scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy.
10.1016/j.gpb.2020.11.008
- Author:
Jiayuan ZHONG
1
;
Chongyin HAN
2
;
Xuhang ZHANG
3
;
Pei CHEN
4
;
Rui LIU
5
,
6
Author Information
1. School of Mathematics, South China University of Technology, Guangzhou 510640, PR China.
2. School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, PR China.
3. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, PR China.
4. School of Mathematics, South China University of Technology, Guangzhou 510640, PR China. Electronic address: chenpei@scut.edu.cn.
5. School of Mathematics, South China University of Technology, Guangzhou 510640, PR China
6. Pazhou Lab, Guangzhou 510330, PR China. Electronic address: scliurui@scut.edu.cn.
- Publication Type:Journal Article
- Keywords:
Cell fate commitment;
Critical transition;
Dark gene;
Embryonic differentiation;
Single-cell graph entropy
- From:
Genomics, Proteomics & Bioinformatics
2021;19(3):461-474
- CountryChina
- Language:English
-
Abstract:
During early embryonic development, cell fate commitment represents a critical transition or "tipping point" of embryonic differentiation, at which there is a drastic and qualitative shift of the cell populations. In this study, we presented a computational approach, scGET, to explore the gene-gene associations based on single-cell RNA sequencing (scRNA-seq) data for critical transition prediction. Specifically, by transforming the gene expression data to the local network entropy, the single-cell graph entropy (SGE) value quantitatively characterizes the stability and criticality of gene regulatory networks among cell populations and thus can be employed to detect the critical signal of cell fate or lineage commitment at the single-cell level. Being applied to five scRNA-seq datasets of embryonic differentiation, scGET accurately predicts all the impending cell fate transitions. After identifying the "dark genes" that are non-differentially expressed genes but sensitive to the SGE value, the underlying signaling mechanisms were revealed, suggesting that the synergy of dark genes and their downstream targets may play a key role in various cell development processes.The application in all five datasets demonstrates the effectiveness of scGET in analyzing scRNA-seq data from a network perspective and its potential to track the dynamics of cell differentiation. The source code of scGET is accessible at https://github.com/zhongjiayuna/scGET_Project.
