Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data

In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to...

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Bibliographic Details
Published in:	Journal of computational biology Vol. 30; no. 5; p. 619
Main Authors:	Mao, Guo, Pang, Zhengbin, Zuo, Ke, Liu, Jie
Format:	Journal Article
Language:	English
Published:	United States 01.05.2023
Subjects:	Gene Expression Profiling - methods Gene Regulatory Networks Neural Networks, Computer Sequence Analysis, RNA - methods Single-Cell Analysis - methods Single-Cell Gene Expression Analysis the atrous spatial pyramid pooling (ASPP) module and the squeeze-and-excitation (SE) module scRNA-seq data 2D co-expression matrix convolutional neural network
ISSN:	1557-8666, 1557-8666
Online Access:	Get more information
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Summary:	In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to construct gene expression regulatory networks. On the other hand, the noise and dropout of single-cell data bring great difficulties to the analysis of scRNA-seq data, resulting in lower accuracy of gene regulatory networks reconstructed by traditional methods. In this article, we propose a novel supervised convolutional neural network (CNNSE), which can extract gene expression information from 2D co-expression matrices of gene doublets and identify interactions between genes. Our method can avoid the loss of extreme point interference by constructing a 2D co-expression matrix of gene pairs and significantly improve the regulation precision between gene pairs. And the CNNSE model is able to obtain detailed and high-level semantic information from the 2D co-expression matrix. Our method achieves satisfactory results on simulated data [accuracy (ACC): 0.712, F1: 0.724]. On two real scRNA-seq datasets, our method exhibits higher stability and accuracy in inference tasks compared with other existing gene regulatory network inference algorithms.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1557-8666 1557-8666
DOI:	10.1089/cmb.2022.0355