ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering

doi:10.1186/s12859-023-05494-8

CASIA OpenIR > 国家专用集成电路设计工程技术研究中心 > 新型计算技术

	ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering
	Jiang, Hanjing 1; Huang, Yabing 2; Li, Qianpeng3 ; Feng, Boyuan 1
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
	2023-11-07
卷号	24 期号:1 页码:15
通讯作者	Huang, Yabing(ybhuangwhu@163.com)
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.
关键词	Single-cell ScRNA-seq Siamese LSTM Cell type detection
DOI	10.1186/s12859-023-05494-8
关键词[WOS]	GENE-EXPRESSION ; HETEROGENEITY ; EMBRYOS ; FATE
收录类别	SCI
语种	英语
资助项目	The authors would like to thank anonymous reviewers for providing valuable comments for our article.
项目资助者	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
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS记录号	WOS:001097000300002
出版者	BMC
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54452
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, Sch Artificial Intelligence & Automat, Inst Artificial Intelligence, Key Lab Image Informat Proc & Intelligent Control,, Wuhan 430074, Hubei, Peoples R China 2.Wuhan Univ, Dept Pathol, Renmin Hosp, Wuhan 430060, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, 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.