c-CSN: Single-Cell RNA Sequencing Data Analysis by Conditional Cell-Specific Network

Li, Lin; Dai, Hao; Fang, Zhaoyuan; Chen, Luonan

doi:10.1016/j.gpb.2020.05.005

Cited by 29 publications

(26 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Notably, scGET is model-free, that is, the SGE strategy requires neither feature selection nor model/parameter training. In summary, SGE opens a new way to predict a cell fate transition at the single-cell level, which is helpful in tracking cell heterogeneity and elucidating the molecular mechanism underlying embryonic cell differentiation by combining with statistics-based and dynamics-based data science [54] , [55] .…”

Section: Discussionmentioning

confidence: 99%

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

Zhong

Han

Zhang

et al. 2021

Genomics, Proteomics &Amp; Bioinformatics

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Zhong

Han

Zhang

et al. 2021

Genomics, Proteomics &Amp; Bioinformatics

View full text Add to dashboard Cite

show abstract

“…In this study, CCSN is constructed for each endocrine cell based on the cell-specific network (CSN) proposed by Dai et al [ 11 ] and the CCSN proposed by Li et al [ 8 ].…”

Section: Methodsmentioning

confidence: 99%

“…In addition, there are few studies on single-cell gene association networks. To fully extract the information for single-cell data, Li et al transformed ‘unstable’ gene expression data into ‘stable’ gene association data by constructing a gene–gene direct association network at the single-cell level [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Research on Potential Network Markers and Signaling Pathways in Type 2 Diabetes Based on Conditional Cell-Specific Network

Xie

Cui

Wang

et al. 2022

Genes

View full text Add to dashboard Cite

Traditional methods concerning type 2 diabetes (T2D) are limited to grouped cells instead of each single cell, and thus the heterogeneity of single cells is erased. Therefore, it is still challenging to study T2D based on a single-cell and network perspective. In this study, we construct a conditional cell-specific network (CCSN) for each single cell for the GSE86469 dataset which is a single-cell transcriptional set from nondiabetic (ND) and T2D human islet samples, and obtain a conditional network degree matrix (CNDM). Since beta cells are the key cells leading to T2D, we search for hub genes in CCSN of beta cells and find that ATP6AP2 is essential for regulation and storage of insulin, and the renin-angiotensin system involving ATP6AP2 is related to most pathological processes leading to diabetic nephropathy. The communication between beta cells and other endocrine cells is performed and three gene pairs with obvious interaction are found. In addition, different expression genes (DEGs) are found based on CNDM and the gene expression matrix (GEM), respectively. Finally, ‘dark’ genes are identified, and enrichment analysis shows that NFATC2 is involved in the VEGF signaling pathway and indirectly affects the production of Prostacyclin (PGI2), which may be a potential biomarker for diabetic nephropathy.

show abstract

“…Ye et al (2020) use coexpression network analysis and subgraph learning to identify interactive gene groups within subpopulations of cells from scRNA-seq data. Both Dai et al (2019) and Li et al (2021) propose novel methods to create cell-specific networks to examine the overall associations between genes for each individual cell. From these cell-specific networks, researchers can further identify changes in gene-gene networks across different cellular populations and/or different time points.…”

Section: Introductionmentioning

confidence: 99%

Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models

2022

View full text Add to dashboard Cite

Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing provides new opportunities to explore these changing gene-gene interactions. Here, we present a sparse hierarchical Bayesian factor model to identify differences across network structures from different biological conditions in scRNA-seq data. Our methodology utilizes latent factors to impact gene expression values for each cell to help account for zero-inflation, increased cell-to-cell variability, and overdispersion that are unique characteristics of scRNA-seq data. Condition-dependent parameters determine which latent factors are activated in a gene, which allows for not only the calculation of gene-gene co-expression within each group but also the calculation of the co-expression differences between groups. We highlight our methodology’s performance in detecting differential gene-gene associations across groups by analyzing simulated datasets and a SARS-CoV-2 case study dataset.

show abstract

c-CSN: Single-Cell RNA Sequencing Data Analysis by Conditional Cell-Specific Network

Cited by 29 publications

References 65 publications

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

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

Research on Potential Network Markers and Signaling Pathways in Type 2 Diabetes Based on Conditional Cell-Specific Network

Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models

Contact Info

Product

Resources

About