scGET: Predicting Cell Fate Transition During Early Embryonic Development by Single-cell Graph Entropy

• 作者: Jiayuan Zhong Chongyin Han Xuhang Zhang Pei Chen Rui Liu
• 来源期刊: 基因组蛋白质组与生物信息学报（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: METHOD
• 页码范围: 461-474
• 页数: 14页
• 语种: 英文

摘要

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 regu-latory 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.