PMF-GRN: a variational inference approach to single-cell gene regulatory network inference using probabilistic matrix...

Area of Science:

• Computational Biology
• Genomics
• Systems Biology

Background:

• Inferring gene regulatory networks (GRNs) from single-cell data is crucial for understanding cellular processes.
• Current methods face limitations in accuracy and lack robust uncertainty quantification.

Purpose of the Study:

• To introduce Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN).
• To address the challenges of accuracy and uncertainty estimation in single-cell GRN inference.

Main Methods:

• Utilized single-cell expression data to infer latent factors representing transcription factor activity and regulatory relationships.
• Employed variational inference for hyperparameter optimization and model comparison.
• Benchmarked PMF-GRN against state-of-the-art methods using both synthetic and real single-cell datasets.

Main Results:

• PMF-GRN demonstrates superior accuracy in inferring gene regulatory networks compared to existing methods.
• The method provides well-calibrated estimates of uncertainty for the inferred regulatory relationships.
• Successful application on real single-cell datasets validates its practical utility.

Conclusions:

• PMF-GRN offers a significant advancement in the field of single-cell GRN inference.
• The incorporation of uncertainty estimation enhances the reliability and interpretability of inferred networks.
• This probabilistic approach facilitates principled model selection and comparison in GRN analysis.