{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T04:30:01Z","timestamp":1767846601902,"version":"3.49.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,4,20]],"date-time":"2023-04-20T00:00:00Z","timestamp":1681948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072243"],"award-info":[{"award-number":["62072243"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772273"],"award-info":[{"award-number":["61772273"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872186"],"award-info":[{"award-number":["61872186"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu","award":["BK20201304"],"award-info":[{"award-number":["BK20201304"]}]},{"name":"Foundation of National Defense Key Laboratory of Science and Technology","award":["JZX7Y202001SY000901"],"award-info":[{"award-number":["JZX7Y202001SY000901"]}]},{"name":"National Health and Medical Research Council of Australia","award":["1127948"],"award-info":[{"award-number":["1127948"]}]},{"name":"National Health and Medical Research Council of Australia","award":["1144652"],"award-info":[{"award-number":["1144652"]}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["LP110200333"],"award-info":[{"award-number":["LP110200333"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP120104460"],"award-info":[{"award-number":["DP120104460"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 AI111965"],"award-info":[{"award-number":["R01 AI111965"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https:\/\/github.com\/liuyan3056\/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.<\/jats:p>","DOI":"10.1093\/bib\/bbad132","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T01:18:26Z","timestamp":1682039906000},"source":"Crossref","is-referenced-by-count":15,"title":["TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level"],"prefix":"10.1093","volume":"24","author":[{"given":"Yan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , 200 Xiaolingwei, Nanjing 210094 , China"}]},{"given":"Guo","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Nanjing University , Nanjing 210023 , China"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[{"name":"Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University , Melbourne, Victoria 3800 , Australia"}]},{"given":"Long-Chen","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , 200 Xiaolingwei, Nanjing 210094 , China"}]},{"given":"Robin B","family":"Gasser","sequence":"additional","affiliation":[{"name":"Department of Veterinary Biosciences, Melbourne Veterinary School, The University of Melbourne , Parkville, Victoria 3010 , Australia"}]},{"given":"Jiangning","family":"Song","sequence":"additional","affiliation":[{"name":"Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University , Melbourne, Victoria 3800 , Australia"},{"name":"Monash Data Futures Institute, Monash University , Melbourne, Victoria 3800 , Australia"}]},{"given":"Dijun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Nanjing University , Nanjing 210023 , China"}]},{"given":"Dong-Jun","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology , 200 Xiaolingwei, Nanjing 210094 , China"}]}],"member":"286","published-online":{"date-parts":[[2023,4,20]]},"reference":[{"key":"2023052022123041200_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms15081","article-title":"Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer","volume":"8","author":"Chung","year":"2017","journal-title":"Nat Commun"},{"key":"2023052022123041200_ref2","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.cell.2020.07.017","article-title":"Therapy-induced evolution of human lung cancer revealed by single-cell RNA 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