Inferring nonlinear gene regulatory networks from gene expression data based on distance correlation

Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measure...

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Bibliographic Details
Published in:PloS one 2014-02, Vol.9 (2), p.e87446-e87446
Main Authors: Guo, Xiaobo, Zhang, Ye, Hu, Wenhao, Tan, Haizhu, Wang, Xueqin
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Bioinformatics
Biology
Computer Simulation
Correlation
Correlation analysis
Data processing
Databases, Genetic
Deoxyribonucleic acid
DNA
DNA repair
E coli
Entropy
Escherichia coli
Escherichia coli - genetics
Gene expression
Gene Expression Regulation, Bacterial
Gene Regulatory Networks
Genes
Genomics
Inference
Laboratories
Mathematics
Nonlinear Dynamics
Random variables
Regulatory mechanisms (biology)
Reproducibility of Results
ROC Curve
Saccharomyces cerevisiae - genetics
Science
SOS Response, Genetics - genetics
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Summary:Nonlinear dependence is general in regulation mechanism of gene regulatory networks (GRNs). It is vital to properly measure or test nonlinear dependence from real data for reconstructing GRNs and understanding the complex regulatory mechanisms within the cellular system. A recently developed measurement called the distance correlation (DC) has been shown powerful and computationally effective in nonlinear dependence for many situations. In this work, we incorporate the DC into inferring GRNs from the gene expression data without any underling distribution assumptions. We propose three DC-based GRNs inference algorithms: CLR-DC, MRNET-DC and REL-DC, and then compare them with the mutual information (MI)-based algorithms by analyzing two simulated data: benchmark GRNs from the DREAM challenge and GRNs generated by SynTReN network generator, and an experimentally determined SOS DNA repair network in Escherichia coli. According to both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve, our proposed algorithms significantly outperform the MI-based algorithms in GRNs inference.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0087446