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Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis.

Dabin Jeong¹, Sangsoo Lim², Sangseon Lee³

¹Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.

Frontiers in Genetics

|June 7, 2021

Summary

This study introduces a new computational method using kernel canonical correlation analysis (kernel CCA) to build condition-specific gene regulatory networks. The approach effectively identifies complex multiple-to-multiple interactions between transcription factors and target genes under stress conditions.

Keywords:

TF cooperation condition specific network gene regulatory network kernel canonical correlation analysis network dynamics transcription factor

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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Gene expression profiles reveal cellular states and responses to stress.
Transcription factor (TF) and target gene (TG) interactions are key regulatory mechanisms.
Existing methods for network construction have limitations, such as one-to-multiple relations or false positives.

Purpose of the Study:

To develop a novel computational method for constructing condition-specific transcriptional networks.
To identify complex multiple-to-multiple TF-TG relationships.
To analyze TF-TG interactions in response to specific biological conditions.

Main Methods:

Application of kernel canonical correlation analysis (kernel CCA) to transcriptome data.
Embedding TFs and TGs into a new space to reflect their correlations.
Construction of condition-specific and combinatorial transcriptional networks.

Main Results:

The method successfully constructed condition-specific transcriptional networks.
It identified known and novel TF-TG regulatory interactions.
Detected modules of TFs regulating modules of TGs under stress.

Conclusions:

The proposed kernel CCA-based method is effective for building condition-specific transcriptional networks.
It accurately captures complex multiple-to-multiple TF-TG interactions.
This approach enhances understanding of cellular responses to stress through gene regulation.