Volume 12 Number 3 (Jul. 2022)
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IJBBB 2022 Vol.12(3): 43-52 ISSN: 2010-3638
DOI: 10.17706/IJBBB.2022.12.3.43-52

A Novel Method for Gene Regulatory Network Inference with Pseudotime Data Using Information Criterion

Shuhei Yao, Kaito Uemura, Shigeto Seno, Hideo Matsuda

Abstract—Trajectory inference has been used to model cellular dynamic processes by using single-cell RNA sequence data. The inference often computes pseudotime representing the progression through the process along the trajectory. Several methods to infer gene regulatory networks have been proposed using the gene expression profiles of the cells ordered with the pseudotime to elucidate the regulatory relationships between genes in a dynamic process. In this paper, we propose a novel method for the inference of such gene regulatory networks. To predict highly accurate gene regulatory relationships in the network, we introduce an edge-scoring scheme with bootstrap sampling. We demonstrate the accuracy of the proposed methods by comparing the results with those of existing methods using synthetic and real single-cell RNA-seq data.


Index Terms—Gene regulatory network, information criterion, pseudotime analysis, single cell RNA transcriptome.

Shuhei Yao, Kaito Uemura, Shigeto Seno, and Hideo Matsuda are with Graduate School of Information Science and Technology, Osaka University, Suita, Osaka, Japan.


Cite: Shuhei Yao, Kaito Uemura, Shigeto Seno, Hideo Matsuda, " A Novel Method for Gene Regulatory Network Inference with Pseudotime Data Using Information Criterion," International Journal of Bioscience, Biochemistry and Bioinformatics, vol. 12, no. 3, pp. 43-52, 2022.


Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0). 


General Information

ISSN: 2010-3638 (Online)
Abbreviated Title: Int. J. Biosci. Biochem. Bioinform.
Frequency: Quarterly 
DOI: 10.17706/IJBBB
Editor-in-Chief: Prof. Ebtisam Heikal 
Abstracting/ Indexing:  Electronic Journals Library, Chemical Abstracts Services (CAS), Engineering & Technology Digital Library, Google Scholar, and ProQuest.
E-mail: ijbbb@iap.org
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