TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data

doi:10.1186/s12864-019-5477-8

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	TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data
	An, Shaokun 1,3; Ma, Liang 2; Wan, Lin1,3
	2019-04-04
发表期刊	BMC GENOMICS
ISSN	1471-2164
卷号	20 页码:16
摘要	BackgroundTime series single-cell RNA sequencing (scRNA-seq) data are emerging. However, the analysis of time series scRNA-seq data could be compromised by 1) distortion created by assorted sources of data collection and generation across time samples and 2) inheritance of cell-to-cell variations by stochastic dynamic patterns of gene expression. This calls for the development of an algorithm able to visualize time series scRNA-seq data in order to reveal latent structures and uncover dynamic transition processes.ResultsIn this study, we propose an algorithm, termed time series elastic embedding (TSEE), by incorporating experimental temporal information into the elastic embedding (EE) method, in order to visualize time series scRNA-seq data. TSEE extends the EE algorithm by penalizing the proximal placement of latent points that correspond to data points otherwise separated by experimental time intervals. TSEE is herein used to visualize time series scRNA-seq datasets of embryonic developmental processed in human and zebrafish. We demonstrate that TSEE outperforms existing methods (e.g. PCA, tSNE and EE) in preserving local and global structures as well as enhancing the temporal resolution of samples. Meanwhile, TSEE reveals the dynamic oscillation patterns of gene expression waves during zebrafish embryogenesis.ConclusionsTSEE can efficiently visualize time series scRNA-seq data by diluting the distortions of assorted sources of data variation across time stages and achieve the temporal resolution enhancement by preserving temporal order and structure. TSEE uncovers the subtle dynamic structures of gene expression patterns, facilitating further downstream dynamic modeling and analysis of gene expression processes. The computational framework of TSEE is generalizable by allowing the incorporation of other sources of information.
关键词	Nonlinear dimensionality reduction Elastic embedding Visualization Single-cell RNA sequencing Time series Cell fate decisions Gene expression pattern Oscillation In-group proportion
DOI	10.1186/s12864-019-5477-8
语种	英语
资助项目	NSFC[11571349] ; NSFC[91630314] ; NSFC[81673833] ; Strategic Priority Research Program of Chinese Academy of Sciences (CAS)[XDB13050000] ; NCMIS of CAS ; LSC of CAS ; Youth Innovation Promotion Association of CAS ; Mathematical Biosciences Institute (MBI) at Ohio State University ; NSF[DMS 1440386]
WOS研究方向	Biotechnology & Applied Microbiology ; Genetics & Heredity
WOS类目	Biotechnology & Applied Microbiology ; Genetics & Heredity
WOS记录号	WOS:000464120900011
出版者	BMC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/33262
专题	系统科学研究所
通讯作者	Ma, Liang; Wan, Lin
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Beijing Inst Genom, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	An, Shaokun,Ma, Liang,Wan, Lin. TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data[J]. BMC GENOMICS,2019,20:16.
APA	An, Shaokun,Ma, Liang,&Wan, Lin.(2019).TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data.BMC GENOMICS,20,16.
MLA	An, Shaokun,et al."TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data".BMC GENOMICS 20(2019):16.