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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Vydáno v:	PLoS genetics Ročník 19; číslo 9; s. e1010942
Hlavní autoři:	Wang, Jiacheng, Chen, Yaojia, Zou, Quan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 13.09.2023 Public Library of Science (PLoS)
Témata:	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
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Popis
Shrnutí:	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.
Bibliografie:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7404 1553-7390 1553-7404
DOI:	10.1371/journal.pgen.1010942