{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T02:35:36Z","timestamp":1773110136403,"version":"3.50.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T00:00:00Z","timestamp":1675382400000},"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":["62206086"],"award-info":[{"award-number":["62206086"]}],"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":["62076109"],"award-info":[{"award-number":["62076109"]}],"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":["61972174"],"award-info":[{"award-number":["61972174"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,2,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell RNA sequencing (scRNA-seq) is an increasingly popular technique for transcriptomic analysis of gene expression at the single-cell level. Cell-type clustering is the first crucial task in the analysis of scRNA-seq data that facilitates accurate identification of cell types and the study of the characteristics of their transcripts. Recently, several computational models based on a deep autoencoder and the ensemble clustering have been developed to analyze scRNA-seq data. However, current deep autoencoders are not sufficient to learn the latent representations of scRNA-seq data, and obtaining consensus partitions from these feature representations remains under-explored.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To address this challenge, we propose a single-cell deep clustering model via a dual denoising autoencoder with bipartite graph ensemble clustering called scBGEDA, to identify specific cell populations in single-cell transcriptome profiles. First, a single-cell dual denoising autoencoder network is proposed to project the data into a compressed low-dimensional space and that can learn feature representation via explicit modeling of synergistic optimization of the zero-inflated negative binomial reconstruction loss and denoising reconstruction loss. Then, a bipartite graph ensemble clustering algorithm is designed to exploit the relationships between cells and the learned latent embedded space by means of a graph-based consensus function. Multiple comparison experiments were conducted on 20 scRNA-seq datasets from different sequencing platforms using a variety of clustering metrics. The experimental results indicated that scBGEDA outperforms other state-of-the-art methods on these datasets, and also demonstrated its scalability to large-scale scRNA-seq datasets. Moreover, scBGEDA was able to identify cell-type specific marker genes and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into identifying cell types and characterizing the scRNA-seq data from different perspectives.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code of scBGEDA is available at https:\/\/github.com\/wangyh082\/scBGEDA. The software and the supporting data can be downloaded from https:\/\/figshare.com\/articles\/software\/scBGEDA\/19657911.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad075","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T15:25:28Z","timestamp":1675351528000},"source":"Crossref","is-referenced-by-count":18,"title":["scBGEDA: deep single-cell clustering analysis via a dual denoising autoencoder with bipartite graph ensemble clustering"],"prefix":"10.1093","volume":"39","author":[{"given":"Yunhe","family":"Wang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology , Tianjin, China"}]},{"given":"Zhuohan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Jilin, China"}]},{"given":"Shaochuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Jilin, China"}]},{"given":"Chuang","family":"Bian","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Jilin, China"}]},{"given":"Yanchun","family":"Liang","sequence":"additional","affiliation":[{"name":"Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Science and Technology , Zhuhai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6062-733X","authenticated-orcid":false,"given":"Ka-Chun","family":"Wong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong , Kowloon Tong, Hong Kong SAR"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8716-9823","authenticated-orcid":false,"given":"Xiangtao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University , Jilin, China"}]}],"member":"286","published-online":{"date-parts":[[2023,2,3]]},"reference":[{"key":"2023021321113883000_btad075-B1","first-page":"3625","article-title":"Psychrophilic proteases dramatically reduce single-cell RNA-seq artifacts: a molecular atlas of kidney development","volume":"144","author":"Adam","year":"2017","journal-title":"Development"},{"key":"2023021321113883000_btad075-B2","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.coisb.2017.07.004","article-title":"Single cells make big data: new challenges and opportunities in transcriptomics","volume":"4","author":"Angerer","year":"2017","journal-title":"Curr. 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