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Biol."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Single\u2010cell RNA sequencing (scRNA\u2010seq) data provides a whole new view to study disease and cell differentiation development. With the explosive increment of scRNA\u2010seq data, effective models are demanded for mining the intrinsic biological information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This paper proposes a novel non\u2010negative matrix factorization (NMF) method for clustering and gene co\u2010expression network analysis, termed Adaptive Total Variation Constraint Hypergraph Regularized NMF (ATV\u2010HNMF). ATV\u2010HNMF can adaptively select the different schemes to denoise the cluster or preserve the cluster boundary information between clusters based on the gradient information. Besides, ATV\u2010HNMF incorporates hypergraph regularization, which can consider high\u2010order relationships between cells to reserve the intrinsic structure of the space.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Experiments show that the performances on clustering outperform other compared methods, and the network construction results are consistent with previous studies, which illustrate that our model is effective and useful.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>From the clustering results, we can see that ATV\u2010HNMF outperforms other methods, which can help us to understand the heterogeneity. We can discover many disease\u2010related genes from the constructed network, and some are worthy of further clinical exploration.<\/jats:p><\/jats:sec>","DOI":"10.15302\/j-qb-021-0261","type":"journal-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T02:08:05Z","timestamp":1626055685000},"page":"451-462","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive total variation constraint hypergraph regularized NMF for single\u2010cell RNA\u2010seq data analysis"],"prefix":"10.1002","volume":"9","author":[{"given":"Ya\u2010Li","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Computer Science Qufu Normal University Rizhao 276826 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao\u2010Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science Qufu Normal University Rizhao 276826 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan\u2010Yuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science Qufu Normal University Rizhao 276826 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin\u2010Xing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science Qufu Normal University Rizhao 276826 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang\u2010Zhen","family":"Kong","sequence":"additional","affiliation":[{"name":"School of Computer Science Qufu Normal University Rizhao 276826 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,12]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_7_2_2","DOI":"10.1126\/science.aah4573"},{"doi-asserted-by":"publisher","key":"e_1_2_7_3_2","DOI":"10.1038\/nmeth.1315"},{"doi-asserted-by":"publisher","key":"e_1_2_7_4_2","DOI":"10.1101\/gr.110882.110"},{"doi-asserted-by":"publisher","key":"e_1_2_7_5_2","DOI":"10.1016\/j.cmet.2016.08.018"},{"doi-asserted-by":"publisher","key":"e_1_2_7_6_2","DOI":"10.1172\/jci.insight.90558"},{"doi-asserted-by":"publisher","key":"e_1_2_7_7_2","DOI":"10.1038\/nn.3881"},{"doi-asserted-by":"publisher","key":"e_1_2_7_8_2","DOI":"10.1126\/science.1254257"},{"doi-asserted-by":"publisher","key":"e_1_2_7_9_2","DOI":"10.1038\/nature13173"},{"key":"e_1_2_7_10_2","first-page":"2579","article-title":"Visualizing data using t\u2010SNE.","volume":"9","author":"Maaten L. d.","year":"2008","journal-title":"J. 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