{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:44:33Z","timestamp":1753875873979,"version":"3.41.2"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:00:00Z","timestamp":1632268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076109"],"award-info":[{"award-number":["62076109"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["20190103006JH"],"award-info":[{"award-number":["20190103006JH"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Special Administrative Region","award":["CityU 11200218","07181426"],"award-info":[{"award-number":["CityU 11200218","07181426"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https:\/\/github.com\/li-shaochuan\/SEDIM.<\/jats:p>","DOI":"10.1093\/bib\/bbab368","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T11:14:40Z","timestamp":1629371680000},"source":"Crossref","is-referenced-by-count":6,"title":["High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning"],"prefix":"10.1093","volume":"23","author":[{"given":"Xiangtao","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Jilin, China"},{"name":"Department of Computer science, City University of Hong Kong, Hong Kong SAR"}]},{"given":"Shaochuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Jilin, China"}]},{"given":"Lei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer science, City University of Hong Kong, Hong Kong SAR"}]},{"given":"Shixiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer science, City University of Hong Kong, Hong Kong SAR"}]},{"given":"Ka-chun","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer science, City University of Hong Kong, Hong Kong SAR"}]}],"member":"286","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"issue":"1","key":"2022011921020351200_ref1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nrg2484","article-title":"RNA-seq: a revolutionary tool for transcriptomics","volume":"10","author":"Wang","year":"2009","journal-title":"Nat Rev Genet"},{"issue":"1","key":"2022011921020351200_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-020-02132-x","article-title":"A systematic evaluation of single-cell RNA-sequencing imputation methods","volume":"21","author":"Hou","year":"2020","journal-title":"Genome 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