SignifiKANTE: Efficient P -value computation for gene regulatory networks

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ID: 321864
2026
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Abstract
Abstract Motivation 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. Results 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 employs 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. Availability and implementation 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.
Reference Key
openalex_W7170062875 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Fabian Woller, Paul Martini, S. N. Sen, David B. Blumenthal, Anne Hartebrodt
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag531
URL
https://doi.org/10.1093/bioinformatics/btag531
Keywords Keywords not found

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