Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference - Seçilmiş, Deniz; Nelander, Sven; Sonnhammer, Erik L. L. (Frontiers in Genetics, 2022)
Accurate inference of gene regulatory networks (GRNs) is important to unravel unknown regulatory mechanisms and processes, which can e.g. lead to the identification of treatment targets for genetic diseases. A variety of GRN inference methods have been proposed, that under suitable data conditions perform well in benchmarks that consider the entire spectrum of false positives and negatives. However, it is very challenging to predict which single network sparsity gives the most accurate GRN. Lacking criteria for sparsity selection, a simplistic solution is to pick the GRN that has a certain number of links per gene, that is guessed to be reasonable. However, this does not guarantee to find the GRN that has the correct sparsity or is the most accurate one. In this study, to provide a general approach for identifying the most accurate and sparsity-wise relevant GRN within the entire space of possible GRNs, we implemented a pipeline called SPA that is inspired by two commonly used model selection criteria, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), but adapted to GRN inference. The results show that the approach can in most cases find the GRN whose sparsity is close to the true sparsity, and gives an accuracy that is equal or close to the highest possible accuracy that the GRN inference method can achieve on the given data. The datasets and source code can be found at https://bitbucket.org/sonnhammergrni/spa/