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Related Experiment Video

Updated: Nov 4, 2025

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations

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Published on: April 21, 2023

Inferring gene regulatory network from single-cell transcriptomic data by integrating multiple prior networks.

Yanglan Gan¹, Yongchang Xin¹, Xin Hu¹

¹School of Computer Science and Technology, Donghua University, Shanghai, China.

Computational Biology and Chemistry

|May 27, 2021

Summary

We developed iMPRN, a computational method that integrates prior networks to reconstruct gene regulatory networks. iMPRN accurately infers gene regulation, revealing differences between tumor and non-tumor cells.

Keywords:

Gene expression Gene regulatory network Prior network scRNA-seq transcriptome

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Last Updated: Nov 4, 2025

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Gene regulatory networks (GRNs) model crucial interactions between transcription factors and target genes.
Reconstructing GRNs is vital for understanding gene function and biological systems.

Purpose of the Study:

To develop an advanced computational method, iMPRN, for accurate gene regulatory network inference.
To integrate multiple prior networks for enhanced regulatory network reconstruction.

Main Methods:

iMPRN utilizes a network component analysis model.
Employs linear regression, graph embedding, and elastic networks for prior network optimization.
Integrates optimized networks based on B scores for regulatory edge confidence.

Main Results:

iMPRN demonstrated superior accuracy in gene regulatory network reconstruction compared to four existing algorithms on simulated data.
Application to scRNA-seq data revealed distinct GRNs in malignant versus nonmalignant head and neck tumor cells.

Conclusions:

iMPRN offers a robust approach for inferring gene regulatory networks.
The method effectively identifies context-specific regulatory differences, such as in tumor biology.