A Variational Inference Approach to Single-Cell Gene Regulatory Network Inference using Probabilistic Matrix Factorization

Inferring gene regulatory networks (GRNs) from single cell data is challenging due to heuristic limitations and a lack of uncertainty estimates in existing methods. To address this, we present Probabilistic Matrix Factorization for Gene Regulatory Network Inference (PMF-GRN). Using single cell expression data, PMF-GRN infers latent factors capturing transcription factor activity and regulatory relationships, incorporating experimental evidence via prior distributions. By utilizing variational inference, we facilitate hyperparameter search for principled model selection and direct comparison to other generative models. We extensively test and benchmark our method using single cell datasets from Saccharomyces cerevisiae, human Peripheral Blood Mononuclear Cells (PBMCs), and BEELINE synthetic data. We discover that PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods, offering well-calibrated uncertainty estimates for additional interpretability.