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A kernel non-negative matrix factorization framework for single cell clustering
Jiang, Hao1; Yi, Ming2; Zhang, Shihua3
2021-02-01
Source PublicationAPPLIED MATHEMATICAL MODELLING
ISSN0307-904X
Volume90Pages:875-888
AbstractThe emergence of single-cell RNA-sequencing is ideally placed to unravel cellular het-erogeneity in biological systems, an extremely challenging problem in single cell RNA sequencing studies. However, most current computational approaches lack the sensitivity to reliably detect nonlinear gene-gene relationships masked by dropout events. We proposed a kernel non-negative matrix factorization framework for detecting nonlinear relationships among genes, where the new kernel is developed using kernel tricks on cellular differentiability correlation. The newly constructed kernel not only provides a description on the gene-gene relationship, but also helps to build a new low-dimensional representation on the original data. Besides, we developed an efficient method for determining the optimal cluster number within each data set with the usage of Diffusion Maps. The proposed algorithm is further compared with representative algorithms: SC3 and several other state-of-the-art clustering methods, on several benchmark or real scRNA-Seq datasets using internal criteria (clustering number accuracy) and external criteria (Adjusted rand index and Normalized mutual information) to show effectiveness of our method. (c) 2020 Elsevier Inc. All rights reserved.
KeywordSingle cell RNA-sequencing Kernel non-negative matrix factorization Cellular heterogeneity
DOI10.1016/j.apm.2020.08.065
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[11901575] ; National Natural Science Foundation of China[91730301] ; National Natural Science Foundation of China[11675060]
WOS Research AreaEngineering ; Mathematics ; Mechanics
WOS SubjectEngineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications ; Mechanics
WOS IDWOS:000590968700001
PublisherELSEVIER SCIENCE INC
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/57829
Collection应用数学研究所
Corresponding AuthorJiang, Hao
Affiliation1.Renmin Univ China, Sch Math, Beijing 100872, Peoples R China
2.China Univ Geosci, Sch Math & Phys, Wuhan, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jiang, Hao,Yi, Ming,Zhang, Shihua. A kernel non-negative matrix factorization framework for single cell clustering[J]. APPLIED MATHEMATICAL MODELLING,2021,90:875-888.
APA Jiang, Hao,Yi, Ming,&Zhang, Shihua.(2021).A kernel non-negative matrix factorization framework for single cell clustering.APPLIED MATHEMATICAL MODELLING,90,875-888.
MLA Jiang, Hao,et al."A kernel non-negative matrix factorization framework for single cell clustering".APPLIED MATHEMATICAL MODELLING 90(2021):875-888.
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