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Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON.

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Title: Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON.
Authors: Wang, Peizhuo, Wen, Xiao, Li, Han, Lang, Peng, Li, Shuya, Lei, Yipin, Shu, Hantao, Gao, Lin, Zhao, Dan, Zeng, Jianyang
Source: Nature Communications; 12/20/2023, Vol. 14 Issue 1, p1-16, 16p
Subject Terms: TRANSCRIPTOMES, GENETIC regulation, GENE regulatory networks, HEMATOPOIETIC stem cells, CELLULAR control mechanisms, REGULATOR genes
Abstract: Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data. Deciphering the roles of gene regulation in cell fate decisions is crucial. Here, authors present CEFCON, a network-based framework that reveals cell-lineage-specific gene regulatory networks and identifies driver regulators controlling cell fate decisions from single-cell transcriptomics data. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data. Deciphering the roles of gene regulation in cell fate decisions is crucial. Here, authors present CEFCON, a network-based framework that reveals cell-lineage-specific gene regulatory networks and identifies driver regulators controlling cell fate decisions from single-cell transcriptomics data. [ABSTRACT FROM AUTHOR]
ISSN:20411723
DOI:10.1038/s41467-023-44103-3