Deniz Seçilmiş1, Thomas Hillerton1, Sven Nelander2, Erik L L Sonnhammer1. 1. Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, Solna, 17121, Sweden. 2. Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
Abstract
MOTIVATION: Accurate inference of gene regulatory interactions is of importance for understanding the mechanisms of underlying biological processes. For gene expression data gathered from targeted perturbations, gene regulatory network (GRN) inference methods that use the perturbation design are the top performing methods. However, the connection between the perturbation design and gene expression can be obfuscated due to problems such as experimental noise or off-target effects, limiting the methods' ability to reconstruct the true GRN. RESULTS: In this study we propose an algorithm, IDEMAX, to infer the effective perturbation design from gene expression data in order to eliminate the potential risk of fitting a disconnected perturbation design to gene expression. We applied IDEMAX to synthetic data from two different data generation tools, GeneNetWeaver and GeneSPIDER, and assessed its effect on the experiment design matrix as well as the accuracy of the GRN inference, followed by application to a real dataset. The results show that our approach consistently improves the accuracy of GRN inference compared to using the intended perturbation design when much of the signal is hidden by noise, which is often the case for real data. AVAILABILITY: https://bitbucket.org/sonnhammergrni/idemax. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Accurate inference of gene regulatory interactions is of importance for understanding the mechanisms of underlying biological processes. For gene expression data gathered from targeted perturbations, gene regulatory network (GRN) inference methods that use the perturbation design are the top performing methods. However, the connection between the perturbation design and gene expression can be obfuscated due to problems such as experimental noise or off-target effects, limiting the methods' ability to reconstruct the true GRN. RESULTS: In this study we propose an algorithm, IDEMAX, to infer the effective perturbation design from gene expression data in order to eliminate the potential risk of fitting a disconnected perturbation design to gene expression. We applied IDEMAX to synthetic data from two different data generation tools, GeneNetWeaver and GeneSPIDER, and assessed its effect on the experiment design matrix as well as the accuracy of the GRN inference, followed by application to a real dataset. The results show that our approach consistently improves the accuracy of GRN inference compared to using the intended perturbation design when much of the signal is hidden by noise, which is often the case for real data. AVAILABILITY: https://bitbucket.org/sonnhammergrni/idemax. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Deniz Seçilmiş; Thomas Hillerton; Andreas Tjärnberg; Sven Nelander; Torbjörn E M Nordling; Erik L L Sonnhammer Journal: Sci Rep Date: 2022-10-03 Impact factor: 4.996