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- Title
Fast and accurate inference of gene regulatory networks through robust precision matrix estimation.
- Authors
Passemiers, Antoine; Moreau, Yves; Raimondi, Daniele
- Abstract
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 article, 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. In addition, we propose a novel scoring metric that builds on graph-theoretical concepts. Availability and implementation 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.
- Subjects
GENE regulatory networks; GENETIC transcription regulation; GENE expression; ACQUISITION of data
- Publication
Bioinformatics, 2022, Vol 38, Issue 10, p2802
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btac178