Borg JP, Colinge J, Ravel P. Modular response analysis reformulated as a multilinear regression problem.
Bioinformatics 2023;
39:btad166. [PMID:
37021935 PMCID:
PMC10097436 DOI:
10.1093/bioinformatics/btad166]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/29/2023] [Accepted: 03/07/2023] [Indexed: 04/07/2023] Open
Abstract
MOTIVATION
Modular response analysis (MRA) is a well-established method to infer biological networks from perturbation data. Classically, MRA requires the solution of a linear system, and results are sensitive to noise in the data and perturbation intensities. Due to noise propagation, applications to networks of 10 nodes or more are difficult.
RESULTS
We propose a new formulation of MRA as a multilinear regression problem. This enables to integrate all the replicates and potential additional perturbations in a larger, over-determined, and more stable system of equations. More relevant confidence intervals on network parameters can be obtained, and we show competitive performance for networks of size up to 1000. Prior knowledge integration in the form of known null edges further improves these results.
AVAILABILITY AND IMPLEMENTATION
The R code used to obtain the presented results is available from GitHub: https://github.com/J-P-Borg/BioInformatics.
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