A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic...

Area of Science:

• Computational Biology
• Systems Biology
• Genomics

Background:

• High-throughput sequencing enables gene expression measurement but not direct TF activity assessment.
• Computational methods are needed to infer regulator activity from gene expression data.
• Transcription factor (TF) activity is crucial for understanding gene regulation.

Purpose of the Study:

• To develop a computational approach for inferring TF activity from gene expression data.
• To provide a flexible framework for incorporating TF-gene regulation logic models.
• To investigate transcriptional regulation in fibroblast phenotypic plasticity.

Main Methods:

• A noisy Boolean logic Bayesian model was developed for TF activity inference.
• The model utilizes differential gene expression data and causal graphs.
• Simulations and controlled over-expression experiments were used for validation.

Main Results:

• The developed method accurately identifies TF activity.
• The approach was successfully applied to bulk and single-cell transcriptomics data.
• The study investigated transcriptional regulation underlying fibroblast phenotypic plasticity.

Conclusions:

• The noisy Boolean logic Bayesian model offers a reliable method for TF activity inference.
• The tool facilitates the study of gene regulatory networks.
• User-friendly software and a web interface are provided for accessibility.