@Article{cmes.2020.09353,
AUTHOR = {Yan Li, Dayou Liu, Yungang Zhu, Jie Liu},
TITLE = {A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {124},
YEAR = {2020},
NUMBER = {1},
PAGES = {303--313},
URL = {http://www.techscience.com/CMES/v124n1/39393},
ISSN = {1526-1506},
ABSTRACT = {Under different conditions, gene regulatory networks (GRNs) of the
same gene set could be similar but different. The differential analysis of GRNs
under different conditions is important for understanding condition-specific gene
regulatory relationships. In a naive approach, existing GRN inference algorithms
can be used to separately estimate two GRNs under different conditions and identify the differences between them. However, in this way, the similarities between
the pairwise GRNs are not taken into account. Several joint differential analysis
algorithms have been proposed recently, which were proved to outperform the
naive approach apparently. In this paper, we model the GRNs under different conditions with structural equation models (SEMs) to integrate gene expression data
and genetic perturbations, and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential structure. Then, a Bayesian inference
method is used to make joint differential analysis by solving the integrated model.
We evaluated the performance of the proposed re-parametrization-based Bayesian
differential analysis (ReBDA) algorithm by running simulations on synthetic data
with different settings. The performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for
SEMs ReDNet obviously. In the end, the ReBDA algorithm was applied to make
differential analysis on a real human lung gene data set to illustrate its applicability
and practicability.},
DOI = {10.32604/cmes.2020.09353}
}