Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

Area of Science:

• Molecular Biology
• Systems Biology
• Bioinformatics

Background:

• Transcription factor (TF) activity is crucial for cellular regulation.
• Inferring TF activity from gene expression is challenging due to limited validation datasets.

Purpose of the Study:

• To systematically evaluate and optimize methods for inferring TF activity.
• To develop a reliable approach for TF activity inference using gene expression data.

Main Methods:

• Factoring gene expression matrices into control strengths and TF activity levels.
• Utilizing gene expression data with perturbed TF activities for optimization.
• Cross-condition validation of inferred control strengths.

Main Results:

• Perturbation data is necessary and sufficient for accurate TF activity inference.
• Inferred control strengths are transferable across different growth conditions.
• Application of the method to uncover upstream regulators of yeast TFs (Gcr2, Gln3, Gcn4, Msn2).

Conclusions:

• The developed method provides a systematic and objective approach to TF activity inference.
• The inferred control strength matrices are valuable resources for the research community.
• This work advances our understanding of TF regulatory networks.