comparing statistical methods for constructing large scale gene networks构建大规模的基因网络的统计方法进行比较.pdf

Comparing Statistical Methods for Constructing Large Scale Gene Networks 1 1,2 1,2 3,4 1 Jeffrey D. Allen , Yang Xie , Min Chen , Luc Girard , Guanghua Xiao * 1 Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America, 2 Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America, 3 Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America, 4 Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America Abstract The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in construc