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

Peizhuo Wang, Xiao Wen, Han Li, Peng Lang, Shuya Li, Yipin Lei, Hantao Shu, Lin Gao, Dan Zhao () and Jianyang Zeng ()
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Peizhuo Wang: Tsinghua University
Xiao Wen: Chinese Academy of Sciences and China National Center for Bioinformation
Han Li: Tsinghua University
Peng Lang: Tsinghua University
Shuya Li: Tsinghua University
Yipin Lei: Tsinghua University
Hantao Shu: Tsinghua University
Lin Gao: Xidian University
Dan Zhao: Tsinghua University
Jianyang Zeng: Tsinghua University

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-44103-3

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