{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:41:48Z","timestamp":1753875708381,"version":"3.41.2"},"reference-count":49,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T00:00:00Z","timestamp":1649808000000},"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":["62172171"],"award-info":[{"award-number":["62172171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Limitations of bulk sequencing techniques on cell heterogeneity and diversity analysis have been pushed with the development of single-cell RNA-sequencing (scRNA-seq). To detect clusters of cells is a key step in the analysis of scRNA-seq. However, the high-dimensionality of scRNA-seq data and the imbalances in the number of different subcellular types are ubiquitous in real scRNA-seq data sets, which poses a huge challenge to the single-cell-type detection.We propose a meta-learning-based model, SiaClust, which is the combination of Siamese Convolutional Neural Network (CNN) and improved spectral clustering, to achieve scRNA-seq cell type detection. To be specific, with the help of the constrained Sigmoid kernel, the raw high-dimensionality data is mapped to a low-dimensional space, and the Siamese CNN learns the differences between the cell types in the low-dimensional feature space. The similarity matrix learned by Siamese CNN is used in combination with improved spectral clustering and t-distribution Stochastic Neighbor Embedding (t-SNE) for visualization. SiaClust highlights the differences between cell types by comparing the similarity of the samples, whereas blurring the differences within the cell types is better in processing high-dimensional and imbalanced data. SiaClust significantly improves clustering accuracy by using data generated by nine different species and tissues through different scNA-seq protocols for extensive evaluation, as well as analogies to state-of-the-art single-cell clustering models. More importantly, SiaClust accurately locates the exact site of dropout gene, and is more flexible with data size and cell type.<\/jats:p>","DOI":"10.1093\/bib\/bbac113","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T20:10:33Z","timestamp":1646856633000},"source":"Crossref","is-referenced-by-count":4,"title":["Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix"],"prefix":"10.1093","volume":"23","author":[{"given":"Hanjing","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, 430074, Wuhan, China"}]},{"given":"Yabing","family":"Huang","sequence":"additional","affiliation":[{"name":"Renmin Hospital of Wuhan University, Department of Pathology, 430060, Wuhan, China"}]},{"given":"Qianpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Institute of 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