Fast and accurate inference of gene regulatory networks through robust precision matrix estimation

MOTIVATION: Transcriptional regulation mechanisms allow cells to adapt and respond to external stimuli by altering gene expression. The possible cell transcriptional states are determined by the underlying Gene Regulatory Network (GRN), and reliably inferring such network would be invaluable to understand biological processes and disease progression. RESULTS: In this paper we present a novel method for the inference of GRNs, called PORTIA, which is based on robust precision matrix estimation, and we show that it positively compares with state-of-the-art methods while being orders of magnitude faster. We extensively validated PORTIA using the DREAM and MERLIN + P datasets as benchmarks. Additionally, we propose a novel scoring metric that builds on graph-theoretical concepts. AVAILABILITY: The code and instructions for data acquisition and full reproduction of our results are available at https://github.com/AntoinePassemiers/PORTIA-Manuscript. PORTIA is available on PyPI as a Python package (portia-grn). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Publication year:2022

Accessibility:Open