Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix
文献类型：期刊论文
| 作者 | Jiang, Hanjing1; Huang, Yabing2; Li, Qianpeng3 |
| 刊名 | BRIEFINGS IN BIOINFORMATICS
 |
| 出版日期 | 2022-04-13 |
| 页码 | 12 |
| ISSN号 | 1467-5463 |
| 关键词 | scRNA-seq Sigmoid kernel Siamese CNN improved spectral clustering |
| DOI | 10.1093/bib/bbac113 |
| 通讯作者 | Huang, Yabing(ybhuangwhu@163.com) |
| 英文摘要 | 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. |
| WOS关键词 | CELLULAR HETEROGENEITY ; GENE-EXPRESSION ; EMBRYOS ; FATE |
| 资助项目 | National Natural Science Foundation of China[62172171] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| 出版者 | OXFORD UNIV PRESS |
| WOS记录号 | WOS:000785718100001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48304]  |
| 专题 | 国家专用集成电路设计工程技术研究中心_新型计算技术 |
| 通讯作者 | Huang, Yabing |
| 作者单位 | 1.Huazhong Univ Sci & Technol, Inst Artificial Intelligence, Key Lab Image Informat Proc & Intelligent Control, Sch Artificial Intelligence & Automat,Educ Minist, Wuhan 430074, Peoples R China 2.Wuhan Univ, Dept Pathol, Renmin Hosp, Wuhan 430060, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Hanjing,Huang, Yabing,Li, Qianpeng. Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix[J]. BRIEFINGS IN BIOINFORMATICS,2022:12. |
| APA | Jiang, Hanjing,Huang, Yabing,&Li, Qianpeng.(2022).Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix.BRIEFINGS IN BIOINFORMATICS,12. |
| MLA | Jiang, Hanjing,et al."Spectral clustering of single cells using Siamese nerual network combined with improved affinity matrix".BRIEFINGS IN BIOINFORMATICS (2022):12. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。