Gene Regulatory Network Inference Using Convolutional Neural Networks from scRNA-seq Data.
Guo MaoZhengbin PangKe ZuoJie LiuPublished in: Journal of computational biology : a journal of computational molecular cell biology (2023)
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.
Keyphrases
- single cell
- rna seq
- gene expression
- convolutional neural network
- genome wide
- electronic health record
- high throughput
- dna methylation
- poor prognosis
- big data
- deep learning
- machine learning
- copy number
- artificial intelligence
- oxidative stress
- climate change
- transcription factor
- working memory
- binding protein
- social media