BiGSM: Bayesian inference of gene regulatory network via sparse modelling

• Published in: Bioinformatics (Oxford, England) Vol. 41; no. 6
• Main Authors: Qin, Hang, Garbulowski, Mateusz, Sonnhammer, Erik L L, Chatterjee, Saikat
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.06.2025; Oxford University Press
• Subjects: Algorithms; Availability; Bayes Theorem; Bayesian analysis; Benchmarks; Computational Biology - methods; Confidence; Gene Regulatory Networks; Mathematical models; Methods; Modelling; Network analysis; Noise levels; Original Paper; Sparsity; Statistical analysis; Statistical inference
• ISSN: 1367-4811; 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btaf318

Inference of gene regulatory network (GRN) is challenging due to the inherent sparsity of the GRN matrix and noisy expression data, often leading to a high possibility of false positive or negative predictions. To address this, it is essential to leverage the sparsity of the GRN matrix and develop a robust method capable of handling varying levels of noise in the data. Moreover, most existing GRN inference methods produce only fixed point estimates, which lack the flexibility and informativeness for comprehensive network analysis. In contrast, a Bayesian approach that yields closed-form posterior distributions allows probabilistic link selection, offering insights into the statistical confidence of each possible link. Consequently, it is important to engineer a Bayesian GRN inference method and rigorously execute a benchmark evaluation compared to state-of-the-art methods. We propose a method-Bayesian inference of GRN via Sparse Modelling (BiGSM). BiGSM effectively exploits the sparsity of the GRN matrix and infers the posterior distributions of GRN links from noisy expression data by using the maximum likelihood based learning. We thoroughly benchmarked BiGSM using biological and simulated datasets including GeneNetWeaver, GeneSPIDER, and GRNbenchmark. The benchmark test evaluates its accuracy and robustness across varying noise levels and data models. Using point-estimate based performance measures, BiGSM provides an overall best performance in comparison with several state-of-the-art methods including GENIE3, LASSO, LSCON, and Zscore. Additionally, BiGSM is the only method in the set of competing methods that provides posteriors for the GRN weights, helping to decipher confidence across predictions. Code implemented via MATLAB and Python are available at Github: https://github.com/SachLab/BiGSM and archived at zenodo.