{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T06:40:42Z","timestamp":1775976042159,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The emergence of single-cell RNA sequencing (scRNA-seq) technology has transformed our understanding of cellular diversity, yet it presents notable challenges for cell type annotation due to data\u2019s high dimensionality and sparsity. To tackle these issues, we present scSorterDL, an innovative approach that combines penalized Linear Discriminant Analysis (pLDA), swarm learning, and deep neural networks (DNNs) to improve cell type classification. In scSorterDL, we generate numerous random subsets of the data and apply pLDA models to each subset to capture varied data aspects. The model outputs are then consolidated using a DNN that identifies complex relationships among the pLDA scores, enhancing classification accuracy by considering interactions that simpler methods might overlook. Utilizing GPU computing for both swarm learning and deep learning, scSorterDL adeptly manages large datasets and high-dimensional gene expression data. We tested scSorterDL on 13 real scRNA-seq datasets from diverse species, tissues, and platforms, as well as on 20 pairs of cross-platform datasets. Our method surpassed nine current cell annotation tools in both accuracy and robustness, indicating exceptional performance in both cross-validation and cross-platform contexts. These findings underscore the potential of scSorterDL as an effective and adaptable tool for automated cell type annotation in scRNA-seq research. The code is available on GitHub: https:\/\/github.com\/kellen8hao\/scSorterDL<\/jats:p>","DOI":"10.1093\/bib\/bbaf446","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T14:25:11Z","timestamp":1756736711000},"source":"Crossref","is-referenced-by-count":4,"title":["scSorterDL: a deep neural network-enhanced ensemble LDAs for single cell classifications"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4996-8875","authenticated-orcid":false,"given":"Kailun","family":"Bai","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, University of Victoria , Victoria, BC V8P 5C2 ,","place":["Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0892-9258","authenticated-orcid":false,"given":"Belaid","family":"Moa","sequence":"additional","affiliation":[{"name":"Digital Research Alliance of Canada , Victoria, BC V8P 5C2 ,","place":["Canada"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3044-621X","authenticated-orcid":false,"given":"Xiaojian","family":"Shao","sequence":"additional","affiliation":[{"name":"Digital Technologies Research Centre, National Research Council Canada , Ottawa, ON K1A 0R6 ,","place":["Canada"]},{"name":"Ottawa Institute of Systems Biology , Department of Biochemistry, Microbiology, and Immunology, , Ottawa, ON K1H8M5 ,","place":["Canada"]},{"name":"University of Ottawa , Department of Biochemistry, Microbiology, and Immunology, , Ottawa, ON K1H8M5 ,","place":["Canada"]}]},{"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 , Victoria, BC V8P 5C2 ,","place":["Canada"]}]}],"member":"286","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"2025090110250733800_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":"2025090110250733800_ref2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13073-017-0467-4","article-title":"A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications","volume":"9","author":"Haque","year":"2017","journal-title":"Genome Med"},{"key":"2025090110250733800_ref3","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":"2025090110250733800_ref4","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":"Pasquini","year":"2019","journal-title":"Nat Methods"},{"key":"2025090110250733800_ref5","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":"2025090110250733800_ref6","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":"2025090110250733800_ref7","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":"2025090110250733800_ref8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0208349","article-title":"Correction: 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":"2025090110250733800_ref9","doi-asserted-by":"publisher","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. 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