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

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 b

SUBMITTER: Wang P 

PROVIDER: S-EPMC10733330 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature