{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:14:08Z","timestamp":1785291248778,"version":"3.55.0"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":2,"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":["12001408"],"award-info":[{"award-number":["12001408"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12161039"],"award-info":[{"award-number":["12161039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12401649"],"award-info":[{"award-number":["12401649"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for studying cellular heterogeneity and complexity. However, dropout events in single-cell RNA-seq data severely hinder the effectiveness and accuracy of downstream analysis. Therefore, data preprocessing with imputation methods is crucial to scRNA-seq analysis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address the issue of oversmoothing in smoothing-based imputation methods, the presented AcImpute, an unsupervised method that enhances imputation accuracy by constraining the smoothing weights among cells for genes with different expression levels. Compared with nine other imputation methods in cluster analysis and trajectory inference, the experimental results can demonstrate that AcImpute effectively restores gene expression, preserves inter-cell variability, preventing oversmoothing and improving clustering and trajectory inference performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code is available at https:\/\/github.com\/Liutto\/AcImpute.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae711","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:11:36Z","timestamp":1741133496000},"source":"Crossref","is-referenced-by-count":6,"title":["AcImpute: a constraint-enhancing smooth-based approach for imputing single-cell RNA sequencing data"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8884-7956","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics, Wuhan Institute of Technology , Wuhan 430205,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Physics, Wuhan Institute of Technology , Wuhan 430205,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Physics, Wuhan Institute of Technology , Wuhan 430205,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2981-8976","authenticated-orcid":false,"given":"Yuanyuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Physics, Wuhan Institute of Technology , Wuhan 430205,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,3,3]]},"reference":[{"key":"2025030920015254500_btae711-B1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-019-1837-6","article-title":"DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data","volume":"20","author":"Arisdakessian","year":"2019","journal-title":"Genome Biol"},{"key":"2025030920015254500_btae711-B2","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cels.2016.08.011","article-title":"A single-cell transcriptomic map of the human and mouse pancreas reveals inter-and intra-cell population structure","volume":"3","author":"Baron","year":"2016","journal-title":"Cell 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