SignifiKANTE: efficient P-value computation for gene regulatory networks

Gene regulatory networks (GRNs) are graph-based representations of regulatory relationships between transcription factors and target genes. Various tools exist to infer GRNs from gene expression data, but since GRN inference itself is already computationally intensive, statistical significance estimates are often omitted. While naïve permutation-based empirical P-value computation methods are relatively straightforward to implement, they are prohibitively expensive when applied to popular regression-based GRN inference methods and realistically sized datasets.

To address this bottleneck, we developed SignifiKANTE, a tool to efficiently quantify edge significance in GRNs obtained via any regression-based GRN inference method. SignifiKANTE is based on the key insight that the background count distributions of groups of target genes may be highly similar, even if their expression vectors show distinct behavior. Relying on this insight, SignifiKANTE uses gene clustering based on the 1-Wasserstein distance to create a small, constant number of background distributions which enables the simultaneous computation of approximate permutation-based P-values for multiple target genes. This reduces the runtime by orders of magnitudes (for some datasets, from several weeks to few hours), without compromising faithfulness of the obtained P-values.

SignifiKANTE's Python source code is available at https://github.com/bionetslab/SignifiKANTE, a packaged version at https://pypi.org/project/signifikante, and scripts to reproduce the results at https://github.com/bionetslab/SignifiKANTE_Results.