TY  - JOUR
T1  - Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model
A1  - Wang, Jiacheng
A1  - Chen, Yaojia
A1  - Zou, Quan
Y1  - 2023/09/13
N2  - Author summary Although many methods have been proposed to infer the gene regulatory network of a single cell, they only focus on the regulatory relationships of pairs of genes and ignore the global regulatory structure. Here, we present a deep learning-based model to learn the global regulatory structure and reconstruct the gene regulatory networks from single-cell RNA sequencing data with a graph view. We utilize the weighted gene co-expression analysis to build a prior regulatory graph of gene and a graph autoencoder to deconstruct the latent regulatory structure among genes. We performed extensive experiments on varieties of single-cell RNA sequencing datasets and compared our method with 9 stat-of-the-art gene regulatory network inference method. The results show that our method can significantly improve the accuracy of gene regulatory network inference and can be applied to identify key regulators in a wide range of scenarios.
JF  - PLOS Genetics
JA  - PLOS Genetics
VL  - 19
IS  - 9
UR  - https://doi.org/10.1371/journal.pgen.1010942
SP  - e1010942
EP  - 
PB  - Public Library of Science
M3  - doi:10.1371/journal.pgen.1010942
ER  -