{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T03:55:34Z","timestamp":1772855734887,"version":"3.50.1"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T00:00:00Z","timestamp":1730851200000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Stable Support Project of Shenzhen","award":["20231122145548001"],"award-info":[{"award-number":["20231122145548001"]}]},{"name":"Stable Support Project of Shenzhen","award":["JCYJ20220531091407016"],"award-info":[{"award-number":["JCYJ20220531091407016"]}]},{"name":"Futian Healthcare Research Project","award":["FTWS055"],"award-info":[{"award-number":["FTWS055"]}]},{"name":"Futian Healthcare Research Project","award":["FTWS069"],"award-info":[{"award-number":["FTWS069"]}]},{"name":"Shenzhen Hospital of Guangzhou University of Chinese Medicine Research Project","award":["GZYSY2024010"],"award-info":[{"award-number":["GZYSY2024010"]}]},{"DOI":"10.13039\/501100015947","name":"Guangdong Province Key Laboratory of Popular High Performance Computers","doi-asserted-by":"publisher","award":["2017B030314073"],"award-info":[{"award-number":["2017B030314073"]}],"id":[{"id":"10.13039\/501100015947","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Provincial Department of Education Youth Talent Project","award":["2024KQNCX052"],"award-info":[{"award-number":["2024KQNCX052"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, which are often present in scRNA-seq data, remaining challenges for downstream analysis. Although a number of studies have been developed to recover single-cell expression profiles, their performance may be hindered due to not fully exploring the inherent relations between genes. To address the issue, we propose scDTL, a deep transfer learning based approach for scRNA-seq data imputation by harnessing the bulk RNA-sequencing information. We firstly employ a denoising autoencoder trained on bulk RNA-seq data as the initial imputation model, and then leverage a domain adaptation framework that transfers the knowledge learned by the bulk imputation model to scRNA-seq learning task. In addition, scDTL employs a parallel operation with a 1D U-Net denoising model to provide gene representations of varying granularity, capturing both coarse and fine features of the scRNA-seq data. Finally, we utilize a cross-channel attention mechanism to fuse the features learned from the transferred bulk imputation model and U-Net model. In the evaluation, we conduct extensive experiments to demonstrate that scDTL could outperform other state-of-the-art methods in the quantitative comparison and downstream analyses.<\/jats:p>","DOI":"10.1093\/bib\/bbae555","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T19:32:57Z","timestamp":1730921577000},"source":"Crossref","is-referenced-by-count":3,"title":["scDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information"],"prefix":"10.1093","volume":"25","author":[{"given":"Liuyang","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University , Guangdong 518057,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Landu","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Future Technology, HKUST(GZ) , Guangdong 510641,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Xie","sequence":"additional","affiliation":[{"name":"Shenzhen Hospital of Guangzhou University of Chinese Medicine (Futian) , Guangdong 518034,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JianHao","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen Hospital of Guangzhou University of Chinese Medicine (Futian) , Guangdong 518034,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoran","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Computing and Decision Sciences, Lingnan University , Hong Kong Special Administrative Region 999077,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Biochemistry , School of Medicine, , Guangdong 518055,","place":["China"]},{"name":"Southern University of Science and Technology , School of Medicine, , Guangdong 518055,","place":["China"]},{"name":"Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology , Shenzhen 518055,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dian","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University , Guangdong 518057,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"2024110619323565800_ref1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad342","article-title":"Deep single-cell RNA-seq data clustering with graph prototypical contrastive 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