{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T08:06:16Z","timestamp":1772697976438,"version":"3.50.1"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["W2431045"],"award-info":[{"award-number":["W2431045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDB38050100"],"award-info":[{"award-number":["XDB38050100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Spatial clustering is a critical analytical task in spatial transcriptomics (ST) that aids in uncovering the spatial molecular mechanisms underlying biological phenotypes. Along with the numerous spatial clustering methods, there comes the imperative need for an effective metric to evaluate their performance. An ideal metric should consider three factors: label agreement, spatial organization, and error severity. However, existing evaluation metrics focus solely on either label agreement or spatial organization, leading to biased and misleading evaluations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To fill this gap, we propose CEMUSA, a novel graph-based metric that integrates these factors into a unified evaluation framework. Extensive testing on both simulated and real datasets demonstrate CEMUSA\u2019s superiority over conventional metrics in differentiating clustering results with subtle differences in topology and error severity, while maintaining computational efficiency.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The source code and data are freely available at https:\/\/github.com\/YihDu\/CEMUSA. CEMUSA is implemented as an R package at https:\/\/yihdu.github.io\/CEMUSA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag056","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T12:50:00Z","timestamp":1770209400000},"source":"Crossref","is-referenced-by-count":0,"title":["CEMUSA: a graph-based integrative metric for evaluating clusters in spatial transcriptomics"],"prefix":"10.1093","volume":"42","author":[{"given":"Jiaying","family":"Hu","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Southern University of Science and Technology , Shenzhen 518055,","place":["China"]}]},{"given":"Yihang","family":"Du","sequence":"additional","affiliation":[{"name":"School of Statistics and Mathematics, Zhongnan University of Economics and Law , Wuhan 430073,","place":["China"]}]},{"given":"Suyang","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhongnan University of Economics and Law , Wuhan 430073,","place":["China"]}]},{"given":"Yueyang","family":"Ding","sequence":"additional","affiliation":[{"name":"Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Science , Hangzhou 310024,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1833-7413","authenticated-orcid":false,"given":"Jinyan","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology , Shenzhen 518055,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1269-7354","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology , Shenzhen 518055,","place":["China"]},{"name":"Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Shenzhen 518055,","place":["China"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9876-5666","authenticated-orcid":false,"given":"Xiaobo","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Human Genetics, Emory University School of Medicine , Atlanta, GA 30322,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"2026030502115110500_btag056-B1","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1093\/bioinformatics\/btt388","article-title":"Testing for presence of known and unknown molecules in imaging mass 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