Title:Inferring Gene Regulatory Networks from Single-Cell Time-Course Data Based on Temporal Convolutional Networks
Volume: 19
Issue: 8
Author(s): Dayu Tan, Jing Wang, Zhaolong Cheng, Yansen Su and Chunhou Zheng*
Affiliation:
- Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei, China
Keywords:
Time-course single-cell RNA sequencing, causal relationship, gene regulatory network, temporal convolutional network, supervised learning, gene function assignment.
Abstract:
Background: Time-course single-cell RNA sequencing (scRNA-seq) data represent dynamic
gene expression values that change over time, which can be used to infer causal relationships
between genes and construct dynamic gene regulatory networks (GRNs). However, most of the existing
methods are designed for bulk RNA sequencing (bulk RNA-seq) data and static scRNA-seq data,
and only a few methods, such as CNNC and DeepDRIM can be directly applied to time-course
scRNA-seq data.
Objective: This work aims to infer causal relationships between genes and construct dynamic gene
regulatory networks using time-course scRNA-seq data.
Methods: We propose an analytical method for inferring GRNs from single-cell time-course data
based on temporal convolutional networks (scTGRN), which provides a supervised learning approach
to infer causal relationships among genes. scTGRN constructs a 4D tensor representing gene expression
features for each gene pair, then inputs the constructed 4D tensor into the temporal convolutional
network to train and infer the causal relationship between genes.
Results: We validate the performance of scTGRN on five real datasets and four simulated datasets,
and the experimental results show that scTGRN outperforms existing models in constructing GRNs.
In addition, we test the performance of scTGRN on gene function assignment, and scTGRN outperforms
other models.
Conclusion: The analysis shows that scTGRN can not only accurately identify the causal relationship
between genes, but also can be used to achieve gene function assignment.
