Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of ge...

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Bibliographic Details
Published in:PLoS genetics Vol. 19; no. 9; p. e1010942
Main Authors: Wang, Jiacheng, Chen, Yaojia, Zou, Quan
Format: Journal Article
Language:English
Published: United States Public Library of Science 13.09.2023
Public Library of Science (PLoS)
Subjects:
Analysis
Biology and Life Sciences
Breast cancer
Cells
Computer and Information Sciences
Datasets
Deep learning
Gene expression
Gene Regulatory Networks - genetics
Genes
Humans
Identification and classification
Leukocytes, Mononuclear
Machine learning
Medicine and Health Sciences
Methods
Performance evaluation
Peripheral blood mononuclear cells
Physical Sciences
Research and Analysis Methods
RNA
RNA sequencing
Transcriptome - genetics
Transcriptomes
Triple Negative Breast Neoplasms
China
ISSN:1553-7404, 1553-7390, 1553-7404
Online Access:Get full text
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Summary:The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition.
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The authors have declared that no competing interests exist.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1010942