{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:31:18Z","timestamp":1774506678197,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T00:00:00Z","timestamp":1626048000000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["20ZR1407700"],"award-info":[{"award-number":["20ZR1407700"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Program of National Natural Science Foundation of China","award":["61932008"],"award-info":[{"award-number":["61932008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell-type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this article, we propose a semi-supervised learning method for cell-type annotation called CALLR. It combines unsupervised learning represented by the graph Laplacian matrix constructed from all the cells and supervised learning using sparse logistic regression. By alternately updating the cell clusters and annotation labels, high annotation accuracy can be achieved. The model is formulated as an optimization problem, and a computationally efficient algorithm is developed to solve it. Experiments on 10 real datasets show that CALLR outperforms the compared (semi-)supervised learning methods, and the popular clustering methods.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The implementation of CALLR is available at https:\/\/github.com\/MathSZhang\/CALLR.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab286","type":"journal-article","created":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T11:17:07Z","timestamp":1619263027000},"page":"i51-i58","source":"Crossref","is-referenced-by-count":28,"title":["CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data"],"prefix":"10.1093","volume":"37","author":[{"given":"Ziyang","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Chicago , Chicago, IL 60637, USA"},{"name":"School of Mathematical Sciences, Fudan University , Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8223-844X","authenticated-orcid":false,"given":"Shuqin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Fudan University , Shanghai 200433, China"},{"name":"Laboratory of Mathematics for Nonlinear Science, Fudan University , Shanghai 200433, China"},{"name":"Shanghai Key Laboratory for Contemporary Applied Mathematics, Fudan University , Shanghai 200433, China"}]}],"member":"286","published-online":{"date-parts":[[2021,7,12]]},"reference":[{"key":"2023062410181579800_btab286-B1","doi-asserted-by":"crossref","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. 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