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

Authors	Argenis Arriojas, Susan Palatano, Jill Macoska, Kourosh Zarringhalam.
Journal	NAR Genomics and Bioinformatics, 5(4), lqad106 (2023)
DOI	10.1093/nargab/lqad106

Abstract

Transcription factors (TFs) play a crucial role in regulating gene expression, but inferring their activities from transcriptomic data remains challenging. Here, we present NLBayes, a Bayesian noisy logic framework for the inference of TF activities from both bulk and single-cell RNA sequencing data. Our approach models the complex regulatory interactions between TFs and their target genes using a probabilistic Boolean logic framework that accounts for the inherent noise and uncertainty in gene expression data. NLBayes incorporates prior knowledge of TF-target relationships and uses Markov Chain Monte Carlo (MCMC) sampling to infer the posterior distributions of TF activities. Through extensive validation on simulated data and applications to real datasets, we demonstrate that NLBayes outperforms existing methods in accuracy, robustness to noise, and interpretability. We apply NLBayes to analyze TF activities in prostate cancer progression and treatment response, revealing key regulators and their dynamics. Furthermore, our model's capability to handle single-cell data allows for the identification of cell type-specific TF activities and regulatory patterns. NLBayes provides a powerful and versatile tool for the systems-level analysis of transcriptional regulation in complex biological processes and diseases.

Key Points

Novel Bayesian framework for inferring transcription factor activities
Handles both bulk RNA-seq and single-cell RNA-seq data
Integrates prior knowledge of regulatory relationships
Provides uncertainty quantification through posterior distributions
Application to cancer progression reveals key regulatory dynamics
Open-source implementation with user-friendly interfaces

Supporting Materials

Citation

@article{arriojas2023bayesian,
  title={A Bayesian noisy logic model for inference of transcription factor activity from single cell and bulk transcriptomic data},
  author={Arriojas, Argenis and Patalano, Stefan and Macoska, Jill and Zarringhalam, Kourosh},
  journal={NAR Genomics and Bioinformatics},
  volume={5},
  number={4},
  pages={lqad106},
  year={2023},
  publisher={Oxford University Press},
  doi={10.1093/nargab/lqad106}
}