We are excited to share that our paper “SignifiKANTE – efficient P-value computation for gene regulatory networks” has now been published in Bioinformatics. Permutation-based significance testing is a common approach for filtering edges in regression-based gene regulatory network inference, but so expensive that it is usually skipped on real-world gene expression data. We tackle this with 1-Wasserstein distance-based gene clustering to build shared background distributions of genes, cutting runtimes from weeks to hours, while ensuring high-quality approximation P-values. The filtered networks show better precision against simulated ground truth and better agreement with tissue ontology. SignifiKANTE is an open source tool and pip-installable from https://github.com/bionetslab/SignifiKANTE
SignifiKANTE published in Bioinformatics
