{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:07:11Z","timestamp":1780765631778,"version":"3.54.1"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":["61873001"],"award-info":[{"award-number":["61873001"]}],"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":["U19A2064"],"award-info":[{"award-number":["U19A2064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xinjiang Autonomous Region University Research Program","award":["XJEDU2019Y002"],"award-info":[{"award-number":["XJEDU2019Y002"]}]},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province","award":["IMIS202105"],"award-info":[{"award-number":["IMIS202105"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The progress of single-cell RNA sequencing (scRNA-seq) has led to a large number of scRNA-seq data, which are widely used in biomedical research. The noise in the raw data and tens of thousands of genes pose a challenge to capture the real structure and effective information of scRNA-seq data. Most of the existing single-cell analysis methods assume that the low-dimensional embedding of the raw data belongs to a Gaussian distribution or a low-dimensional nonlinear space without any prior information, which limits the flexibility and controllability of the model to a great extent. In addition, many existing methods need high computational cost, which makes them difficult to be used to deal with large-scale datasets. Here, we design and develop a depth generation model named Gaussian mixture adversarial autoencoders (scGMAAE), assuming that the low-dimensional embedding of different types of cells follows different Gaussian distributions, integrating Bayesian variational inference and adversarial training, as to give the interpretable latent representation of complex data and discover the statistical distribution of different types of cells. The scGMAAE is provided with good controllability, interpretability and scalability. Therefore, it can process large-scale datasets in a short time and give competitive results. scGMAAE outperforms existing methods in several ways, including dimensionality reduction visualization, cell clustering, differential expression analysis and batch effect removal. Importantly, compared with most deep learning methods, scGMAAE requires less iterations to generate the best results.<\/jats:p>","DOI":"10.1093\/bib\/bbac585","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T14:37:22Z","timestamp":1672670242000},"source":"Crossref","is-referenced-by-count":17,"title":["scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data"],"prefix":"10.1093","volume":"24","author":[{"given":"Hai-Yun","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mathematics and System Sciences, Xinjiang University , Urumqi , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8486-744X","authenticated-orcid":false,"given":"Jian-Ping","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Mathematics and System Sciences, Xinjiang University , Urumqi , China"},{"name":"Institute of Mathematics and Physics, Xinjiang University , Urumqi , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Hou","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Mathematics and System Sciences, Xinjiang University , Urumqi , China"},{"name":"School of Artificial Intelligence, Anhui University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan-Sen","family":"Su","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"issue":"2","key":"2023011917113861500_ref1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1038\/nbt.3102","article-title":"Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells","volume":"33","author":"Buettner","year":"2015","journal-title":"Nat 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