using single and multiple reference","volume":"16","author":"Lin","year":"2020","journal-title":"Mol Syst Biol"},{"key":"2025082008424958800_ref26","doi-asserted-by":"publisher","first-page":"100914","DOI":"10.1016\/j.isci.2020.100914","article-title":"scID uses discriminant analysis to identify transcriptionally equivalent cell types across single-cell RNA-Seq data with batch effect","volume":"23","author":"Boufea","year":"2020","journal-title":"iScience"},{"key":"2025082008424958800_ref27","doi-asserted-by":"publisher","first-page":"vbad030","DOI":"10.1093\/bioadv\/vbad030","article-title":"scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data","volume":"3","author":"Ji","year":"2023","journal-title":"Bioinform Adv"},{"key":"2025082008424958800_ref28","doi-asserted-by":"publisher","first-page":"e43","DOI":"10.1093\/nar\/gkab1275","article-title":"scMAGIC: accurately annotating single cells using two rounds of reference-based classification","volume":"50","author":"Zhang","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2025082008424958800_ref29","doi-asserted-by":"publisher","first-page":"e48","DOI":"10.1093\/nar\/gkz116","article-title":"SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles","volume":"47","author":"Xie","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2025082008424958800_ref30","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1093\/bioinformatics\/btz592","article-title":"ACTINN: automated identification of cell types in single cell RNA sequencing","volume":"36","author":"Ma","year":"2020","journal-title":"Bioinformatics"},{"key":"2025082008424958800_ref31","doi-asserted-by":"publisher","first-page":"e122","DOI":"10.1093\/nar\/gkab775","article-title":"scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network","volume":"49","author":"Shao","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2025082008424958800_ref32","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1038\/s42256-021-00432-w","article-title":"Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding","volume":"4","author":"Chen","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2025082008424958800_ref33","doi-asserted-by":"publisher","first-page":"i51","DOI":"10.1093\/bioinformatics\/btab286","article-title":"CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data","volume":"37","author":"Wei","year":"2021","journal-title":"Bioinformatics"},{"key":"2025082008424958800_ref34","doi-asserted-by":"publisher","first-page":"1781","DOI":"10.1101\/gr.268581.120","article-title":"scNym: semi-supervised adversarial neural networks for single cell classification","volume":"31","author":"Kimmel","year":"2021","journal-title":"Genome Res"},{"key":"2025082008424958800_ref35","volume-title":"Journal of Machine Learning Research"},{"key":"2025082008424958800_ref36","doi-asserted-by":"crossref","first-page":"5042","DOI":"10.1093\/bioinformatics\/btac652","article-title":"scSemiGAN: a single-cell semi-supervised annotation and dimensionality reduction framework based on generative adversarial network","volume":"38","author":"Zhongyuan","year":"2022","journal-title":"Bioinformatics"},{"key":"2025082008424958800_ref37","volume-title":"Advances in Neural Information Processing Systems"},{"key":"2025082008424958800_ref38","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1038\/s42256-022-00534-z","article-title":"scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data","volume":"4","author":"Yang","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2025082008424958800_ref39","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","article-title":"Fast, sensitive and accurate integration of single-cell data with Harmony","volume":"16","author":"Korsunsky","year":"2019","journal-title":"Nat Methods"},{"key":"2025082008424958800_ref40","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-019-1850-9","article-title":"A benchmark of batch-effect correction methods for single-cell RNA sequencing data","volume":"21","author":"Tran","year":"2020","journal-title":"Genome Biol"},{"key":"2025082008424958800_ref41","doi-asserted-by":"publisher","volume-title":"Journal of Open Source Software","DOI":"10.21105\/joss.00861"},{"key":"2025082008424958800_ref42a","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume-title":"Proceedings of the Second International Conference on Knowledge Discovery and Data Mining; 1996","author":"Ester"},{"key":"2025082008424958800_ref42","volume-title":"Advances in Neural Information Processing Systems"},{"key":"2025082008424958800_ref43","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell Syst"},{"key":"2025082008424958800_ref44","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.cels.2016.09.002","article-title":"A single-cell transcriptome atlas of the human pancreas","volume":"3","author":"Muraro","year":"2016","journal-title":"Cell Syst"},{"key":"2025082008424958800_ref45","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.cmet.2016.08.018","article-title":"RNA sequencing of single human islet cells reveals type 2 diabetes genes","volume":"24","author":"Xin","year":"2016","journal-title":"Cell Metab"},{"key":"2025082008424958800_ref46","doi-asserted-by":"publisher","first-page":"3028","DOI":"10.2337\/db16-0405","article-title":"Single-cell transcriptomics of the human endocrine pancreas","volume":"65","author":"Wang","year":"2016","journal-title":"Diabetes"},{"key":"2025082008424958800_ref47","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1016\/j.cmet.2016.08.020","article-title":"Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes","volume":"24","author":"Segerstolpe","year":"2016","journal-title":"Cell Metab"},{"key":"2025082008424958800_ref48","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1101\/gr.212720.116","article-title":"Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes","volume":"27","author":"Lawlor","year":"2017","journal-title":"Genome Res"},{"key":"2025082008424958800_ref49","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1038\/s41586-018-0590-4","article-title":"Single-cell transcriptomics of 20 mouse organs creates a tabula Muris","volume":"562","author":"Tabula Muris Consortium, Overall coordination, Logistical coordination, Organ collection and processing, Library preparation and sequencing, Computational data analysis, Cell type annotation, Writing group, Supplemental text writing group, and Principal investigators","year":"2018","journal-title":"Nature"},{"key":"2025082008424958800_ref50","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1038\/s41587-020-0465-8","article-title":"Systematic comparison of single-cell and single-nucleus RNA-sequencing methods","volume":"38","author":"Ding","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2025082008424958800_ref51","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1038\/nn.4216","article-title":"Adult mouse cortical cell taxonomy revealed by single cell transcriptomics","volume":"19","author":"Tasic","year":"2016","journal-title":"Nat Neurosci"},{"key":"2025082008424958800_ref52","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nn.4495","article-title":"A molecular census of arcuate hypothalamus and median eminence cell types","volume":"20","author":"Campbell","year":"2017","journal-title":"Nat Neurosci"},{"key":"2025082008424958800_ref53","doi-asserted-by":"publisher","volume-title":"Computational and Structural Biotechnology Journal","DOI":"10.1016\/j.csbj.2025.07.019"},{"key":"2025082008424958800_ref54","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1038\/s41587-023-01767-y","article-title":"Dictionary learning for integrative, multimodal and scalable single-cell analysis","volume":"42","author":"Hao","year":"2024","journal-title":"Nat Biotechnol"},{"key":"2025082008424958800_ref55","doi-asserted-by":"publisher","first-page":"531","DOI":"10.3390\/genes10070531","article-title":"SCINA: a semi-supervised subtyping algorithm of single cells and bulk samples","volume":"10","author":"Zhang","year":"2019","journal-title":"Genes"},{"key":"2025082008424958800_ref56","doi-asserted-by":"publisher","first-page":"4930","DOI":"10.1182\/blood-2013-02-486217","article-title":"Mast cell and macrophage chemokines CXCL1\/CXCL2 control the early stage of neutrophil recruitment during tissue inflammation","volume":"121","author":"De Filippo","year":"2013","journal-title":"Blood"},{"key":"2025082008424958800_ref57","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1093\/cvr\/cvae066","article-title":"Mast cells: a novel therapeutic avenue for cardiovascular diseases?","volume":"120","author":"Poto","year":"2024","journal-title":"Cardiovasc Res"},{"key":"2025082008424958800_ref58","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1038\/s41586-020-2922-4","article-title":"A molecular cell atlas of the human lung from single-cell RNA sequencing","volume":"587","author":"Travaglini","year":"2020","journal-title":"Nature"},{"key":"2025082008424958800_ref59","doi-asserted-by":"publisher","first-page":"D596","DOI":"10.1093\/nar\/gkab1020","article-title":"DISCO: a database of deeply integrated human single-cell omics data","volume":"50","author":"Li","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2025082008424958800_ref60","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1038\/nbt.3102","article-title":"Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells","volume":"33","author":"Buettner","year":"2015","journal-title":"Nat Biotechnol"},{"key":"2025082008424958800_ref61","doi-asserted-by":"publisher","volume":"6","journal-title":"Sci Immunol","DOI":"10.1126\/sciimmunol.abe6291"},{"key":"2025082008424958800_ref62","doi-asserted-by":"publisher","first-page":"bbae072","DOI":"10.1093\/bib\/bbae072","article-title":"scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration","volume":"25","author":"Xiong","year":"2024","journal-title":"Brief Bioinform"},{"key":"2025082008424958800_ref63","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.12688\/f1000research.15666.2","article-title":"A systematic performance evaluation of clustering methods for single-cell RNA-seq data","volume":"7","author":"Du\u00f2","year":"2018","journal-title":"F1000Research"},{"key":"2025082008424958800_ref64","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1111\/cea.13732","article-title":"Gene signatures from scRNA-seq accurately quantify mast cells in biopsies in asthma","volume":"50","author":"Jiang","year":"2020","journal-title":"Clin Exp Allergy"},{"key":"2025082008424958800_ref65","doi-asserted-by":"publisher","first-page":"101069","DOI":"10.1016\/j.jhepr.2024.101069","article-title":"Interleukin-7-based identification of liver lymphatic endothelial cells reveals their unique structural features","volume":"6","author":"Yang","year":"2024","journal-title":"JHEP Reports"},{"key":"2025082008424958800_ref66","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1182\/blood-2008-02-078071","article-title":"B lymphocytes: how they develop and function","volume":"112","author":"LeBien","year":"2008","journal-title":"Blood"},{"key":"2025082008424958800_ref67","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1038\/nrc1802","article-title":"Common markers of proliferation","volume":"6","author":"Whitfield","year":"2006","journal-title":"Nat Rev Cancer"},{"key":"2025082008424958800_ref68","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1038\/nbt.4091","article-title":"Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors","volume":"36","author":"Haghverdi","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2025082008424958800_ref69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41421-019-0114-x","article-title":"A novel approach to remove the batch effect of single-cell data","volume":"5","author":"Feng Zhang","year":"2019","journal-title":"Cell Discovery"},{"key":"2025082008424958800_ref70","doi-asserted-by":"crossref","first-page":"bbaa097","DOI":"10.1093\/bib\/bbaa097","article-title":"SMNN: batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection","volume":"22","author":"Yang","year":"2020","journal-title":"Brief Bioinform"},{"key":"2025082008424958800_ref71","doi-asserted-by":"crossref","first-page":"4945","DOI":"10.1214\/17-EJS1335SI","article-title":"Fast Bayesian hyperparameter optimization on large datasets","volume":"11","author":"Klein","year":"2017","journal-title":"Electron J Stat"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/4\/bbaf428\/64089052\/bbaf428.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/26\/4\/bbaf428\/64089052\/bbaf428.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:42:57Z","timestamp":1755693777000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaf428\/8238522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":72,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,7,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaf428","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,7]]},"published":{"date-parts":[[2025,7]]},"article-number":"bbaf428"}}