TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data
TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data
复制标题
TSEE：一种弹性嵌入方法，可可视化时间序列单细胞 RNA 测序数据的动态基因表达模式
DOI：
10.1186/s12864-019-5477-8
复制
发表时间：
2019-04-04
期刊：
影响因子：
4.4
通讯作者：
Wan, Lin
中科院分区：
文献类型：
--
作者：
An, Shaokun;Ma, Liang;Wan, Lin
时间序列单细胞RNA测序（scRNA-seq）数据正在出现。然而，时间序列scRNA-seq数据的分析可能会受到以下因素的影响：1）不同时间样本的数据收集和生成来源造成的失真，以及2）基因表达的随机动态模式导致的细胞间变异的遗传。这就要求开发一种算法，能够可视化的时间序列scRNA-seq数据，以揭示潜在的结构和发现动态transition processs.ResultsIn这项研究中，我们提出了一种算法，称为时间序列弹性嵌入（TSEE），通过将实验的时间信息到弹性嵌入（EE）的方法，以可视化的时间序列scRNA-seq数据。TSEE扩展了EE算法，通过惩罚与实验时间间隔分开的数据点对应的潜在点的近端位置。TSEE在本文中用于可视化在人类和斑马鱼中处理的胚胎发育的时间序列scRNA-seq数据集。我们证明，TSEE优于现有的方法（如PCA，tSNE和EE）在保持局部和全局结构，以及提高样本的时间分辨率。同时，TSEE揭示了斑马鱼胚胎发育过程中基因表达波动的动态振荡模式。结论TSEE能够有效地可视化时间序列scRNA-seq数据，通过稀释不同时间段的各种数据变化源的失真，并通过保持时间顺序和结构来实现时间分辨率的增强。TSEE揭示了基因表达模式的微妙动态结构，有助于进一步的下游动态建模和基因表达过程的分析。TSEE的计算框架是可推广的，允许合并其他来源的信息。
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.