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Updated: Jan 7, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Gene Regulatory Network Inference from Pseudotime-Ordered scRNA-seq Data via Time-Lagged Divergence Measures.

Lingling Zhang¹, Tong Si², Lucas Koch³

¹Department of Mathematics, State University of New York at Brockport, Brockport, NY, USA.

Bioinformatics Research and Applications : ... International Symposium, ISBRA ... Proceedings. ISBRA (Conference)

|January 5, 2026

Summary

We developed PseudoGRN, a new method to build gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data. This tool helps understand dynamic gene regulation in complex biological systems.

Keywords:

Applied Computing → Bioinformatics Gene Regulatory Network Integral Probability Metric Partial Correlation Pseudotime Analysis f-Divergence scRNA-seq Data

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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

Area of Science:

Computational Biology
Genomics
Systems Biology

Background:

Inferring gene regulatory networks (GRNs) from time-series single-cell RNA sequencing (scRNA-seq) data presents significant challenges.
These challenges include sparse temporal resolution, high dimensionality, and inherent cellular heterogeneity.

Purpose of the Study:

To develop a novel integrative framework, PseudoGRN, for reconstructing directed GRNs from time-series scRNA-seq data.
To address limitations of existing methods in capturing dynamic regulatory mechanisms.

Main Methods:

PseudoGRN unifies multiple pseudotime inference methods.
It incorporates various time-lagged divergence measures and non-redundant penalized network inference.
Partial correlation analysis is employed for robust GRN reconstruction.

Main Results:

PseudoGRN demonstrated superior performance compared to existing approaches when applied to a real-world scRNA-seq dataset.
The method successfully reconstructed directed GRNs from complex time-series scRNA-seq data.
It provided a robust and interpretable tool for analyzing dynamic regulatory mechanisms.

Conclusions:

PseudoGRN offers an advanced solution for inferring cell type-specific GRNs from time-series scRNA-seq data.
The framework enhances our ability to uncover dynamic regulatory mechanisms in single-cell systems.
It represents a significant advancement in computational biology for analyzing scRNA-seq data.