ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering
文献类型：期刊论文
| 作者 | Jiang, Hanjing3; Huang, Yabing2; Li, Qianpeng1 ; Feng, Boyuan3
|
| 刊名 | BMC BIOINFORMATICS
 |
| 出版日期 | 2023-11-07 |
| 卷号 | 24期号:1页码:15 |
| ISSN号 | 1471-2105 |
| 关键词 | Single-cell ScRNA-seq Siamese LSTM Cell type detection |
| DOI | 10.1186/s12859-023-05494-8 |
| 通讯作者 | Huang, Yabing(ybhuangwhu@163.com) |
| 英文摘要 | MotivationCategorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes.ResultTo achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types. |
| WOS关键词 | GENE-EXPRESSION ; HETEROGENEITY ; EMBRYOS ; FATE |
| 资助项目 | The authors would like to thank anonymous reviewers for providing valuable comments for our article. |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| 出版者 | BMC |
| WOS记录号 | WOS:001097000300002 |
| 资助机构 | The authors would like to thank anonymous reviewers for providing valuable comments for our article. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54451]  |
| 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.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Wuhan Univ, Dept Pathol, Renmin Hosp, Wuhan 430060, Peoples R China 3.Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Inst Artificial Intelligence, Key Lab Image Informat Proc & Intelligent Control,, Wuhan 430074, Hubei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Hanjing,Huang, Yabing,Li, Qianpeng,et al. ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering[J]. BMC BIOINFORMATICS,2023,24(1):15. |
| APA | Jiang, Hanjing,Huang, Yabing,Li, Qianpeng,&Feng, Boyuan.(2023).ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering.BMC BIOINFORMATICS,24(1),15. |
| MLA | Jiang, Hanjing,et al."ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering".BMC BIOINFORMATICS 24.1(2023):15. |
入库方式： OAI收割
来源：自动化研究所
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