{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T05:45:33Z","timestamp":1775627133459,"version":"3.50.1"},"reference-count":50,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":26,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82170045"],"award-info":[{"award-number":["82170045"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Translational Medicine Cross Research Fund of Shanghai Jiao Tong University","award":["ZH2018QNB29"],"award-info":[{"award-number":["ZH2018QNB29"]}]},{"name":"Macao Polytechnic University Internal Research","award":["RP\/FCSD-02\/2022"],"award-info":[{"award-number":["RP\/FCSD-02\/2022"]}]},{"name":"Macao Polytechnic University Internal Research","award":["RP\/FCSD-03\/2024"],"award-info":[{"award-number":["RP\/FCSD-03\/2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,5,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurately deconvoluting cellular composition from bulk RNA-seq data is pivotal for understanding the tumor microenvironment and advancing precision medicine. Existing methods often struggle to consistently and accurately quantify cell types across heterogeneous RNA-seq datasets, particularly when ground truths are unavailable. In this study, we introduce DECEPTICON, a deconvolution strategy inspired by the Anna Karenina principle, which postulates that successful outcomes share common traits, while failures are more varied. DECEPTICON selects top-performing methods by leveraging correlations between different strategies and combines them dynamically to enhance performance. Our approach demonstrates superior accuracy in predicting cell-type proportions across multiple tumor datasets, improving correlation by 23.9% and reducing root mean square error by 73.5% compared to the best of 50 analyzed strategies. Applied to The Cancer Genome Atlas (TCGA) datasets for breast carcinoma, cervical squamous cell carcinoma, and lung adenocarcinoma, DECEPTICON-based predictions showed improved differentiation between patient prognoses. This correlation-based strategy offers a reliable, flexible tool for deconvoluting complex transcriptomic data and highlights its potential in refining prognostic assessments in oncology and advancing cancer biology.<\/jats:p>","DOI":"10.1093\/bib\/bbaf234","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T09:18:02Z","timestamp":1748337482000},"source":"Crossref","is-referenced-by-count":3,"title":["DECEPTICON: a correlation-based strategy for RNA-seq deconvolution inspired by a variation of the Anna Karenina principle"],"prefix":"10.1093","volume":"26","author":[{"given":"Fulan","family":"Deng","sequence":"first","affiliation":[{"name":"School of Materials Science and Engineering, Shanghai Institute of Technology , 100 Haiquan Road, Fengxian District, Shanghai 201418 ,","place":["China"]},{"name":"Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University , Rua de Lu\u00eds Gonzaga Gomes, 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Center, Department of Pathology, Zhongshan Hospital, Fudan University, 966 Huaihai Middle Road , Xuhui District, Shanghai 200032,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henry H Y","family":"Tong","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University , Rua de Lu\u00eds Gonzaga Gomes, Macao SAR 999078 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"Center for Ultrafast Science and Technology, Key Laboratory for Laser Plasmas (Ministry of Education), School of Physics and Astronomy, Collaborative Innovation Center of IFSA (CICIFSA, Shanghai Jiao Tong University , 800 Dongchuan Road, Minhang District, Shanghai 200240 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