Inferring Nonlinear Gene Regulatory Networks from Gene Expression Data Based on Distance Correlation

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2014 Feb 20

PMID 24551058

Citations 22

Authors

Xiaobo Guo

Ye Zhang

Wenhao Hu

Haizhu Tan

Xueqin Wang

Affiliations

Soon will be listed here.

Abstract

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.

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