A kernel non-negative matrix factorization framework for single cell clustering
文献类型:期刊论文
作者 | Jiang, Hao1; Yi, Ming2; Zhang, Shihua3![]() |
刊名 | APPLIED MATHEMATICAL MODELLING
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出版日期 | 2021-02-01 |
卷号 | 90页码:875-888 |
关键词 | Single cell RNA-sequencing Kernel non-negative matrix factorization Cellular heterogeneity |
ISSN号 | 0307-904X |
DOI | 10.1016/j.apm.2020.08.065 |
英文摘要 | The 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. |
资助项目 | National Natural Science Foundation of China[11901575] ; National Natural Science Foundation of China[91730301] ; National Natural Science Foundation of China[11675060] |
WOS研究方向 | Engineering ; Mathematics ; Mechanics |
语种 | 英语 |
WOS记录号 | WOS:000590968700001 |
出版者 | ELSEVIER SCIENCE INC |
源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/57828] ![]() |
专题 | 应用数学研究所 |
通讯作者 | Jiang, Hao |
作者单位 | 1.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 |
推荐引用方式 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. |
入库方式: OAI收割
来源:数学与系统科学研究院
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