PUBLICATION
TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data
- Authors
- An, S., Ma, L., Wan, L.
- ID
- ZDB-PUB-190411-4
- Date
- 2019
- Source
- BMC Genomics 20: 224 (Journal)
- Registered Authors
- Keywords
- Cell fate decisions, Elastic embedding, Gene expression pattern, In-group proportion, Nonlinear dimensionality reduction, Oscillation, Single-cell RNA sequencing, Time series, Visualization
- MeSH Terms
-
- Algorithms*
- Animals
- Computational Biology/methods*
- Embryonic Development*
- Gene Expression Profiling
- Gene Expression Regulation, Developmental*
- High-Throughput Nucleotide Sequencing/methods*
- Humans
- Sequence Analysis, RNA/methods*
- Single-Cell Analysis/methods*
- Time Factors
- Zebrafish/genetics
- PubMed
- 30967106 Full text @ BMC Genomics
Citation
An, S., Ma, L., Wan, L. (2019) TSEE: an elastic embedding method to visualize the dynamic gene expression patterns of time series single-cell RNA sequencing data. BMC Genomics. 20:224.
Abstract
Background Time 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.
Results In 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.
Conclusions TSEE 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.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping