SignifiKANTE: Efficient P-value computation for gene regulatory networks
Fabian Woller1, Paul Martini1, Souptik Sen1,2
1Biomedical Network Science Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nürnberger Straße 74, 91052 Erlangen, Germany.
Bioinformatics (Oxford, England)
|July 22, 2026
Summary
SignifiKANTE efficiently estimates gene regulatory network (GRN) edge significance using gene clustering and the 1-Wasserstein distance. This tool drastically reduces computation time for GRN inference, making P-value estimation feasible.
Area of Science:
- Computational Biology
- Bioinformatics
- Systems Biology
Background:
- Gene regulatory networks (GRNs) model gene expression relationships.
- Inferring GRNs is computationally intensive, often omitting statistical significance.
- Existing P-value methods are too slow for large datasets and regression-based inference.
Purpose of the Study:
- Develop an efficient tool, SignifiKANTE, for quantifying edge significance in GRNs.
- Address the computational bottleneck in statistical significance estimation for GRNs.
- Enable accurate P-value computation for regression-based GRN inference methods.
Main Methods:
- SignifiKANTE utilizes gene clustering based on the 1-Wasserstein distance.
- It identifies similar background distributions across target gene groups.
- This enables simultaneous, approximate permutation-based P-value computation for multiple genes.
Main Results:
- SignifiKANTE significantly reduces runtime for P-value estimation (weeks to hours).
- The tool maintains the faithfulness of P-value computations.
- It is compatible with any regression-based GRN inference method.
Conclusions:
- SignifiKANTE provides an efficient solution for statistical significance estimation in GRNs.
- The method accelerates P-value computation without sacrificing accuracy.
- This tool enhances the reliability of inferred GRNs.
