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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,Shaokun1,3; Ma,Liang2; Wan,Lin1,3
2019-04-04
Source PublicationBMC Genomics
ISSN1471-2164
Volume20Issue:Suppl 2
AbstractAbstractBackgroundTime 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.
KeywordNonlinear dimensionality reduction Elastic embedding Visualization Single-cell RNA sequencing Time series Cell fate decisions Gene expression pattern Oscillation In-group proportion
DOI10.1186/s12864-019-5477-8
Language英语
WOS IDBMC:10.1186/s12864-019-5477-8
PublisherBioMed Central
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/33262
Collection系统科学研究所
Affiliation1.
2.
3.
Recommended Citation
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(Suppl 2).
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(Suppl 2).
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.Suppl 2(2019).
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